The theoretical capability of modular robots to recover their original configuration or function after disintegration caused by external impacts has been cited as an advantage for planetary exploration. The key to achieving self-recovery lies in addressing the stochasticity of disintegration. Here, a self-recovery strategy is proposed for modular planetary exploration robots. Firstly, the recovery process is analyzed to construct a strategy framework and provide the problem definitions and strategy assumptions. Secondly, by standardizing the selection criteria for meta-modules under the stochasticity of disintegration, non-mobile modules can acquire mobility through the meta-module method, thereby laying the groundwork for executable self-recovery. Finally, a comprehensive optimization model is proposed, which encompasses the module interactions arising from stochastic disintegration. By integrating selfrecovery characteristics with the simulated annealing algorithm, a solution method is designed to obtain self-recovery plans. Extensive hardware experiments were conducted, and the results demonstrate that the self-recovery strategy operates stably and executes successfully across various configurations and scenarios, thereby validating the feasibility and reliability. In this way, the self-recovery strategy and experiments could substantially advance the application of modular robots in space exploration, while also providing insights for other areas, such as assembly planning and applications involving non-mobile modular robots.

Trajectory planning plays a pivotal role in robotic motion planning, particularly in achieving time-optimal motion under complex dynamic constraints. Although the Time-Optimal Path Parameterization (TOPP) algorithm effectively addresses trajectory generation under joint torque constraints, classical methods often overlook third-order constraints. As a result, the generated trajectories, while torque-feasible, exhibit excessive jerk and poor dynamic stability, which limits their practical applicability. To overcome these limitations, this paper proposes a trajectory planning framework that simultaneously enforces torque and jerk constraints. Building upon torque-constrained TOPP, the method integrates a shooting-based strategy to identify switching points through bidirectional integration under jerk constraints and employs a Sigmoid-based fusion scheme to eliminate integration errors and ensure smooth transitions. The proposed approach is experimentally validated on a six-degree-of-freedom industrial robot. Comparative evaluations with the TOPP-RA algorithm demonstrate that the method significantly reduces both high-frequency vibrations during high-speed execution and residual oscillations after motion termination. Feedback from torque rate measurements, vibration sensors, and laser tracker data confirms faster settling and improved compliance, making the approach well-suited for complex industrial scenarios.

Indoor semantic segmentation is fundamental to computer vision and robotics, supporting applications such as autonomous navigation, augmented reality, and smart environments. Although RGB-D fusion leverages complementary appearance and geometric cues, existing methods often depend on computationally intensive cross-attention mechanisms and insufficiently model intra- and inter-modal feature relationships, resulting in imprecise feature alignment and limited discriminative representation. To address these challenges, we propose DiffPixelFormer, a differential pixel-aware Transformer for RGB-D indoor scene segmentation that simultaneously enhances intra-modal representations and models inter-modal interactions. At its core, the Intra-Inter Modal Interaction Block (IIMIB) captures intra-modal long-range dependencies via self-attention and models inter-modal interactions with the Differential–Shared Inter-Modal (DSIM) module to disentangle modality-specific and shared cues, enabling fine-grained, pixel-level cross-modal alignment. Furthermore, a dynamic fusion strategy balances modality contributions and fully exploits RGB-D information according to scene characteristics. Extensive experiments on the SUN RGB-D and NYUDv2 benchmarks demonstrate that DiffPixelFormer-L achieves mIoU scores of 54.28% and 59.95%, outperforming DFormer-L by 1.78% and 2.75%, respectively. Moreover, its effectiveness is further validated on the large-scale ScanNetv2 dataset, indicating strong generalization capability. Code is available at https://github.com/gongyan1/DiffPixelFormer.

This article proposes a unified task trajectory planner (UTTP) that integrates motion observation, trajectory planning and obstacle avoidance function, which is a key technology for dynamic target operations. The high-order motion observer (HOMO) for capturing target motion information, and the virtual impedance model for noise reduction and obstacle avoidance are two cores of UTTP. First, in HOMO, an exploratory model and optimal policy iteration method are established based on dynamics principles. Guided by the optimal policy, the exploratory model simulates the target's dynamic behavior and outputs pose, velocity, and acceleration in real-time. Second, a controller is designed based on the high-order motion information observed by HOMO and the positions of obstacles, driving the virtual impedance model to produce safe and smooth task trajectories. The introduction of UTTP can effectively enhance the robot's autonomous decision-making capabilities in dynamic environments. Finally, simulation analyses and experiments on a hyperredundant humanoid upper-limb robot support the effectiveness and superiority of the proposed algorithm in terms of accuracy and smoothness.

Traditional pedestrian detection methods based on red-green-blue (RGB) images struggle in adverse illumination, but a key capability required for pedestrian detection is all-day detection due to its critical role in diverse applications, e.g., security, surveillance, and autonomous driving. To address this issue, multispectral pedestrian detection attempts to introduce thermal images to supplement the RGB images, since they can be captured based on heat radiation difference without relying on external light sources. However, how to fuse the two modalities effectively is still lacking in-depth investigation. To prompt this field, we propose an implicit illumination-aware representation to address the limited availability of specific illumination labels in existing multispectral datasets, coupled with a prefusion feature alignment strategy to reconcile spatial misalignments of identical objects across modalities. We also identify four critical fusion challenges, revealing persistent limitations in existing multispectral detectors' ability to holistically address these issues, particularly regarding underdeveloped cross-modal interactions and suboptimal cross-domain feature fusion. To this end, we propose a universal multispectral pedestrian detection paradigm (UMPDP), which includes a modality alignment module (MAM) for adaptive feature space alignment, a differential modality fusion module (DMFM) to enhance the relationship of different modalities, and a task-conditioned illumination module (TCIM) to dynamically adjust network weights based on illumination condition. Extensive experiments on KAIST and CVC-14 datasets demonstrate the general effectiveness of our proposed method. Code is available at https://github.com/gongyan1/UMPDP.

Exoskeleton technology holds significant promise within the human-centric paradigm of Industry 5.0 for mitigating work-related musculoskeletal disorders (WMSDs). However, existing systems often struggle with mismatched assistive torque and inefficient human-machine collaboration under dynamic loading conditions, largely due to insufficient motion intent recognition accuracy. This study proposes a dual-model-based multimodal fusion control strategy that integrates a bidirectional LSTM neural network (Bi-LSTM) with a transformer-based multi-task learning model (MTL) to enable real-time torque compensation and accurate prediction of dynamic load mass under varying conditions. The team developed a lightweight elbow joint exoskeleton prototype, leveraging multi-modal information to enhance assistive torque prediction accuracy. Experimental results show an 83.7% reduction in agonist muscle activation under a 3.5 kg load compared to conditions without the exoskeleton, underscoring its potential for industrial material handling scenarios.

In recent years, significant progress has been made in the prototype design and control methodologies of modular snake robots. However, there is still relatively little research on the potential enabled by the active morphological transformation of robots. This paper presents a novel modular snake robot capable of morphing into a bipedal configuration. The robot, ZBOT, is composed of some independent and homogeneous unit modules (named ZBot) connected in series. Each ZBot module has a dual-motor-driven 1-DoF rotational joint, which can rotate continuously, provide a large output torque and achieve backlash elimination. There are four connection orientations between adjacent modules. This paper proposes an articulation configuration, which enables the snake robot to achieve the active transformation from a snake form to a bipedal form. Meanwhile, through reinforcement learning (RL), movements including the stand-up gait are trained and verified in the IsaacSim/Lab simulation environment. This research will advance snake robots beyond surface-dependent locomotion, endowing them with more possibilities, unlocking greater potential for versatