Robotic non-contact three-dimensional (3-D) micromanipulation of micro-objects is critical for micro-assembly applications, but achieving high precision and reliable release in the microscale remains challenging. Traditional contact and non-contact techniques face limitations in automated 3-D operation. We present a fully automated micromanipulation system using an acoustic bubble end-effector for non-contact 3-D handling of microbeads. The system integrates real-time vision feedback with Z-axis autofocus and adaptive proportional-integral-derivative control for precise trapping, transport, and release. Leveraging localized bubble-driven microstreaming and acoustic radiation forces, our end-effector forms a stable trap within 100 ms and transports microbeads at speeds up to 1 mm/s. Experiments demonstrate exceptional 3-D dexterity, with trapping success rates consistently greater than 80% for microbeads of 60-120 μm diameter, and release placement accuracy is confirmed to be within ±2.5 μm when operating below a 150 μm height. The system achieves broad size adaptability and operates in free-space 3-D environments, overcoming workspace constraints of microfluidic setups. By eliminating mechanical contact, this approach reduces contamination and damage while delivering precise 3-D control often lacking in other non-contact methods. Our system bridges acoustofluidics and robotic automation, offering a versatile solution for automated micro-assembly and biomedical applications.

Bipedal robots have achieved remarkable locomotion capabilities through reinforcement learning (RL), yet their real-world deployment remains hindered by the sim-to-real gap—dynamics mismatches between simulation and reality that degrade locomotion performance through behavioral deviations. This work introduces an online behavior adaptation framework that bridges this gap at the behavioral level by dynamically aligning emergent locomotion strategies with simulation-derived objectives. Our method integrates two core innovations: (1) a structured latent space constructed via an augmented Variational Autoencoder (VAE), which quantifies behavioral divergence through domain-invariant representations of locomotion patterns, and (2) a closed-loop adaptation module that maps latent-space deviations to real-time adjustments in low-level controller parameters. By reformulating sim-to-real transfer as a problem of behavioral alignment rather than explicit dynamics matching, the framework enables continuous adaptation to unmodeled dynamics mismatch without requiring system identification or offline retraining. Extensive experimental evaluations demonstrate the effectiveness of the proposed method, highlighting its potential to bridge the behavior gap between simulation and reality.

Precise motion control of magnetic microrobots in complex and dynamic environments remains a critical challenge for enabling key applications such as targeted therapy and micromanipulation. Purely manual teleoperation is prone to operator fatigue and error, while fully autonomous systems often lack the robustness and adaptability to handle. Here, we propose a human-machine shared cascade control method for magnetically driven microrobots, which effectively integrates human cognitive intelligence with machine autonomy for collision-free navigation in dynamic environments. The outer-loop hybrid shared control unit smoothly modulates control authority in response to real-time collision risk, dynamically integrating the operator instructions and the autonomous navigation system output guided by the enhanced artificial potential field method to formulate the guidance law. For the inner-loop motion tracking, a data-driven adaptive orientation controller is designed, which integrates a nonlinear feedforward compensator leveraging a Gaussian process regression (GPR) model with a linear feedback controller whose parameters are optimized using the virtual reference feedback tuning (VRFT) method, ensuring fast and precise tracking of the desired motion. The effectiveness of the proposed method was validated through both simulation and physical experiments. In human-subject studies conducted on a physical magnetic actuation platform featuring both static and dynamic obstacle scenarios, quantitative results demonstrate that the shared control strategy significantly outperforms both purely manual and fully autonomous modes across all key metrics, including success rate, task completion time, stability, and safety ( $p \lt 0.001$ ). Furthermore, successful navigation within a complex gastric model demonstrates the potential of the shared control system for practical application in unstructured environments.

Magnetic soft millirobotic-grippers, equipped with agile pivot-walking motions and adaptive enveloping morph abilities, hold great promise for biomedical target acquisition tasks. However, deploying these millirobotic-grippers in highly constrained, disturbed, and variable terrains to realize multisequence target acquisition and transport tasks remains challenging. Here, we introduce a hierarchical multimodal motion control method for pivot-walking magnetic milliroboticgrippers, which enhances adaptive locomotion capabilities and enables high-precision motion control, facilitating autonomous target acquisition in complex terrains. The millirobotic-gripper utilizes a centrosymmetric three-pivot design, enabling adaptive soft enveloping deformation and robust multimodal locomotion. A hierarchical control architecture is proposed, comprising: 1) an upper-level Event-Based Finite State Machine planner that dynamically orchestrates transitions between motion modes according to environmental feedback and task-specific conditions; and 2) a lower-level Sliding Mode controller integrated with Gaussian Process-based gait parameter optimization, significantly improving motion accuracy and robustness against environmental disturbances. Experimental results demonstrate that our proposed method allows millirobotic-grippers to efficiently navigate morphing tunnels, leap across gaps exceeding 3 times their body length, accurately follow arbitrary paths with errors less than 5% of their body length, and reliably perform three types of targets grasping and transport. Furthermore, the biomedical application potential of our system is initially validated through ex vivo porcine gastrointestinal tract experiment with ultrasound guidance.

Microrobots have garnered significant attention due to their vast potential applications across various fields. Among various types of microrobots, piezoelectric robots stand out due to their exceptional motion accuracy, low power consumption, and simple structural design. This work introduces a novel piezoelectric microrobot, the Dual-Modal Piezoelectric Robot (DMPBot), which is fabricated with an innovative carbon fiber substrate through a heat-pressing process with a compact size of 6 mm × 9 mm × 1.1 mm and a weight of only 0.05 g. DMPBot can achieve both high-speed and high-precision motion in non-resonant mode, as well as omnidirectional movement by integrating non-resonant and resonant modes. In non-resonant mode, the robot can reach a speed of 33 mm/s (3.67 body lengths per second) and a sub-micron resolution of 0.4 μm by adjusting the applied signal. This work presents an analysis of the design, fabrication, and performance of DMPBot, focusing on its dynamic response, motion mechanisms, high-speed and high-precision motion, and omnidirectional movement capabilities. Experimental results validate the ability of DMPBot to perform high-speed, high-precision, and omnidirectional motion, demonstrating its promising potential in the field of micromanipulation.

During the past decade, DNA origami has emerged as a promising technology to construct DNA nanorobots with programmable configurations and excellent biocompatibility. However, existing DNA origami-based nanorobots exhibit weak stability in complex biological environments and lack efficient delivery capabilities when serving as drug carriers. This study introduces a reconfigurable tetrahedral DNA nanorobot, whose conformational transition pathways are validated by multi-resolution molecular dynamics simulations. Building on this, we systematically analyzed the structural stability of the tetrahedral nanorobot using multiple simulation methods. We then fabricated the specific molecule-triggered tetrahedral DNA nanorobot with high structural stability and efficient drug delivery capacity. The proposed nanorobot was further employed for the recognition and inhibition of circulating tumor cells. These results highlight the application potential of the proposed DNA origami-engineered nanorobot in biomedicine and nanosensing.

Traditional single-cell mechanical characterization techniques (e.g., atomic force microscopy) often face limitations in throughput, require invasive labeling, or fail to replicate physiological microenvironments, impeding their clinical utility for rapid cancer cell analysis. To address these limitations for automated characterization of cellular mechanical properties, this study proposes a novel method using microchannels with narrow geometric structures to measure cellular mechanical characteristics. A dynamic mechanical characterization technique with serially connected microchannels simulates malignant tumor cell deformation and migration in vivo, enabling precise identification of three malignant tumor cell lines and three normal cell lines through consecutive compressions. High-speed imaging combined with computer vision and image processing techniques facilitates rapid and accurate automated analysis for tumor cells. Furthermore, this study reveals that the mechanical properties of the cell nucleus determine the overall cellular mechanics, with the differences between tumor and normal cells attributed to variations in nucleus mechanics. This approach shows promise for early cancer diagnosis.

Magnetic helical microrobots have been widely applicated in environmental remediation, sensing, targeted medical applications, and so on. However, for locomotion and manipulation in unstructured liquid environments, the capabilities of distinguished motions and on-demand parking/starting over a team of microrobot are essential. Here, we propose a method for achieving on-demand motions conversion of helical microrobots by modulating surface wettability through surface chemical modification and surface microstructural modifications. An obvious difference shows that microrobot after chemical modification exhibit hydrophilicity and microrobot after microstructural modification exhibit hydrophobicity, where the latter possess higher moving step-out frequency and maximum forward velocity compared to the microrobot after surface chemical modification. The step-out frequencies and maximum velocities of the three types of microrobots (chemistry-modified, unmodified, and pimples-modified) are 13 Hz, 16 Hz, 22 Hz, and 385 μm/s, 511 μm/s, 649 μm/s. Furthermore, our method has demonstrated that can be employed to achieve effective on-demand targeted motions and modal conversion in liquid environment. We anticipate that the method can be potentially employed to achieve precise targeted drug delivery and surgery in biomedical applications.

Micro-nano robots must break the symmetry of the flow field to generate net displacement in the low Reynolds number environment. The spherical micro-robots utilize the frictional forces generated through interaction with the surface. We designed a magnetic microroller robot powered by the rotating AC magnetic field. Here, we employed dual measurements of laser ranging and computer vision to demonstrate that a single 100 μm microroller maintains a lubrication film of 1 to 15 μm with the surface during normal motion. We found that the translational velocity of the microroller is correlated with the lubrication film thickness. Based on the robot's gravity, we controlled an additional downward gradient magnetic field to effectively increase the load of robot and reduce the lubrication film thickness, thereby controllably increasing the translational velocity of the robot. For example, the gradient magnetic field generated by superimposing a 30mA direct current input can reduce the lubrication film thickness from 8 μm to 4 μm in a 10 Hz rotating magnetic field, and increase the translational velocity from 230 μm/s to 460 μm/s. The enhancement of the robot's motion performance enables it to better control its movement in fluids. Finally, we validated the strategy for controllable acceleration of micro-scale particles rolling on surfaces, applied to control fluid motion in multiple arteries within blood vessels. These results offer deeper insights into the physical motion mechanism of surface robots and hold significant implications for future applications in biomedical engineering.

Chemical reactions constitute a cornerstone of fundamental scientific inquiry, yet traditional methodologies and platforms are encumbered by excessive reagent and consumable demands. Emerging alternatives, such as microfluidic systems, while innovative, suffer from intricate fabrication processes and elevated costs associated with operator training. Other contemporary approaches face limitations including reagent compatibility constraints and prohibitively expensive instrumentation. To address these challenges, this study introduces a contactless chemical reaction platform leveraging an ultrasonic vortex field to achieve stable capture, microscale droplet transport, and sequential multi-droplet mixing without direct contact. This platform substantially reduces contamination risks, minimizes reagent and consumable usage, accommodates a broad spectrum of reagent types, and imposes minimal demands on operator expertise. Demonstrating robust performance in microdose reaction control, the system offers significant potential for advancing chemical research and its applications.

Micromanipulation techniques struggle to achieve three-dimensional rotational control at the microscale without compromising biocompatibility or spatial flexibility. Conventional methods based on mechanical contact, optical forces, or confined microfluidics constrain dynamic reconfiguration and surgical accessibility. Here, we introduce a dual-bubble acoustic micromanipulator that enables multidirectional rotation through controlled hydrodynamic fields. By placing oscillating microbubbles at the tips of micropipettes, this system creates adjustable vortex patterns: a single microbubble generates toroidal flows for out-of-plane rotation, while two microbubbles produce shear forces for in-plane spinning. This approach uses simple mechanical adjustments to control rotational axes in open fluid environments, without needing frequency modulation or phase synchronization. Flow-field simulations and experiments with polystyrene microspheres confirm deterministic orientation control, and tests with shrimp embryos demonstrate rotation at clinically relevant speeds. The open architecture integrates seamlessly with standard microscopy and robotic injection systems, offering a non-contact, precise tool for applications such as polar body alignment, intracellular surgery, and 3-D imaging.

A magnetically controlled catheter system is proposed to enhance the precision and safety of vascular interventions by reducing procedure time and radiation exposure. The system can also function as a support channel for guidewire deployment. A novel navigation approach is introduced, employing an external permanent magnet capable of controlled rotation to actuate a catheter with an embedded magnetic tip. Leveraging magnetic coupling and a redundant rotational DoF, the system achieves fine angular tip control with minimal spatial displacement, significantly enhancing maneuverability in constrained vascular environments. The magnetic field distribution and its influence on catheter response are characterized, and a kinematic model of the actuation mechanism is established. Experimental validation is conducted under varying magnetic field strengths and orientations, demonstrating reliable steering performance. Application-based experiments in simulated clinical environments further confirm precise navigation capability. The results highlight the advantages of rotational magnetic control in enhancing flexibility and accuracy. The proposed system presents a promising solution for automating catheter-based interventions, offering improved efficiency and power in minimally invasive procedures.

Microassembly plays an important role in fabricating complex structures with small basic components in industrial and biomedical fields. Inverted optical microscope could provide high-quality image feedback for microassembly with its continuously improving resolution. However, a compact stage capable of positioning and reorienting micro-objects while fitting within the limited space under an inverted optical microscope remains unavailable. This paper proposes a compact R-X-Y stage that can transport micro-objects over long distances in the X and Y directions, and reorient the objects by the 360-degree continuous rotation. Additionally, different from commonly putting the rotational stage on the X-Y stage, we mount the thin X-Y stage on a rotational stage. Thus, after aligning the centers of the visual field and rotational stage at the beginning, all the visiable micro-objects will not move out of the visual field during the rotation. We further integrate the R-X-Y stage and the dual-finger micromanipulator, and then use them to assemble 2-D patterns and complex 3-D micromachine. The obtained results and preliminary demonstration indicate that the proposed compact R-X-Y has great potential in assembling complex micromachines.

Miniature robots hold great promise for performing micromanipulation tasks within hard-to-reach confined spaces. However, effectively maneuvering across complex and unstructured terrain, achieving adaptive morphogenesis, and developing adaptive multimodal locomotion strategies remain challenges for these robotic systems. Here, we develop a multi-stimulus-responsive deformable miniature robot integrated with an adaptive multimodal motion control method. Sodium alginate hydrogel and graphene-coated magnetic elastomer are integrated into the sheet-shaped robot to enable responsiveness to temperature, humidity, and magnetic fields. A kinematic gait model is designed to control oscillatory motion in the semi-contracted state and rotational motion in the fully contracted state of the miniature robot. To automatically mitigate angular deviation between the robot's motion direction and the intended path, an adaptive orientation compensation control algorithm based on Support Vector Regression (SVR) is proposed. Experimental results demonstrate that the proposed robot exhibits capabilities for flexible and accurate navigation within unstructured environments (e.g., rock piles and stomach models), and is further shown to be capable of cargo transport. The proposed adaptive morphogenesis robots, enabled by dual-mode motion control, hold significant potential for targeted delivery and other micromanipulation applications in complex, unstructured, and confined environments.

Online gait planning plays a crucial role for the locomotion of humanoid robots. While simplified models often fail to capture critical dynamic features of the robot’s motion, making online gait modifications with constrained nonlinear optimization in complex models is highly challenging with current computational power. This paper introduces a gait planning method that leverages supervised learning to expedite the gait planning and optimization process. Building upon our previous work, this paper extends a three-body model to the three-dimensional (3D) case. This model incorporates the angular momentum and height variation of body as well as the influence of leg motions, thus facilitating the generation of omnidirectional walking and running patterns. Due to the complexity of the model, an online gait planning modification is impractical. Therefore, supervised-learning is employed to train a policy derived from the model-based gait planning approach. This policy is then implemented online to produce versatile locomotion. Furthermore, the gradient of the trained neural network is utilized for nonlinear optimization of gait parameters, significantly improving the robot’s balance against external perturbations. The effectiveness of the proposed method is validated through a series of experiments conducted in simulation and on the real robot BHR-T, confirming its capability to generate adaptive walking and running motions in response to varying demands and disturbances.

Bipedal-vehicle transformable robots (BVTRs), equipped with driving wheels, combine the flexibility of bipedal locomotion with the speed of wheeled movement. However, maintaining balance across different formations under various external disturbances remains a significant challenge due to uncertain disturbance types and dynamic shifts between formations. To address these challenges, this paper introduces the concept of Equivalent Predictive Control (EPC), which models all disturbances as unified virtual wrenches and integrates them directly into the robot’s predictive control model, treated as an inertia-varying single rigid body. By anticipating the future impact of disturbances, EPC enhances stability and enables simultaneous handling of various disturbances. To address the challenge of dynamic changes, contact variations, and shifting constraints during formation transitions, we propose Handle Point Control (HPC). HPC simplifies multi-task tracking by reducing joint space control to a set of virtual target points, called ‘handle points’, such as knees, hips, and shoulders. This method facilitates real-time formation switching by tracking different handle points. Experiments on the BVTR platform BHR8-2 validate the effectiveness of the proposed control strategies. Note to Practitioners—This paper addresses two critical challenges for applying BVTRs in real-world industrial scenarios: 1) managing various external disturbances, and 2) overcoming the complexities associated with changing dynamics and contact situations during formation transitions. The proposed EPC strategy unifies all disturbance types and integrates them into the robot’s dynamic model, enhancing stability and adaptability across formations. The HPC method simplifies control by focusing on tracking key handle points, allowing smooth, real-time formation transitions without needing multiple optimization schemes. These methods can also be applied to other robotic systems that face similar control challenges.

This paper proposes a novel nonlinear model predictive control (NMPC) method based on geometric variational calculus for high-dynamic and complex motion control of quadruped robots. By approximating system trajectory tracking error dynamics on the Special Euclidean group (SE(3)), the method avoids the singularities of Euler angles and the challenges of quaternion representation while capturing the coupling between rotational and translational dynamics for a more comprehensive motion description. Leveraging variational calculus, the resulting Geometric Nonlinear Model Predictive Controller (GNMPC) enables high-frequency updates while preserving essential nonlinear system characteristics. Experimental results across various scenarios validate the effectiveness and advantages of the proposed controller. Note to Practitioners—The primary motivation of this paper is to investigate the application of geometric methods in Model Predictive Control (MPC) and to validate their effectiveness in the context of quadruped robots, which exhibit nonlinear dynamics. In this work, the authors model the robot’s motion on a nonlinear manifold and linearize the system using variational methods. Sequential Quadratic Programming (SQP) is then applied to approximate the globally optimal solution. Experimental results demonstrate that this approach significantly improves the performance of quadruped robots, particularly in handling highly dynamic and robust motions.

High dynamic jump motions are challenging tasks for humanoid robots to achieve environment adaptation and obstacle crossing. The trajectory optimization is a practical method to achieve high-dynamic and explosive jumping. This paper proposes a 3-step trajectory optimization framework for generating a jump motion for a humanoid robot. To improve iteration speed and achieve ideal performance, the framework comprises three sub-optimizations. The first optimization in-corporates momentum, inertia, and center of pressure (CoP), treating the robot as a static reaction momentum pendulum (SRMP) model to generate corresponding trajectories. The second optimization maps these trajectories to joint space using effective Quadratic Programming (QP) solvers. Finally, the third optimization generates whole-body joint trajectories utilizing trajectories generated by previous parts. With the combined consideration of momentum and inertia, the robot achieves agile forward jump motions. A simulation and experiments (Fig. 1) of forward jump with a distance of 1.0 m and 0.5 m height are presented in this paper, validating the applicability of the proposed framework.

Magnetic helical microrobots have great potential in biomedical applications due to their ability to access confined and enclosed environments via remote manipulation by magnetic fields. However, achieving collision-free navigation for microrobots in complex and unstructured environments, particularly in highly dynamic settings, remains a challenge. In this paper, we present a novel deep reinforcement learning-based control framework for magnetic helical microrobots, focusing on the tasks of goal-reaching and dynamic obstacle avoidance. To streamline data collection, a specialized training environment capturing essential aspects of navigation for magnetic helical microrobots is devised. The robustness and adaptability of the trained policy are supported using a randomization technique within the training environment. To facilitate seamless integration with real-world magnetic actuation systems, a visual processing algorithm based on OpenCV is devised and incorporated to collect policy observations. Simulations and experiments in various scenarios validate the high robustness and adaptability of the method. The performance assessment revealed a success rate of 99% in navigating the microrobot around 4 dynamic obstacles of comparable speeds and a success rate of 90% in environments with 14 dynamic obstacles. The results indicate the potential for future applications of our method in unstructured, confined, and dynamic living environments.Note to Practitioners—The motivation of this work is to develop a robust and effective control scheme for collision-free navigation of magnetic helical microrobots in dynamic environments. The conventional navigation strategies in dynamic environments mainly include global path planning and local path replanning; thus, highly dynamic environments require frequent updates to the planned path, making it difficult to apply in highly dynamic environments. In this work, a deep reinforcement learning-based control framework is proposed that can guide microrobots through many dynamic obstacles to a series of locations without collisions. The simulation and experimental results validate the efficacy of the proposed control framework and the robustness and adaptability of the trained policy. The proposed control scheme enables better understanding of advanced motion control methods for magnetic microrobots.

The development of magnetic soft microrobots has been constrained by the lack of precise control of microactuator’s programmability. To address such an issue, we use a piezo-driven two-fingered microhand to selectively position the magnetic microactuators within a soft scaffold. Each microactuator is sequentially oriented to a desired direction through surface rotation by applying magnetic field–based torques, and is then fixed to the scaffold using ultraviolet (UV) cross-linking. A detailed analysis is conducted on the spatial positioning capability of the microhand’s parallel mechanism and the magnetic programming performance of electromagnetic coils’ orientation control. To overcome the inefficiency and inaccuracy of labor-intensive manual assembly, we propose an automated assembly strategy to create magnetic soft-bodied microrobots following our design. The multiple fabricated microrobot prototypes exhibit programmed 2D and 3D shape transformations and various robotic gaits for surface locomotion. This strategy can enable the rapid fabrication of multimaterial 3D magnetic microrobot designs with potential applications in robotics, biomedical engineering, and environmental governance. Note to Practitioners—The motivation of this work is to address the limitations in current fabrication methods for magnetic soft microrobots, which include 3D printing, heating, mold casting, and chemical synthesis. These approaches fall short when creating microrobots with arbitrary structures, multimaterial compositions, and complex magnetization profiles. Micro-assembly offers a potential solution, yet existing techniques depend on either time-consuming manual assembly or costly equipment lacking sufficient flexibility for 3D spatial manipulations. In this work, a 3-degree-of-freedom, high-precision parallel microhand-based assembly method is proposed. The microhand provides high precision and speed for rapid positioning of assembly modules, while electromagnetic coils enable precise orientation in magnetization programming. Experimental results confirm the effectiveness of this method and demonstrate the robustness and adaptability of the automated fabrication strategy. This approach facilitates the fabrication of magnetic soft microrobots with specific deformation and locomotion capabilities according to design intent.

Dynamic objects pose significant challenges to the accuracy of state estimation and map quality in Simultaneous Localization and Mapping (SLAM). While current dynamic SLAM methods often rely on semantic information to detect specific movable objects, this dependency on pre-trained models and semantic priors can lead to false dynamic detections. This paper presents a novel semantic-independent dynamic SLAM method that detects truly moving regions, without being constrained by the classes or motion patterns of dynamic objects. We introduce a geometric re-clustering approach to improve object clustering by addressing the under- and over-segmentation caused by the K-Means algorithm. Next, instead of simply classifying entire clusters as dynamic or static, we propose a method to detect dynamic regions within each cluster based on dense optical flow residuals. This enables the detection of partial object movements, such as a seated person moving only his hands. Dynamic detection results are propagated across consecutive frames as dynamic priors for calculating optical flow residuals. Additionally, to enhance map quality, we address the mis-detection of slowly or intermittently moving objects through depth consistency checks applied over a larger time interval. Extensive evaluations on public datasets (TUM and Bonn) and real-world scenes show that our method outperforms state-of-the-art semantic-based methods in terms of localization accuracy and generalizability across various scenarios, particularly when facing unknown dynamic objects. Our method also achieves clean and dense reconstructions, demonstrating its potential for applications like robot navigation in dynamic environments.

Soft millirobots are highly promising for biomedical applications due to their reconfigurability and multifunctionality within physiological environments. However, the diverse and narrow biological cavity environments pose significant adaptability challenges for these millirobots. Here, we present a dual-morphology, thin-film millirobot equipped with a magnetic drive head and a functional tail to facilitate multimodal motion and targeted cell delivery. The millirobot can reversibly switch between two distinct morphologies in response to environmental stimuli through the deformation of its hydrogel body. Utilizing these dual morphologies, the millirobot can perform robust multimodal fundamental motions controlled by magnetic fields. We encapsulate fundamental motions with specific programmable magnetic field parameters into motion primitives, allowing easy invocation and adjustment of motion modes on demand. A knowledge graph is established to map terrain features to motion units, enabling the identification of optimal motion modes based on typical terrain characteristics. Experimental results indicate that the millirobot can effectively switch its morphology and movement modes to navigate various terrains, including narrow and curved channels as small as 1 mm, 0.8 mm high stairs with a 15° incline, and even the complex environment of a swine intestinal lumen. Its functional tail can carry immune cells to target and kill cancer cells. This robot can transport drugs and cells while navigating complex terrains through multimodal motion, paving the way for targeted medical tasks in intricate human environments in the future.

In recent years, micromanipulation technology has achieved extensive applications in industry and life science. Improving the precision and bandwidth of the micromanipulator and simultaneously reducing size, weight, and cost pose significant challenges to the existing micromanipulator design and fabrication methods. Here, we propose a 3-RRS micromanipulator with an origami-inspired spherical joint based on the PC-MEMS process, aiming for miniaturization and cost-effectiveness. The spherical joint allows rotations of 140° around the x-axis approximately, 140° around the y-axis approximately, and 20° around the z-axis approximately. The micromanipulator has weights of 0.8 g, dimensions of 16 mm × 16 mm × 22 mm, and workspace of 0.7 mm3. The end platform of the micromanipulator can be equipped with various effectors to accomplish different kinds of tasks. Experimental results validated its high precision and bandwidth, exhibiting its potential to perform intricate micromanipulation tasks.

Micro-assembly is an emerging method to fabricate microrobots with multiple modules or particles. However, there is always a lack of a flexible and efficient method to freely create the desired magnetic soft microrobots. In this paper, an automated assembly system based on a two-fingered microhand is presented for fabricating magnetic soft microrobots. Our proposed system can automatically pick and place components to assemble microrobots with a two-fingered micromanipulator, and orient these components through an external magnetic field. The automated assembly has the advantages of high accuracy, high speed, and high success rate. It can endow magnetic microrobots with flexible material selection, arbitrary geometry design, and programable magnetization profile. We can make full use of this system to fabricate multiple magnetic soft microrobots. The experiment results demonstrate that this system can efficiently fabricate microrobots with excellent mechanical properties, which have application potential in robotics, biomedical engineering, and environmental governance.

High-frequency and accurate state estimation is crucial for biped robots. This paper presents a tightly-coupled LiDAR-Inertial-Kinematic Odometry (LIKO) for biped robot state estimation based on an iterated extended Kalman filter. Beyond state estimation, the foot contact position is also modeled and estimated. This allows for both position and velocity updates from kinematic measurement. Additionally, the use of kinematic measurement results in an increased output state frequency of about 1kHz. This ensures temporal continuity of the estimated state and makes it practical for control purposes of biped robots. We also announce a biped robot dataset consisting of LiDAR, inertial measurement unit (IMU), joint encoders, force/torque (F/T) sensors, and motion capture ground truth to evaluate the proposed method. The dataset is collected during robot locomotion, and our approach reached the best quantitative result among other LIO-based methods and biped robot state estimation algorithms. The dataset and source code will be available at https://github.com/Mr-Zqr/LIKO.

Micromanipulation techniques that can achieve controlled fine operations at the micro scale play an important role in biomedical fields including embryo engineering, gene engineering, drug screening, and cell analysis. However, micromanipulation of biological micro-objects, such as cells and micro tissues, suffers from mechanical damage and low efficiency. Several techniques have been introduced to manipulate cells more easily, but most of them are restricted by expensive devices, limited work area, and potential damage to cellular structure. Here we develop a hydrodynamic manipulation method to rotate and transport mouse oocytes, which utilizes acoustic waves and micropipette to generate acoustic radiation force and excite microstreaming. This method can accomplish rotational and translational operations precisely and controllably. We tested the process of trapping, rotation, and transportation of the mouse oocytes, and measured rotational and translational speed with a range of applied voltage. The method was able to shorten the cost time of delivery and posture adjustment before oocyte injection. Our study provides an easy-to-use technique for oocyte manipulation without contact, and it has the potential to be universally applied in many cellular studies.

Recent advances in both legged robot locomotion and Reinforcement Learning have shown a promising path for developing bipedal robot controllers. While the difference in dynamics between real world and simulation, also known as reality gap, still hinders the use. In this paper, we focus on sim-to-real bipedal robot locomotion task. We leverage the recent advances in auto-tuning sim-to-real transfer and use it to address sim-to-real bipedal robot locomotion problem. Similar to existing work, we first train a parameter searching model with dataset collected from simulator and use real-world data to tune the simulation parameters. However, the prediction tuning can be unreliable if the training dataset distribution fails to cover the real-world data. We address this problem by formulating this problem as an Out-of-distribution problem and further extending the current framework with a dataset verification model. With extended module, our method is capable of tuning the simulation parameters safely and efficiently. We demonstrate our method outperforms existing work and achieves sim-to-real bipedal robot locomotion on bipedal robot BITeno.

For humanoid robots, online motion generation on non-flat terrain remains an ongoing research challenge. Computational complexity is one of the primary restrictions that preclude motion planners from generating adaptive behaviors online. In this paper, we investigate this problem and decompose it into two sequential components: an Efficient Behavior Generator (EBG) and a Nonlinear Centroidal Model Predictive Controller (NC-MPC). The EBG is responsible for optimizing the physically feasible whole-body template behaviors, which can provide reliable warm-starts for NC-MPC, thereby greatly reducing the computational effort of online planning. With tailored objective function and feet complementary constraints, the EBG can search for a near-optimal solution after several iterations within seconds for different behaviors including walking, running, and jumping, even with intuitive initial guesses. To make the template behaviors extensible when the robot encounters possible different scenarios, the NC-MPC is proposed to regenerate the reactive motion online to adapt it to the real local environment. Finally, we validate the effectiveness of synthesizing EBG and NC-MPC for humanoid locomotion on non-flat terrain in simulation and on the real humanoid robot BHR7P.Note to Practitioners— For current humanoid robots, dynamically traversing non-flat terrain such as stairs, slopes, and gaps in the real world presents a significant challenge. In this paper, we propose an adaptive motion planner for humanoid robots to traverse non-flat terrain, which is properly integrated into the closed loop of online control. Considering computational complexity and motion extensibility, the planner consists of two parts: an efficient behavior generator performed offline and a nonlinear model predictive controller performed online. The behavior generator can efficiently generate template behaviors for the humanoid robot, including various gaits such as walking, running, and jumping. To make these template behaviors adaptable, a nonlinear model predictive controller based on the centroidal dynamics model is developed to plan reactive motions online. It can extend template behaviors to fit potentially different scenarios in practice. The proposed method is validated in simulations and experiments with the humanoid robot BHR7P. Furthermore, this method can be applied to legged robots or systems that need to move dynamically on non-flat terrain, such as quadruped and hexapod robots.

Microrobotic contact manipulation enables automated and precise cell capture, positioning and screening and has potential in biomedical engineering and disease detection. However, when using an optical microscope for visual positioning of targets, the poor clarity, limited cell-background contrast and lack of global 3D information of the cells in the field of view hinder global strategy making and automation, thereby affecting the accuracy and efficiency of manipulation. Here, we propose the 3D locating of multiple biological targets based on digital holography. Global–local combined visual feedback is developed for overall spatial locating and partial locating in a liquid-phase bright-field environment. By applying a filtering-based planar locating algorithm and maximum-area-based depth detection algorithm, the 3D global distribution of micro-targets in the field of view is periodically updated with a high detection rate. By applying a planar locating algorithm based on a convolutional neural network and a depth detection algorithm based on a gradient descent, the 3D fast locating of targets is performed precisely. Experiments show that the detection rate of the global positioning is 95.1%, the mean average precision of the local planar positioning is 90.53%, and the deviation of the local depth positioning is$1.22~\mu \text{m}$. When capturing cells, this method reaches an average speed of 7.4 cells/min and a collection rate of 90.5%. We anticipate that our method will support the research in cell-based bioengineering including cell screening and early disease diagnosis. Note to Practitioners—Automated cell manipulation is one of the most significant techniques in cell-based biomedical applications. This paper introduces a three-dimensional multi-target visual positioning method for automated cell manipulation. Combining with holographic imaging technique, the global information for the cells in the limited field of view is provided with improved imaging clarity and extended depth of field. The visual recognition algorithm can screen and extract the rare cells in the cell population, which will support the research in the field of early disease diagnosis and biomedicine. The research outcome provides an effective and precise solution to achieve biological targets positioning, capture, and screening within 3D liquid-phase bright-field environment.

Magnetic helical microrobots have attracted considerable attention in navigation control. However, the performance of microrobots is negatively affected by time-varying uncertain perturbations and obstacles, at the microscale. In this study, we present a navigation control scheme for accurately guiding the helical microrobot to targeted positions in dynamically changing environments. To efficiently plan smooth paths, a search-based algorithm with pruning rules is implemented to quickly find collision-free waypoints and design an optimal method with spatial and dynamic constraints for obtaining smooth paths globally. Velocity gain and potential fields are integrated to develop an emergency local motion replanning method for addressing random obstacles that suddenly appear in the preset path. In order to attain microrobot system dynamic linearization and achieve precise path following of a helical microrobot, a robust control strategy that integrates geometric and model-free controllers in a complementary manner is presented. The geometric controller as a feedforward controller, responsible for managing path information and generating guidance laws. In contrast, the model-free controller operates as a feedback controller, specifically designed to rapidly address position deviation. Meanwhile, we employ an observer to compensate for disturbances. Experimental results of precise motion control in both static and dynamic environments demonstrate the effectiveness of this navigation control scheme, which is promising for moving with high accuracy in cluttered and dynamic living enclosed environments.Note to Practitioners—This paper was motivated by the problem of the navigation control of magnetic microrobots in dynamic environment. The existing navigation control methods of microrobots mainly focus on the static environment, which is challenging to meet the emergency obstacle avoidance requirements in the cluttered environment with low Reynolds number. In addition, the conventional path following control always ignores the nonlinearity of the microrobot system, resulting in insufficient following accuracy. In this work, a novel navigation control method for microrobots is proposed, which can guide microrobots to accurately follow dynamically planned paths in cluttered environments without collision. Simulations and experiments validate the performance of the proposed navigation control method using helical microrobots. The proposed navigation control method paves the way for a better understanding of advanced navigation control method for magnetic microrobots.

Manipulating heterogeneous microtargets based on optoelectronic tweezers (OETs) to construct micropatterns with specific distribution and ordered arrangement enables recapitulating the spatial architecture of cells in native tissues, and has significant potential in tissue regeneration, medical diagnostics, and cell behavior research. However, the uncertain disturbances in liquid environment, collision risk, and electrokinetic interference in OETs system can cause microtargets to deviate from the safe and controlled state, especially for manipulation tasks with heterogeneous microtargets. Here, we propose an improved hierarchical value iteration network (IHVIN)-GAT-based path planning method for parallel manipulation of heterogeneous microtargets with independent control, integrating goal assignment, feature extraction, and decentralized decision-making. The Kuhn-Munkres-based goal assignment model periodically modifies the matching relationship between microtargets and goal positions to reduce the task complexity. High-order features involving path planning are extracted by an IHVIN model, and then selectively aggregated and convolved through GAT to yield real-time locomotion strategies for all microtargets. For the issues of constraint variability and system heterogeneity, discrete locomotion constraints are developed through analysis of escape mechanism, then embedded into modeling procedures and converted to heterogeneous edge weights in graph domain. The simulation and experimental results demonstrate the desired performance of the IHVIN-GAT model in high timeliness, high strategy quality, and compatibility for microtarget number, where up to 20 microtargets from three categories are parallel manipulated within 16 s to form arbitrary micropatterns recapitulating microscale architecture of cells in native tissues. We anticipate that our method will contribute to construct more biomimetic microstructures with heterogeneous cells for biomedical applications in the future.

Micron-range untethered, magnetic helical robots have great potential for biomedical applications due to their desirable performance with high flexibility and accuracy in unstructured and confined environments. However, at the microscale, time-varying uncertain disturbances in the environment and electromagnetic system greatly hinder helical microrobot tracking control performance. When a microrobot is replaced or even a derivative version with a slight helical body structure change is used for different tasks, the performance of the original control scheme remarkably decreases or even becomes ineffective. Here, we propose a data-driven optimal integrated controller (D2-OIC) that realizes precise tracking and transfer control among a series of helical microrobots with derived structures in different situations. The control approach has a parallel structure with nonlinear feedforward and linear feedback controllers. The nonlinear feedforward controller inversely maps the relationship between the electromagnetic field state and the helical microrobot motion state, allowing the helical microrobot to quickly approach the desired motion state. The linear feedback controller effectively adjusts the controller parameters using the virtual reference feedback tuning (VRFT) method, thus eliminating any residual motion errors arising from nonlinear control. By retraining on newly acquired and collected cumulative data with assigned weights, the nonlinear feedforward controller is updated to achieve transfer control among various helical microrobot types. In the experiment, two helical microrobot types performed arbitrary path tracking and obstacle avoidance tasks with tracking errors consistently less than 4% of the microrobot body length, demonstrating the feasibility of the proposed method.

In the last decade, researchers have been trying to develop many microrobots that mimic the extraordinary abilities of bionts in complex environments. How to fabricate the biomimetic microrobot with satisfying deformability and complex shapes to realize desired precise motion is the key issue. In this paper, we proposed an efficient programable fabrication method of the magnetic soft micro-robot through an on-chip photopolymerization system. The superparamagnetic nanoparticles were compiled according to the magnetic anisotropy and assembled in the micro-robot. Then these nanoparticles were immobilized by photopolymerization of the hydrogel polymer. With this fabrication method, a joint rotation mechanism was first fabricated to characterize the deformation performance under the magnetic field control. Besides, the snake-like micro-robot were also fabricated, and the desired motions were achieved. The experimental results show that the proposed programable on-chip fabrication of magnetic soft micro-robot has the potential to facilitate the development of magnetic microrobots and their applications in the biomedical field.

Highly dynamic movements such as jumping are important to improve the agility and environmental adaptation of humanoid robots. This article proposes an online optimization method to realize a vertical jump with centroidal angular momentum (CAM) control and landing impact absorption for a humanoid robot. First, the robot's center of mass (CoM) trajectory is generated by nonlinear optimization. Then, a quasi-sliding mode controller is designed to ensure that the robot tracks the CoM trajectory accurately. To avoid unexpected spinning in the flight phase, a center-of-pressure-guided angular momentum controller is designed to stabilize the CAM. The modifications of CoM and CAM are realized by online optimization of dynamic components and inverse dynamics. Two quadratic programming optimizations are utilized to generate feasible contact force/torque and joint acceleration referring to uplevel CoM and CAM controllers. In addition, a viscoelastic model-based controller is designed to absorb the vibration caused by a large contact impact. A simulation and experiment of a 0.5-m high (foot lifting distance) vertical jump are achieved on a humanoid robot platform in this article (Fig. 1).

In this paper, we fabricated a soft sensor based on PEDOT:PSS for thin film structure. The developed soft sensor can measure the contraction force at real time to be embedded in a modular bio-actuator [1]. The modular actuator generated contraction forces at 0.3 mN when applying electric pulse stimulation. To measure millinewton contraction forces and make a built in sensor, we fabricated a soft sensor using PEDOT:PSS-PDMS film. To verify that the sensor can measure the force of the actuator and can be integrated to the actuator, we analyzed characteristic of the sensor. First, we measure Young's modulus of the sensor and compare them with the bio-actuator. From the previous research [2], the Young's modulus of the bio-actuator and sensor were 45.8 kPa and 165 kPa, respectively. In addition, we simulated the sensors to estimate the change of the displacement according to the applied force. Next, we have experiments by stretching sensors using stepping motor to measure the resistance change of the sensor. From the simulation data, the displacement change is 23 µm when applying 0.3 mN of forces and then we detect the displacement change smaller than is 20 µm from the experiments. Finally, we analyzed the movement of the bio-actuator when applying stimulation using high speed camera and time response of the developed sensor. The actuator was contracted to the maximum after 150 ms from the electrical stimulation and the sensor detected the repeated motion at 10 Hz without time delay. As a result, the proposed sensor can measure the force of bioactuator at real time.

The magnetic microrobot has become a promising approach in many biomedical applications due to its small volume, flexible motion, and untethered micromachines. The micro-chain robot is one of the most popular magnetic microrobots. However, the uncontrollable magnetic moment direction and quantity of the magnetic beads consisted in the existing self-assembled micro-chain robot limit their locomotion and applications. This paper proposed an on-chip micro-chain robot fabrication method to assemble the magnetic beads with controllable magnetic moment direction and quantity. The bead quantity can be controlled by the structure limits of the microchannel, and the direction of the magnetic moment can be adj usted by the integrated external magnetic field. The assembled magnetic beads are then glued by the hydrogel under UV exposure. The micro-chain robots with different quantities and magnetic moment directions of the magnetic beads were successfully fabricated and tested in experiments. Due to the array structure of the microfluidic device, batch manufacturing of low-cost magnetic robots was achieved in our method. The movement of dual-bead microrobots with two orthogonal magnetic moment directions was analyzed and compared. One of the dual-bead microrobots was applied in the transportation of the hydrogel module using pushing and pulling modes. It indicated that the proposed controllable on-chip fabrication of the magnetic micro-chain robots has the potential to enhance the microrobot ability in biomedical applications.

Microfluidic devices for cell immobilization have significantly advanced the biological analysis at the single-cell level. However, the previous research on immobilization of multiple single cells, especially with deterministic quantities, is insufficient. In this paper, we proposed a novel microfluidic device based on the passive hydrodynamics and the uniform geometric design principle, which can array different numbers of cells in every capture cavity. The capture cavities could be stretched to accommodate more cells, and the trapping force was adjusted by modifying the related geometric parameters of the inside channel. The whole procedure was monitored and further automatized by integrating computer vision technology under a microscope. On the proposed integrated on-chip platform, we realized full-automated arraying of a single cell, two cells, and three cells on a single chip, achieving success rates up to 95%, 75%, and 72%, respectively. As a primary experimental demonstration, the cell viability test of arraying multiple cells with different quantities showed excellent biocompatibility and no significant association between trapping quantity and cell survivability. We envision that the proposed quantity-controllable, high-efficiency microfluidic devices for multiple cell arraying could be a powerful platform for an in-depth study of cell heterogeneity and cell communication between multiple cells.Note to Practitioners—This article is motivated by the biomedical applications of multi-cell arraying. The designed microfluidic devices employ passive hydrodynamics, and the capture cavities are stretched to accommodate different numbers of cells. The whole arraying procedures are automatized using computer vision technology. Simulations and experiments demonstrate the high efficiency, controllability of the cell quantity, and excellent biocompatibility.

Stability control for humanoid robots based on zero moment point (ZMP) control and impedance control are widespread. However, uncertain changes in the center of mass (CoM) height for ZMP control and specific regulation of the variable stiffness of impedance control have been challenging issues in previous studies. In this article, these two problems are solved by implementing fuzzy control-based regulations. First, the fuzzy ZMP controller, which regulates the feedback gains online based on the CoM height change and CoM tracking errors, is proposed. Second, we propose a fuzzy regulation law for variable stiffness, which is applied for uncertain contact situations and inspired by the pattern of human muscle stiffness. With these two methods, the ground adaptability for humanoid robots is enhanced. The proposed method is validated with experiments on a real robot platform, BHR-T.

Legged robots are very promising for use in real-world applications, but their operation in narrow spaces is still challenging. One solution for enhancing their environmental adaptability is to design a small-sized biomimetic robot capable of performing multiple motions. By capturing a decent representation of an actual rat (rattus norvegicus), we developed a small-sized quadruped robotic rat (SQuRo), which includes four limbs and one flexible spine. On the basis of the extracted key movement joints, SQuRo was subtly designed with a relatively elongated slim body (aspect ratio: 3.42) and smaller weight (220 g) compared with quadruped robots of the same scale. Accordingly, we propose a control framework for multimodal motion planning, and the appropriate control parameters were tuned through optimization with consideration to the stability and actuation limits. The results obtained through a series of experimental tests reveal that SQuRo achieves a superior motion performance compared with existing state-of-the-art small-sized quadruped robots. Remarkably, SQuRo has an extremely small turning radius (0.48 BL) and strong payload capacity (200 g), and it can recover from falls.

Many previous works have facilitated muscle cell (C2C12) alignment to form fiber-like cell structures. However, there still remains a challenge how to induce C2C12 myoblasts in the cell structures to differentiate into matured myocytes to form a functional muscle tissue, while external mechanical stimulation has been proved to have good effects on proliferation and differentiation of myoblasts. In this paper, we proposed a vision-based micro robotic manipulation system to achieve automatic mechanical stimulation for one single muscle fiber-like cell structures (MFCS). A tube, which is attached to a three degree-of-freedom (DOF) manipulator, and a probe are employed to apply the uniaxial mechanical stimulation to train the MFCS. To measure the force applied on MFCS, a vision-based measuring and correction method is utilized, which decrease the error by 74%. Moreover, based on the viscoelastic property of the MFCS, a feedback control algorithm has been applied to compensate for the force loss to realize the force stimulation. And the final value of force remains 699 ± 1μN after 110s experiment.

In spite of recent high attention of the biohybrid robot system, the previous researches focused on actuation system depend on simple on/off control without feedback control. To solve this problem, we proposed a soft sensor for feedback control of a bio-actuator driven by skeletal muscle. The proposed soft sensor can measure contraction forces of the proposed bio-actuator [1]. The bio-actuator was constructed with tendon structure and culture template made by polydimethylsiloxane (PDMS). It generated contraction forces at 0.3 mN when applying electrical stimulation. To measure that kind of small amount of contraction forces (0.3 mN), we fabricated a soft sensor using liquid metal, Galinstan, and HTV-2000. At first, we measured the Young’s modulus of the bioactuator and sensor and then fabricated the soft sensor having 68.52 kPa of Young’s modulus that is similar the bioactuator (45.8 kPa). Next, we simulated the sensor to estimate the resistance change according to the applied force. Since the resistance change is too small, we design the circuit to amplify the signal. Then, we detect very small resistance at milli-ohm. In addition, we analyzed time response to detect signal of actuator faster than 200 ms. As a result, the proposed sensor can measure the force of bioactuator without time delay.

In this paper, we propose an automatic robot-rat interaction framework that enables a robotic rat to realize real-time localization, tracking and movement analysis of a laboratory rat. Specifically, we combine an object detector with stereo matching to achieve fast localization of the laboratory rat. Combined with the rat-like motion of the robot, one-step tracking of the rat is achieved, which enables the robot to eliminate last location error in one cycle of visual servo control. When positioning the rat, a unified quantitative description and analysis of rat motion is implemented by using a state vector composed of the centroids of head, body and tail. Preliminary robot-rat interaction tests show that the robot achieved a steady tracking of a fast-moving rat for a duration of 10 minutes. To the best of our knowledge, it is the first time that a rat-sized robot achieves a continuous tracking of actual rats by a built-in miniature stereo vision system. Experimental results show that the sequence of state vectors accurately represents the pitch movement of the rat. Thus, this work is a step toward more natural interaction between robots and animals.

For a small-sized biomimetic robot, it is challenging to mimic animal-like motion with high speed and high flexibility. To enable high flexibility, high stability, and high biomimicry degree for the robotic rat, we drew inspirations from three agile rat movements, namely, the pitch, yaw, and U-turn movements. First, we proposed key movement joints (KMJs) to capture a decent representation of the rat with a reduced-order model. By extracting the primary KMJs, we determined the number and distribution of robotic joints for the design of a bioinspired spine mechanism. Second, to meet the demand of high biomimicry degree, we generated an optimal compensation term to minimize the trajectory error introduced by simplifying the model. Moreover, we calculated the optimal minimum motion cycle based on the constraints of equilibrium under extreme conditions to ensure high flexibility without compromising the stability. Finally, the proposed method was successfully verified through simulation and experimental tests with a robotic rat endowed with the bioinspired spine mechanism.

In this paper, an automated tracking system with head and tail recognition for time-lapse observation of free-moving C. elegans is presented. In microscale field, active C. elegans can move out of the view easily without an automated tracking system because of the narrow field of view and rapid speed of C. elegans. In our previous works, we constructed an automated platform with 3D freedom to track centroid region of the nematode successfully. However, tracking time was not long enough to support a full time-lapse observation. Our proposed system in this study integrate the detection method in horizontal plane with depth evaluation more tightly. Tracking time and response speed have been greatly improved. Besides, we make full use of curvature calculation to make the system recognize the head and tail of C. elegans and the recognition rate can be up to 95%. The results demonstrate that the system can fully achieve automated long-term tracking of a free-living nematode and will be a nice tool for C. elegans behavioral analysis.

Interaction with the environment is arguably one of the necessary actions for many robot applications such as haptic devices, manipulation, parts assembly, cooperation with humans, and the use of tools. Taxonomy of interaction behaviours is classified into three categories: cooperation, collaboration, and competition. In theory, interaction dynamics may be modelled by D'Alembert's principle or nonsmooth mechanics through seeking equality and/or inequality kinematic constraints. However, it is hard to gain these kinematic constraints in practice since they may be variable or be hardly described in a mathematical form. As a result, bond graph methodology is preferred in interaction dynamics modelling.In this paper, passivity and passivity indices with the differential operator are put forward by restricting its domain from the whole extended Hilbert function space to a set of all continuous function with finite derivative, and then the input-output stability condition, in this case, is derived. Next, mechanical impedance and admittance are defined, and a linear spatial impedance representation is given from the energetic point of view. Base on the bond graph theory, an ideal model is presented to model the idealized interaction, and invariance of port functions derived from the ideal interaction model is introduced; An interaction model is then proposed accounting for nonidealized factors and to describe cooperative, collaborative, and competitive interactions in a unified way. Finally, interaction stabilities are analyzed corresponding to different interaction models, and robustness of interaction stability is addressed based on the passivity indices.

In this paper, we have constructed actively perfusable multiple hepatic lobule-like vascular networks in a 3D cellular structure by using magnetic tweezers. Without well-organized channel networks, cells in a large 3D tissue cannot receive nutrients and oxygen from the channel, and therefore, the cells will be dead after few days. To construct well-organized channel networks, we fabricated a hepatic lobule like vascular networks by using magnetic fields in our previous works. However, the size of the hepatic lobule like vascular network was more than five times larger than real hepatic tissue. To improve the previous research, we have proposed several things. First, we have constructed the vascular network having similar size of the real thing in this step. Second, we have cultured the constructed structure for a long-time (more than two weeks) to verify the biocompatible condition. Third, we assemble the constructed hepatic tissues to make a large size of organ, liver. Finally, an actively perfusable system have been adopted to implement a bioreactor system by adding micro pump.

Compliance is important for humanoid robots, especially a position-controlled one, to perform tasks in complicated environments where unexpected or sudden contacts will result in large impacts which may cause instability or destroy the hardware of robots. This paper presents a compliance control method based on viscoelastic model for humanoid robots to survive on these conditions. The viscoelastic model is used to obtain the relationship between the differential of contact force/torque and linear/angular position. Thus a state equation of this model can be established and a state feedback controller adjusting the position to adapt to the contact force/torque can be designed to realize the compliant movement. The proposed compliance control method based on viscoelastic model has been employed in ankle compliance for stable walking on indefinite uneven terrain and arm compliance for falling protection on BHR-6P, a position-controlled humanoid robot, which validates its effectiveness.

Path planning under two-dimensional maps is a fundamental problem in mobile robotics and other real-world applications (unmanned vehicles, navigation applications for mobile phones, and so forth). However, traditional algorithms (graph searching, artificial potential field, genetic, and so forth) rely on grid-by-grid searching. Thus, these methods generally do not find the global optimal path, and as the map scale increases, their time cost increase sharply, except artificial potential field. A few algorithms that do not rely on grid-by-grid searching (rapidly-exploring random tree, visibility graph, and tangent graph) have special requirements for maps. Considering that the shortest path is composed of tangents between obstacles, in this paper, we propose a method called ReinforcedRimJump (RRJ) that does not rely on the point-by-point traversal but rather obtains the shortest path by finding the tangent multiple times between obstacles. The first improvement of this method is the precomputation of tangents, which causes the method to have a lower time cost than traditional methods. The second improvement of RRJ is edge segmentation, which allows RRJ to be used when the target is in the depression of the obstacle. To verify the theoretical advantages of RRJ, some comparative experiments under various maps are performed. The experimental results show that RRJ can always find the shortest path in the shortest time. Furthermore, the time cost of RRJ is insensitive to the map size compared to other methods. The experimental results presented herein demonstrate that RRJ meets the theoretical expectations.

Controlling a robot astronaut to move in the same way as a human astronaut to realize a wide range of motion in a space station is an important requirement for the robot astronauts that are meant to assist or replace human astronauts. However, a robot astronaut is a nonlinear and strongly coupled multibody dynamic system with multiple degrees of freedom, whose dynamic characteristics are complex. Therefore, implementing a robot astronaut with wide-ranging motion control in a space station is a tremendous challenge for robotic technology. This article presents a wide-ranging stable motion control method for robot astronauts in space stations based on human dynamics. Focusing on the astronauts' parking motion in a space station, a viscoelastic dynamic humanoid model of parking under microgravity environment was established using a mass-spring-damper system. The model was used as the expected model for stable parking control of a robot astronaut, and the complex dynamic characteristics were mapped into the robot astronaut system to control the stable parking of the robot astronaut in a manner similar to a human astronaut. This provides a critical basis for implementing robots that are capable of steady wide-ranging motion in space stations. The method was verified on a dynamic system of a robot astronaut that was constructed for this research. The experimental results showed that the method is feasible and effective and that it is a highly competitive solution for robot astronauts with human-like moving capabilities in space stations.

In this paper, we have fabricated a multilayered hepatic lobule-like vascular network in a 3D tissue using a heptapole magnetic tweezer. The tissue consists of cell-laden hydrogels with 3D channel networks. To fabricate multilayered channel system, magnetic hydrogel fibers were manipulated by a magnetic tweezer. The hepatic lobule tissue shows a hexagonal structure with different sizes of veins. Six portal veins transfer the blood including nutrients and oxygen to a central vein by sinusoids. The portal and central veins are made by steel rods, whereas the magnetic hydrogel fibers has a role of sinusoids. An important point of this research is to connect two veins - portal and central vein - by magnetic fibers. For this, we used magnetic tweezer with seven poles to magnetize the steel rods. In order to generate high magnetic fields, we design magnetic tweezer with a flat tip and additional lower tweezer based on simulation data. The manipulation was performed in fibrin gel inside rat liver cells. By applying high magnetic fields, we attracted magnetic fibers to the steel rods and constructed 3D channel network in cellular structure. To verify the efficiency of the channel, we supply culture medium to the channel and then analyze the cell viability according to the distance from the channel. As a result, the cells located at close to the channel show higher cell viability than others.

The research on rare cells makes a significant contribution to biology research and medical treatment for the application of diagnostic operation as well as prognoses treatment. Therefore, sorting them from heterogeneous mixtures is crucial and valuable. Traditional cell sorting methods featured with poor purity and recovery rate as well as limited flexibility, which are not ideal approaches for rare type. In this paper, we proposed a cell screening method based on automated microrobotic aspiration-and-placement strategy under fluorescence microscope. An innovative autofocusing visual feedback (AVF) method is proposed for precise three-dimensional (3D) locating of target cells. For depth detection, multiple depth from defocus (MDFD) method is adopted to solve symmetry problem and attain an average accuracy of 97.07%. For planar locating, Markov random field (MRF) based locating method is utilized to separate and locate the overlapped cells. The end actuator locating and real-time tracking are performed relying on normalized cross-correlation (NCC) method. Experiential results show that our system collects rare cells (100 cells ml-1) at a speed of 5 cells min-1with 90% purity and 75% recovery rate, which is valuable for biological and medical application.

We have successfully achieved manipulation and assembly of microbeads having the size of 100μm diameter by hemispherical end-effectors with high stability and accuracy. The motivation of achieving assembly of actual cells lies in the great significance of it in tissue regeneration and cell analysis. Firstly, the most difficult problem we need to solve is the releasing problem caused by adhesion force. The viscosity on cell surface is much larger than the microbeads which makes cell releasing challenging. Secondly, the cell can generate its deformation, then contact area with end-effector will change during grasping process. This may influence the adhesion force and also bring problem to releasing. Thirdly, cell is much smaller, around 15μm in diameter, so we need to fabricate smaller end-effector to achieve successful manipulation and ensure the stability in the meantime. In this paper, we realize the manipulation by decreasing the adhesion forces and apply vibration to release a cell stably. We found the appropriate scale size for the end-effector is around 10μm diameter. It can not only grasp a 15μm cell but also bring little interference to the environment. As a demonstration of the proposed manipulation method, the repeated experiments were conducted to explore the dependence of adhesion force on the grasping distance, which can be helpful in the improvement of successful rate. Finally, we achieved automatic cell assembly using Hela cells.

Social interaction between a robot and rats is important since the robot can generate reproducible social behaviors across trials. However, lacking internal state feedback from the rat makes current robot-rat interaction a very preliminary level comparing with rat-rat interaction. Previous biological studies showed that ultrasonic vocalizations (USVs) emitted by a rat are expressions of its internal emotional states, which therefore can be used as part of feedback for a robot-rat interaction. The challenge is to accurately identify rat USVs in real-time from mix sounds generated by the robot in a noisy environment. To address these problems, we propose an SVM-based rat USVs identification method. This SVM method uses three types of features to represents the characteristics of mix sound and use these multidimensional features to identify rat USVs. Results show that our identification method has an accuracy of 84.29% with only 4.84% false-positive rate. Furthermore, we carefully design the filter window length with respect to sound chunk length and use only one microphone to record the mix sound. All of these efforts are to reduce the calculation time to realize real-time identification. Eventually, the identification process can be executed within 3. 5ms, which definitely meet the real-time demand. This research lays the foundation of the feedback based interaction between rat and robot, and also shows promise in the study of ethology and the interaction between robot and animals.

Generally, large knee joint torque is required when a leg is flexed. However, the large torque motor will increase the robot size and total weight. Also, a large reduction ratio gear box to realize the large torque will decrease output speed and lower backdrivability of the joint. It makes the robot difficult to perform agile and flexible motion like animals. In this paper, we propose a robot leg with a knee joint mechanism consisting of a variable reduction ratio crossed four-bar linkage mechanism (VRRCFLM) based on cruciate ligament of an animal. The VRRCFLM is to increase the reduction ratio for large knee flexion postures without greatly reducing the total backdrivability. In this paper, we developed a robot leg with a knee joint mechanism consisting of the VRRCFLM. In order to design the link parameters of the leg mechanism, optimization design aimed at maximizing the jumping height of the robot was performed. The robot model with the designed mechanism was evaluated through the dynamics simulations. Thanks to the VRRCFLM, the required torque of the knee motor at the large flexion postures was decreased. Moreover, the vertical jumping height was improved by 24.6 % comparing with a model without the mechanism. In experiments using the prototype, the required static torque was decreased as in simulation, and the jumping height was more than one leg length.

Humanoid robots are being designed to perform tasks currently carried out by human workers in industry, manufacturing, service, and disaster assistance. To this end, the humanoid robot should be able to walk stably across many types of terrain. However, when traversing a complex unknown environment, it is difficult to realize accurate terrain perception immediately through large data collected by the sensor system, leading to a difference between planned foot landing positions and actual foot landing positions. As a result, an unexpected contact force/torque may affect the stability of the robot. This paper adopts active contact perception instead of terrain perception and proposes a contact force/torque control method based on the viscoelastic model to address this problem. In addition, we design a body stability controller based on tracking the trajectories of the virtual repellent point (VRP) and the divergent component of motion (DCM) to restrain the disturbance caused by the unexpected contact force/torque. Simulations and experiments on the BHR-6P humanoid robot platform demonstrate the proposed contact force/torque control method for walking on indefinite uneven terrain.

For real-world applications, a biped robot should maintain stable walking when subjected to sudden external disturbances. Typically, unexpected changes to body acceleration indicate that a robot is experiencing an external disturbance. This paper presents a biped walking controller for rapid response to large external disturbances. First, a novel adjustment algorithm for foot placement is proposed. Here, zero-moment point variations are mapped onto the new foothold based on calculations from changes in body acceleration. Second, a novel impact reduction control for foot landing is presented based on abating body vibrations. To avoid false detection triggers and excessive foothold adjustment, a rapid disturbance detection method is established using the body acceleration derivative. Finally, the effectiveness of the proposed methods is validated under simulations and in experiments with an actual biped robot.

Nowadays single cell analysis becomes a more and more important method to gather the information of individual cells and study the heterogeneity of cells caused by random expression of gene, protein and the level of metabolism. Many device and technologies of single cell analysis have been developed to meet these needs. In this paper, we presented a high-throughput microchannel for single cell immobilization with small sheer pressure. It features high density arrays, which can accommodate up to 130~300 traps within 1~2 mm2. According to our experiment, about 91% of capture unit can be occupied by the single cell in 40 s, using the optimized structure of microchannels. Therefore, we expect that the high throughput microchannels can be of great importance for the biological research.

Microgrippers play a significant role in manipulation of micro-objects. To achieve dexterous and precise manipulation, a microgripper is required to be compactly designed and embedded with sensing feedback. Meanwhile, to convert the sensor position into displacement of the microgripper, the embedded sensors should be calibrated by additional equipment like laser sensor. However, a microgripper always needs to be calibrated during manipulation (online calibration), which is still a big challenge with current technology. In this paper, we proposed a highly compact microgripper integrated with position sensors, and a visual-based calibration method to handle such challenge. Moreover, to enhance grasping accuracy, flexure hinges are employed to achieve a low impedance grasping mechanism and to avoid the backlash in traditional bearing. Furthermore, kinematics analysis and Fine Element Analysis (FEA) are implemented to improve the design efficiency. Finally, fibrous micro-rings are successfully assembled, and the results reveal that the calibrated microgripper can be well employed to operate micro-objects.

Study of cellular behaviors, especially the ultra-rare cell type, can aid in the accuracy of clinic diagnoses as well as the development of bioresearch engineering, thus the importance of isolating them from heterogeneous mixtures. However, current methods may fail in purity, versatility or cause contamination to cell targets, which is fatal drawback to rare cells. To address this issue, we propose a versatile method to automatically select and capture fluorescent stained target cells with high purity and recovery rate, through developing a novel 3D image processing algorithm under dark field. With the automated pick-and-place strategies, the micro-robotic system achieves cell screening even in an environment with ultra-sparse cells. In the proposed visual method, Markov Random Field (MRF) separation is adapted into the fluorescent environment to attain real-time planar location of micropipette and target cells. A reformative method derived from Depth from Defocus (DFD) is brought up to acquire 3D information. The basic system for this method mainly consists of a camera mounted on motorized fluorescent microscope and a micromanipulator for cell capture. The fluorescent label help to screen out most of the undesired cells while also bring extra constraints and requisition to our visual method. Finally, experiments of collecting 3 T3 cells are performed to verify the feasibility and validity of the designed method, achieving average 98% purity and 80% recovery rate within the time limits. This study indicates that proposed visual processing method can not only provides reliable location feedback for micro-manipulation in rare cell sorting, but also can be easily extended to satisfy other automated micro-robotics manipulation.

In micromanipulation, lots of methods have been developed to manipulate objects in microscale. However, few of them can be applied in both the transportation and the rotation of the micro objects. In this paper, we present a novel method to realize the non-contact transportation and rotation of the micro objects based on the vibration-induced swirl flow. A piezo actuator is set between the glass needle and a metal rod. The sine wave with controlled frequency and amplitude is input into the piezo actuator to drive the glass needle to move circularly, which is caused by resonance of the actuator and the metal rod. We place the glass needle under water and keep a limited distance to the bottom. The circular vibration of the glass needle can generate a swirl flow and low pressure around it. The low pressure can trap and transport the micro objects vertically to the glass needle, and the swirl flow can rotate the objects continuously. Finally, we realize the trap and rotation of micro object with only one piezo actuator. Experiments of transportation and rotation of microbeads are carried out, and the results demonstrate it is a simple, low-cost, effective micromanipulation method.

This study presents the high-precision microinjection of fluorescent micro-gel beads into Caenorhabditis elegans trapped in a suction microchannel. In our previous works, we demonstrated survival microinjection by a conventional micromanipulation technique. However, the focal planes differed between the tip of the microinjection tool and the target axon inside the C. elegans body. To resolve this problem, we here propose a suction microchannel that traps C. elegans during the microinjection operation. The focal plane of the target nerve axon matches that of the fluorescent microbead in the microinjection tool, enabling high-precision microinjection into the interior of the C. elegans body under a microscopic view. In an experimental evaluation, the positioning accuracy of the injection into C. elegans was within the target accuracy (15 μm). The head-flrst navigation alignment of C. elegans along the microchannel was controlled by electrotaxis. Injection of the fluorescent micro-gel bead into the C. elegans body was quantitatively confirmed by confocal microscopy.

Helical structure assembled by hydrogel microfibers is significant for culture of smooth muscle cells. However, the helical structure is only fabricated at the macroscale, while the fabrication of helical microstructure is still a challenge due to the lack of assembly method. In this paper, we propose a robotics-based assembly method to handle such challenge. An electromagnetic needle (EMN) is employed as end-effector to magnetically reel the microfiber encapsulating magnetic nanoparticles around a micropillar, and a dual-ring structure is designed to keep the microfiber being attracted at the EMN tip. For enhancing the stability of tip attraction, the manipulation mode of anticlockwise pushing microfiber is established. Moreover, the interaction mechanism between EMN tip and microfiber is analyzed by developing a static force model, and then the key condition of stably reeling microfiber is concluded. Furthermore, a robotics-based motion trajectory of EMN tip is planned to achieve a smooth reeling process. Based on such planning, the size of dual-ring structure is further optimized to improve the success rate of reeling. Finally, the helical microstructure with there-turn coils is successfully fabricated.

The interaction test between a robotic rat and living rat is considered as a possible way to quantitatively characterize the rat sociality. In such robot-rat interactions, the robot should be designed to fully replicate a real rat in terms of morphological and behavioral characteristics. To address this problem, a multi-jointed robot prototype has been modified based on our previous work. We optimally update the forelimb of the robot and redesign the control board to make it more dexterous and increase its behavioral capability. Then, we systematically and kinematically analyze the rotational range of joint variables and the workspace of the robot by using traversal method. To evaluate the motion capability of the modified robot, we propose two quantitative parameters: maximum reachable height (MRH) and minimum bendable distance (MBD). Additionally, we achieve to quantitatively evaluate the behavioral similarity between the robot and rat with the calculated accumulative distance (AD) by using dynamic time warping (DTW). These evaluated methods show high promise to improve the robot-rat interaction to be more similar to rat-rat interaction.

This paper focuses on chaos control of a seven-link torque-stiffness-controlled dynamic walking model, actuated by a bio-inspired control system. The biped consists of compliant hip, knee and ankle joints and flat feet. We employed Ott-Grebogi-Yorke and delayed feedback control methods, responsible for small errors around the equilibrium solution and large errors far away, respectively. In simulation, we study the stabilization of bifurcations and chaotic behaviors under diverse actuation parameters, and the convergence speed to 1-period gaits. The results of this paper may provide insights into motion control of dynamic walking robots and principles of human locomotion.

This paper presents a novel In situ nanomanipulation integrated with scanning electron microscope- computed tomography (SEM-CT) imaging system for 3D nanomanipulation. In our previous works, a nanorobotic manipulation system was established inside an environmental-SEM (E-SEM) for water-contained samples, including biological organism, based on a real-time high resolution SEM observation. However, the SEM image is limited in two dimensional (2D) and surficial information from the signals of secondly electrons. For nanosurgery applications, such as nanoinjection, it is needed to evaluate the sample in 3D space with its internal information after manipulation. The SEM-CT imaging system is developed for In situ nanomanipulation based on SEM observation. The CT is an effective method to obtain the internal 3D information as a non-destructive manner. The imaging resolution of our SEM-CT system is in less than 400 nm. A Caenorhabditis elegans (C. elegans) was used as a target of biological sample. To improve the contrast of SEM-CT imaging of C. elegans, the X-ray was tested by generating using brass and copper materials. Finally, the nanoinjection was demonstrated with SEM-CT imaging system to C. elegans using the nanoinjector which was fabricated by focused ion beam (FIB) process.

It is of great importance to pick up a single carbon nanotube (CNT) from a bulk of CNTs for nanodevice fabrication. In this study, we have proposed a nanorobotic manipulation system allowing automated pick-up of CNTs based on visual feedback. We utilize histogram normalization for automatic binarization, and it achieves to clearly distinguish CNTs from substrate and other impurities under different image brightness. Furthermore, we develop the gradient orientation inversion (GOI) algorithm to recognize CNT tip and atomic force microscopy (AFM) cantilever. Taking full advantages of the geometrical characteristics of CNT and AFM cantilever, GOI is proved to be quite robust. We have designed segment detection method (SDM) to successfully separate the AFM cantilever and CNT, whereas the contact detection between them is achieved by analyzing the straightness variation. Preliminary experimental results imply that our method shows high promise in realistic fabrication of nanodevices.

Cats have protractible claws to fold their tips to keep them sharp. They protract claws while hunting and pawing on slippery surfaces. Protracted claws by tendons and muscles of toes can help cats anchoring themselves steady while their locomotion trends to slip and releasing the hold while they retract claws intentionally. This research proposes a kind of modularized self-adaptive toe mechanism inspired by cat claws to improve the extremities' contact performance for legged robot. The mechanism is constructed with four-bar linkage actuated by contact reaction force and retracted by applied spring tension. A feasible mechanical design based on several essential parameters is introduced and an integrated Sole-Toe prototype is built for experimental evaluation. Mechanical self-adaption and actual contact performance on specific surface have been evaluated respectively on a biped walking platform and a bench-top mechanical testing.

In this paper, we propose a new method to assemble microstructures with biological cells towards in vitro 3D cellular structures. The proposed assembly method uses self-assembly process of magnetized toroidal microstructures. Biocompatible toroidal hydrogel microstructures are prepared by electrodeposition method, and ferrite particles are put on the fabricated structures using poly-L-lysine (PLL). The microstructures are magnetized by the magnetic field at 3 T, and assembled by the magnetic self-assembly process. Biological cells were encapsulated in the microstructures and cultured to achieve high density of cells inside structures. The magnetized microstructures were assembled automatically. The magnetic force generated from the ferrite embedded microstructures was estimated and compared to the fluid resistance applied to the microstructures. The proposed method can be applied to achieve 3D in vitro cell structures with vascular networks for tissue engineering applications.

When a biped robot moves about in a physical environment, it may encounter inclined ground. Biped walking on inclined ground still remains challenging for biped robots. Previous studies have discussed biped walking on inclined ground along specific directions. However, omnidirectional walk on inclined ground has rarely been investigated. In this paper, we propose a gait pattern generation method for omnidirectional biped walking on inclined ground. First, a model that describes the motion of biped walking on inclined ground uniformly with two angle parameters is proposed. A mathematical relationship between motions in the sagittal and coronal planes of the biped robot are presented. Then, based on nonorthogonal motion decoupling, a method that generates gait patterns for omnidirectional walking with a double support phase for biped robots is proposed. The trajectories of each foot are designated by the walking speed, step length, and walking direction. The motion trajectory of the center of mass (CoM) of the robot is planned using a linear inverted pendulum model in the sagittal and coronal planes. The motion of CoM in the sagittal and coronal planes is constrained in parallel to the gradient vector of the inclined ground and the horizontal plane, respectively. Finally, the effectiveness of the proposed gait planning method for biped walking on is validated by simulations and experiments with an actual biped robot.

Due to the poor controllability in hydrogels, Hydrogels-based assembly to form larger 3D complex shapes is still a big challenge. In this paper, we have reported a novel “bottom-up” method to fabricate three-dimensional (3D) magnetic alginate microfibers (MAMs) assemblies with complex shapes. Specifically, Alginate microfibers encapsulating Fe3O4magnetic nanoparticles (MNs) and fibroblasts (NIH/3T3) have been spun using microfluidic method with “pinch-off” scheme. Experimental results show that the MAMs can respond quickly to the magnetic field, enabling their enhanced controllability. The magnetic assembly system is constructed by PDMS microfluidic device, 3D supporting model adhered on the bottom surface of dish filled with deionized water and magnet. The microfluidic “printing“ and magnetic deposition in magnetic assembly process are experimentally demonstrated, respectively. Because of magnetic field, the complex assembly shapes can be fabricated just by moving the microfluidic device in a plane. To match well with the shape of supporting model and to keep a stable assembly structure, the secondary cross-linking method is employed. From the LIVE/DEAD assay, cells can survive well during the magnetic assembly process.

In this paper, we conducted a new method to assemble microstructures inside a droplet named “Lab in a Droplet (LiD)”. The method can realize the assembly of micro-nano structures inside a droplet size. The surface tension is used to assemble microstructures automatically. Micro-scale or nano-scale objects are ejected from an inkjet nozzle. They are gathered at the center of the droplet and assembled by the surface tension. We conducted preliminary experiments to check whether the self-assembly of microstructures can be achieved by LiD. The experimental results show that the self-assembly of microbeads is conducted inside a droplet. The nano-scale objects can be patterned on a substrate depend on their sizes. A microstructure made of photo-crosslinkable resin was prepared, and microbeads were assembled with the microstructure inside a droplet. The method can be used for autonomous assembly of micro-nano structures in high precision.

In this study, we describe a novel method of fabricating shape-controlled calcium alginate gel microcapsules. Alginate-poly-L-lysine (PLL) hydrogel microcapsules with predefined shapes were constructed based on electrodeposition method. Firstly, electrolysis of water in alginate solutions with calcium carbonate particles induced alginate gelation on micro-patterned electrode to form 2D gel structures. Then, these structures will be detached from the electrode surface and treated with the alginate-PLL microcapsules system. By passive control based on the micro-pattern geometric confinement and electrodeposition parameters, we succeeded in producing calcium alginate-PLL microcapsules with diverse shapes (such as sphere rod and cubic). The shape and size of the calcium alginate microcapsules could be tuned by adjusting the geometric design of micro-pattern on electrode and the apply voltage of electrodepostion. The preparation conditions of size- and shape-controlled calcium alginate-PLL microcapsules and influence factors were studied. This proposed method can lead to more accurate and creative studies of fabricating biocompatibility scaffold for tissue engineering.

Fabrication of artificial blood vessels in micro scale significantly benefits the regeneration of functional human vascular networks. In this paper, we develop an efficient multi-microrobotic system with an innovative motorized sample holder (MSH) and two manipulators. Air is injected into the solution through a glass pipette fixed on one manipulator to create bubbles. These bubbles conduct a regular rising movement, which is utilized to assemble the 2D ring-shaped microgels fabricated in a simple micro fluidic device. With this novel bubble-based method and the robotic system, we achieve the automation of the assembly. A 1.2 mm long vascular-like microtube with an outer diameter of 200 μm is fabricated. The whole process of the bubble-based assembly is visually observed and analyzed with side view. Key parameters are characterized to improve the assembly. Results show that the automated bubble-based assembly success rate is 100% and average time cost of assembling every microgel is as low as 3.25s.

In this study, cell-laden alginate-poly-L-lysine (PLL) hydrogel structures with arbitrary shapes were constructed based on electrodeposition method. Electrolysis of water in alginate solutions with calcium carbonate particles induced alginate gelation on micro-patterned anode electrode, and cell-laden alginate gel structures were formed. The several different shapes of gel were generated at one time by predefined micro-patterns. The micro-patterned electrode was fabricated by coating photoresist on Fluorine-doped tin oxide (FTO) glass slide. Alginate-PLL encapsulation technique was introduced to this platform forming alginate-PLL hydrogel structures. This proposed method can lead to more accurate and creative studies of fabricating cell-laden scaffold for tissue engineering.

This study presents the survival microinjection into Caenorhabditis elegans (C. elegans) with in vivo observation based on micromanipulation. The microinjections were achieved with micro-gel beads which are enable to encapsulate chemicals for injection. In this study, a fluorescent material was used to evaluate the injection positional precision inside the C. elegans. The fluorescent microbead was picked up at the tip of a micropipette injection tool and injected by a piezo actuated microinjector. The distance between the injected micro-gel bead and closest nerve axon was measured as 20.3 μm and 16.5 μm by in vivo observation of a confocal microscopy. The two types of pipette tools were used to evaluate the success and survival rates of microinjection, and the smaller pipette (pipette A, 0.8 μm in diameter) showed higher rates as 50 % and 67 % respectively.

A cane-type robot called intelligent cane has been developed to support the elderly during walking. By supporting a part of a user's body weight, the cane robot aims to reduce a load applied to a user's affected leg. Therefore, while the user's affected leg is a support leg, it is preferable that the cane robot stops to sufficiently support the user. In our previous work, the cane robot is controlled based on horizontal component of force applied to the cane robot and moment around a vertical axis. In this paper, virtual friction force, which is proportional to vertical component of force, is proposed to improve a walking assistance capability of the cane robot. In addition, virtual frictional coefficients are arranged based on the user's state inferred by a laser range finder. By employing the proposed method, the cane robot moves easily in the both legs support phase, stops in the healthy leg support phase, and supports the user reliably in the affected leg support phase.

To enhance the safety of a humanoid robot when it is operating a complex environment, a number of methods that combine visual and force information have been presented. These methods are generally divided into two approaches. The first approach is to coordinate the visual controller and force controller in a parallel way, and the second approach is to coordinate them in series. However, these two approaches do not consider the interaction between the visual controller and force controller. Specifically, the first approach does not consider the interaction between the controllers. The second approach only considers the effect of the output of the visual controller on the force controller, while the effect of the force controller on the visual controller is not considered. This study presents a design for a new flexible controller for a humanoid robot that considers the interaction of visual and force information. The advantages of the proposed method are that it simultaneously incorporates the functions of a visual servo controller and a flexible controller as well as its ability to consider the interaction of visual and force information when a humanoid robot is operating.

In the past we achieved to use a rat-like robot and a single rat to develop Animal Models of Mental Disorder (AMMDs) through stress exposure. However, to simulate the real social environment, we use a rat-like robot composed of multiple links to chase a specific rat (target) in a group of rats (outside observers). In this paper, we aim to develop a real-time control system for a multi-link robot surrounded by multiple rats. A virtual impedance model was adopted to generate posture and trajectory for a multi-link robot named WR-5. With the analysis of virtual forces/moments acting on WR-5 based on the model, corresponding dynamic equations can be obtained to control the motion of WR-5. Simulation results show that the head of WR-5 can accurately direct a target object in the following test. After conducting experiments with three rat subjects (a target rat, two outside observers), the output results suggest that the head and body gesture of WR-5 achieves to follow the target in real-time. Meanwhile, the control system allows real-time avoidance of the moving outside observers during interaction.

3D assembly of cellular structures is important for the fabrication of biological substitute in tissue engineering. In this paper, a novel rail-guided multi-microrobotic system was proposed for the assembly of cellular structure. The cellular 2-dimensional (2D) module was fabricated by UV illumination of the crosslinkable hydrogel. The coordinated manipulation among the micromanipulators was performed with newly designed concentric movement along the rail, which realized the arbitrary change of micromanipulator posture. Through the rotation of the end-effectors around the specimen without swapping out the visual field, the manipulation flexibility was improved. The distance information between the micromanipulator and the module was acquired from vision feedback system and utilized for the automatic pick-up of the microstructure. Through the cooperation among multi-manipulators with hybrid motors, the micromanipulation to assemble the 3D structure with 30 nm operation resolution was achieved. Finally, the rail-guided DeSCom system realized the bottom-up fabrication of cellular vascular-like microtube with vision feedback.

Microfluidic devices provide efficient approaches for building cellular tubular structures for in vitro tissue models in tissue engineering. In this paper, we report a novel method of constructing three-dimensional (3D) multilayered tubular structures based on axis translation of two-dimensionally (2D) microstructures inside microfluidic devices. The on-chip fabrication of movable 2D microstructures embedding fibroblasts (NIH/3T3) based on Poly (ethylene glycol) Diacrylate (PEGDA) was reported. Novel two-layered microfluidic devices were fabricated by Polydimethylsiloxane (PDMS), for conducting the fluidic self-assembly of the 2D microstructures. The self-assembly process was experimentally demonstrated. For improving the assembly results, a funneled structure and 3 micro grooves were added inside the microfluidic channel. Improved self-assembly result of constructing a multilayered tubular microstructure with higher efficiency was demonstrated.

Cellular vascular-like microtubes occupy an important position in tissue engineering for building in vitro tissue models. In this paper, we report a method of constructing three-dimensional (3D) multilayered vascular-like microtubes based on fluidic axis-translation self-assembly of two-dimensional (2D) microstructures inside microfluidic devices. The on-chip fabrication of cell (fibroblasts NIH/3T3) embedded 2D microstructures based on Poly (ethylene glycol) Diacrylate (PEGDA) and biodegradable material Gelatin Methacrylate (GelMa) were reported. A multilayered Polydimethylsiloxane (PDMS) microfluidic device was fabricated for conducting the fluidic self-assembly of 2D microstructures. The fluidic axis-translation self-assembly process was experimentally demonstrated. Multiple hydrogels embedded microtube was constructed. The fabrication of GelMa microstructures was demonstrated. The degradability of cell embedded GelMa microstructures was evaluated by long-term observation, and it shows the great potential of GelMa to be used for constructing cellular vascular-like microtubes.

This paper introduces a novel catalytic mobile micro/nanorobot made only of platinum that realizes nanometer locomotion in hydrogen peroxide solution. The innovative mechanism and principle of the nanorobot are presented. A simple and effective fabrication process by focused ion beam and a stable manipulation method of the nanorobot are demonstrated. The nanorobot can steer or navigate by its finely designed geometry, and keep a stable rotational motion rather than arbitrary and uncertain movement. This paper evaluates the influence of some critical factors on the movement of the nanorobot, such as the concentration and temperature of the hydrogen peroxide solution, and the geometry of a nanorobot. The control of the nanorobot's movement can be realized based on the result of this evaluation. Compared with previous studies, this catalytic platinum nanorobot realizes bidirectional rather than unidirectional movement. The Langevin equation is used to describe the dynamic model of the platinum nanorobot.

For reproducing the manipulation of TCM remedial massage and meanwhile guaranteeing safety, a 4-DOF anthropomorphic BIT soft arm with integrated elastic joints is developed, and a passivity-based impedance control is used. Due to their series elasticity, the integrated joints may minimize large forces which occur during accidental impacts and, further, may offer more accurate and stable force control and a capacity for energy storage. Then, human expert's fingertip force curve in the process of massage therapy is acquired in vivo by a dedicated measurement device. Three massage techniques, pressing, kneading and plucking, are implemented by the soft arm, respectively, on torso model in vitro and on human body in vivo. Experimental results show that the developed robotic arm can effectively imitates the TCM remedial massage techniques.

An improved ZMP (Zero Moment Point) trajectory for a biped robot is designed in this paper, which imitates a human's actual ZMP trajectory in the walking process. A new method of walking pattern generation based on forward moving ZMP in SSP (Single Support Phase) is also provided. It can keep the ZMP moving forward instead of staying in the center of supporting region during SSP, which is helpful for increasing the walking speed. We have been developing BHR, which has 38 DOFs (degree of freedom). The effectiveness of the method is conducted by simulation and walking experiment on BHR.

Speed and precision are important for object detection algorithms. In this paper, a novel object detection algorithm based on color histogram and adaptive bandwidth mean shift is proposed. The algorithm is capable of detecting objects rapidly and precisely. It is composed of two stages: a rough detection stage and a precise detection stage. At the rough detection stage, histogram back projection and thresholding are applied to fast object identification and rough global localization. At the precise detection stage, the precise position, size and orientation are derived under the adaptive bandwidth mean shift framework. Experiments verify that the algorithm is able to detect the size, position and orientation of general objects rapidly and precisely.

Vision is a very important noncontact sensor for humanoid robots. In this paper, a method combining visual feedforward and visual feedback is proposed to implement reach-to-grasp task for a humanoid robot. Visual feedforward facilitates the reach-to-grasp task and reduces the manipulation time. Visual feedback increases the robustness by compensating the weak calibration error. The combination of two control strategies facilitates reach-to-grasp task for a humanoid robot. The robustness of the system is confirmed by the experiment results.

Most visual tracking systems use single visual cue and usually fail in a complex environment. In this paper, first, different visual cues are analyzed for a pan-tilt robot head tracking system. Then, a visual cues integration method combining disparity, color and shape is proposed. Two computers linked with Memolink communication module ensure the robot head to track a moving object rapidly. The high robustness and real time performance of the system are confirmed by the experiments

Walking pattern generation is a main issue for humanoid robot. We have already proposed a method for planning stable walking pattern. Based on this method, this paper mainly discusses generating stable and harmonious walking pattern by considering upper body motion, and planning hip trajectories in both sagittal plane and lateral plane. To reduce the iterative computation cost, some constraints of the relationship between sagittal hip motion and lateral hip motion are formulated, and only the trajectories satisfy these constraints are worked out. Finally, we determine the trajectory with a large stability margin from these generated trajectories. The effectiveness of the proposed method is confirmed by simulations and experiments with our developed humanoid robot BHR-02 with 32 DOF

The complexity of nonlinear differential equations of dynamics makes it practically impossible to obtain the walk pattern on-line through computing the whole dynamics. This paper proposed a method of online trajectory generation based on key parameters of off-line typical walk patterns for a biped humanoid. The key parameters include hip parameters, step length, walking cycle and so on. The walking pattern can be obtained according to these parameters. In order to generate walking patterns online, first the key parameters of the on-line walking pattern are computed based on the parameters of off-line typical patterns, then stability optimization has been done on-line and on-line trajectories are derived. The effectiveness of the proposed method is confirmed by simulations and experiments with our developed humanoid robot with 33 DOF.

Captured human data must be adapted for the humanoid because its kinematics and dynamics differ from those of the human actor. On the other hand, it is desirable that humanoid movements are highly similar to those of the human actor, since the human actor's motion is regarded as a teaching motion. This paper explores the design of a humanoid complicated dynamic motion based on human motion capture. First, the kinematic constraints, including ground contact conditions, are formulated. Next, the similarity evaluation and dynamic stability based on ZMP (zero moment point) of the humanoid motion are discussed, and the method to derive humanoid motion with a high similarity, and satisfying kinematic constraints and dynamic stability, is presented. Finally, the effectiveness of the proposed method is confirmed by simulations and experiments with the "sword" motion - a complicated and dynamic Chinese kungfu movement - using our developed humanoid robot with 32 degree of freedom.

Since a biped humanoid inherently suffers from instability and always risks tipping itself over, ensuring high stability and reliability of walk is one of the most important goals. This paper proposes a walk control consisting of a feedforward dynamic pattern and a feedback sensory reflex. The dynamic pattern is a rhythmic and periodic motion, which satisfies the constraints of dynamic stability and ground conditions, and is generated assuming that the models of the humanoid and the environment are known. The sensory reflex is a simple, but rapid motion programmed in respect to sensory information. The sensory reflex we propose in this paper consists of the zero moment point reflex, the landing-phase reflex, and the body-posture reflex. With the dynamic pattern and the sensory reflex, it is possible for the humanoid to walk rhythmically and to adapt itself to the environmental uncertainties. The effectiveness of our proposed method was confirmed by dynamic simulation and walk experiments on an actual 26-degree-of-freedom humanoid.

Although some researchers have studied the humanoid running based on simplified models, the issue of humanoid running based on the whole dynamics has not been sufficiently discussed before. The objective of this paper is to study the stability criterion and the dynamic pattern generation for humanoid running based on the whole dynamics. First, the cycle and the dynamics of running are analyzed. Next, the stability criterion of humanoid running is presented. Then, the method to plan a running pattern consisting of a foot trajectory and a hip trajectory is proposed. Finally, the effectiveness of the proposed method is illustrated by the dynamic simulation examples in DADS (Dynamic Analysis and Design System).

A novel system for 3D microchannel fabrication based on CO/sub 2/ laser-micromachining is presented. The system consists of a CO/sub 2/ laser focusing system and a 3D precision positioning platform. The CO/sub 2/ laser focusing system can regulate a laser beam which is of appropriate energy level and of micron dimension in beam diameter. The fabrication of 3D microchannel system in PMMA (polymethyl methacrylate) can be realized by controlling the CO/sub 2/ laser and a 3-axis platform with micron-resolution movement. A special 'sacrificial mask' process was used to produce translucent channels of micron dimensions with low surface roughness using the developed system. The effectiveness of our developed system is confirmed by experimental results. Potentially, our developed system can be automated to produce 3D micro channels in PMMA substrates without the requirement for the costly and time-consuming lithography and hot-embossing processes that are needed currently.

The captured data must be adapted for the humanoid because its kinematics and dynamic differ from those of the human actor. The kinematics constraints such as ground contact conditions are crucial for humanoid locomotion. Furthermore, it is desirable that the humanoid motion have of high similarity with those of the human actor. In this paper, first the similarity function of the humanoid motion is proposed. Then, the kinematics constrains including ground contact conditions are formulated, and the algorithm to derive the humanoid motion with a high similarity and satisfying kinematics constraints is present. Finally, the effectiveness is confirmed by the experiment of Chinese Kungfu "Taiji" using our developed 33 DOF humanoid robot.

This paper proposes a walk structure consisting of a dynamic pattern, a sensory reflex and a motion adjustment. The dynamic pattern is generated off-line based on the constraint of dynamic stability, assuming that the models of the humanoid and the environment are known. The sensory reflex is simple, but rapid motion programmed in respect to sensory information. The sensory reflex increases the humanoid adaptability to environmental uncertainties, but it may conflict with humanoid its constraints. To solve this problem, the walking constraints violated easily by the sensory reflex are formulated, and the method to coordinate the dynamic pattern and the sensory reflex through the motion adjustment is present. The effectiveness was confirmed by walk experiments of our developed 31 DOF humanoid.

Since a biped humanoid inherently suffers from instability and always risks tipping over, ensuring high stability and reliability of walking is one of the most important goals. The paper proposes a walk control consisting of a feedforward dynamic pattern and a feedback sensory reflex. The dynamic pattern is a rhythmic and periodic motion, which satisfies the constraints of dynamic stability and ground conditions, and is generated assuming that the models of the humanoid and the environment are known. The sensory reflex is a simple, but rapid motion programmed with respect to sensory information. The sensory reflex, we propose, consists of the body posture control, the actual ZMP (zero moment point) control, and the landing time control. With the dynamic pattern and the sensory reflex, it is possible for the humanoid to walk rhythmically and to adapt itself to environmental uncertainties. The effectiveness of our proposed method was confirmed by walk experiments of an actual 26 DOF humanoid on an unknown rough terrain and in the presence of disturbances.

The reliability of stable walk and the development of high performance components are two crucial issues to develop a humanoid with human-like physical capability. In order to for the humanoid to walk smoothly and to adapt to unknown environments, we first propose a balance control that combines a feedforward dynamic pattern and a feedback sensory reflection. Then, we present a method for clarifying the relationship between the physical capability and actuator's specifications. Using this method, it is possible to predict the walking speed based on known actuator specifications and to obtain the necessary specifications to accomplish a desired walking speed. Finally, experiments of an 26-DOF humanoid and simulation examples are provided to illustrate the effectiveness of the proposed method.

Biped robots have better mobility than conventional wheeled robots, but they tend to tip over easily. To be able to walk stably in various environments, such as on rough terrain, up and down slopes, or in regions containing obstacles, it is necessary for the robot to adapt to the ground conditions with a foot motion, and maintain its stability with a torso motion. When the ground conditions and stability constraint are satisfied, it is desirable to select a walking pattern that requires small torque and velocity of the joint actuators. We first formulate the constraints of the foot motion parameters. By varying the values of the constraint parameters, we can produce different types of foot motion to adapt to ground conditions. We then propose a method for formulating the problem of the smooth hip motion with the largest stability margin using only two parameters, and derive the hip trajectory by iterative computation. Finally, the correlation between the actuator specifications and the walking patterns is described through simulation studies, and the effectiveness of the proposed methods is confirmed by simulation examples and experimental results.

Since a biped robot tends to tip over easily, stable and reliable biped walking is a very important achievement. In this paper, we propose a balance control method based on an off-line planned walking pattern with real-time modification. First, a method of generating a highly stable, smooth walking pattern is presented. Then, a method of real-time modification consisting of body posture control, actual zero moment point control and landing time control based sensor information is proposed. By combining the proposed off-line walking pattern with real-time modification, the biped robot can walk smoothly and adapt to unknown environments. The effectiveness of the proposed method is confirmed by dynamic simulator such as walking on unexpected irregular rough terrain, soft ground and in environments in the presence of disturbances.

Since a biped humanoid inherently suffers from instability and always risks to tipping over, stable and reliable biped walking is the most important goal. The simulator is a significant tool to pursue this goal. In this paper, we first present a method for constructing a humanoid simulator that can closely model and predict the motion of an actual humanoid. We then propose a balance controller consisting of an off-line walk-pattern generator and a real-time modification. Using the simulator, we can predict the humanoid's physical capability subject to the constraints of actuators, and clarify the required specifications of actuators to execute a desired task. The functions of the developed simulator and the effectiveness of the proposed balance controller were evaluated through simulated walks on an unknown rough terrain, soft ground, and an environment in the presence of disturbances.

Biped robots have better mobility than conventional wheeled robots, but they tip over easily. In order to walk stably in various environments such as rough terrain, up and down slopes, or regions containing obstacles, it is desirable to adapt to such ground conditions with a suitable foot motion, and maintain the stability of the robot by a smooth hip motion. We propose a method to plan a walking pattern consisting of a foot trajectory and a hip trajectory. First, we formulate the constraints of a foot trajectory, and generate the foot trajectory by 3rd order spline interpolation. By setting the values of constraint parameters, it is easy to produce different types of foot motion. Then, we formulate a hip trajectory using a 3rd order periodic spline function, and derive the hip trajectory with high stability. Finally, the effectiveness of the proposed method is illustrated by simulation examples.

Since most conventional robots cannot easily be adapted to environments designed for humans, a human-size biped robot is expected to be able to play an important role in assisting human activities. The selection of suitable joint actuators is an important point when developing a human-size biped robot. In order to select suitable actuators and effectively utilize the selected actuators, it is necessary to clarify the relationship between walking patterns and the specifications of each joint actuator, and this is the issue tackled in the paper. First, a method of generating a high stability, smooth walking pattern is presented, and it is shown how various walking patterns can be produced by setting a series of defined walking parameters. Then, the dynamics of the robot, including the reaction force between the feet and the ground, are formulated. Finally, by simulation studies, the correlation found between actuator specifications and walking patterns is described, and the effectiveness of the proposed method is suggested.

In order for a mobile manipulator to be used in areas such as offices and houses, the mobile platform must be small-sized. In the case of a small-sized platform, the mobile manipulator may fall down when moving at high speed, or executing tasks in the presence of disturbances. Therefore, it is necessary to consider both stabilization and manipulation simultaneously while coordinating vehicle motion and manipulator motion. In this paper, we propose a method for coordinating vehicle motion planning considering manipulator task constraints, and manipulator motion planning considering platform stability. Specifically, first, the optimal problem of vehicle motion is formulated, considering vehicle dynamics, manipulator workspace and system stability. Next, the manipulator motion is derived, considering stability compensation and manipulator configuration. Finally, the effectiveness of this method is demonstrated by simulation.

We have proposed the collision-tolerant mobile manipulator equipped with a passive trunk (i.e., supporting part). In collision experiments with unknown environments, results show that this mechanism is suitable for the suppression of contact forces which is an important issue for human-robot collaborations. The trunk with mechanical elements such as springs and dampers is passively deformed to deal with physical contacts, while the mobile platform moves around on the horizontal plane in response to friction between the platform and the ground. The end-effector of the manipulator, however, is difficult to track a desired task due to the deformation of the compliant trunk and the mobility of the platform. In order to solve the problem, the desired joint configurations of the manipulator are directly calculated according to the movement of the trunk and the platform, and a feedback control scheme is employed.

In order for a mobile manipulator to move stably (not overturn) and execute the given motions of the end-effect and the vehicle simultaneously, a manipulator must have redundancy. By using this redundancy, it is possible to perform task at an optimal manipulation configuration when the robot is stable, and recovering the system's stability when the robot is unstable. The ability to recover stability by this manipulator compensation motion is limited. Thus in order to ensure the feasibility of stability compensation, the task plan or vehicle motion must be within this ability. In this paper, first the concept of stability compensation range by static posture change is proposed. Then, within the stability compensation range, the compensation motion of a redundant manipulator considering the manipulation configuration and the system stability is derived, given the motions of the end-effector and the vehicle. Finally, the effectiveness of this method is illustrated by simulation experiments.

It is desired that a vehicle-mounted mobile manipulator can move with stability and can operate tasks in various environments in the presence of disturbances. In this paper, a mobile manipulator cooperative motion planning algorithm is proposed, consisting of a rough motion planning and a local motion modification. As a step to realize the cooperative motion, the vehicle motion planning is discussed, given the end-effector trajectory. First, the vehicle path is planned. Then the optimal problem of determining the passing time of the vehicle along the planned path is formulated, considering the vehicle acceleration, the manipulator workspace and the system stability. Using a gradient projection method, the vehicle motion is derived. Finally, the effectiveness of this method is illustrated by simulation.

The stability of a vehicle-mounted mobile manipulator has a close relation with the vehicle's motion, the manipulator's posture and motion and the end-effector's force. The purpose of this study is to derive the cooperative motions of the vehicle and the manipulator for a stabilization which is compatible with task operation so that the mobile manipulator can successfully accomplish tasks in environments with various disturbances. The authors have already proposed the stability concepts based on the ZMP criterion to discuss the stabilization and the task operation, and have presented the method of ZMP moved path by a stability potential field to maintain the stability for a mobile manipulator. In this paper, based on the above-mentioned considerations, the manipulator compensatory motion is discussed for stabilizing the mobile manipulator while the vehicle is moving along a given motion.

Many future applications of robotic systems will require that manipulators perform operations while being carried by moving vehicles. However, such a vehicle mounted mobile manipulator can be unstable or even tip over. Previous work on stability control hardly considered the dynamics and environmental disturbances. The stability of a mobile manipulator has a close relation with the vehicle motion, manipulator motion and posture, and end-effector force. To evaluate the stability for a mobile manipulator, the concepts about stability, such as the stability degree and the valid stable region based on the zero moment point (ZMP) criterion have already been proposed. In this paper, as a control scheme for maintaining or recovering stability, the method of ZMP path planning by a stability potential field is presented, in which the concepts of the goal state and prohibitive state of stability are outlined. A motion planning algorithm is then formulated, which controls the manipulator in order to maintain the stability of the whole system while the vehicle is moving along a given trajectory.>

Many future applications of robotic systems will require manipulators to operate from moving vehicles. However, vehicle-mounted mobile manipulators might be unstable and even tip over. The authors assume that the stability of such a mobile manipulator has a close relationship with the vehicle's motion, the manipulator's posture and motion, and the endpoint's force. They present the concepts of the stability degree and the valid stable region based on the ZMP zero moment point criterion, which can be used as effective stability criteria in controlling mobile manipulators. Finally, the concepts are illustrated by computer simulation.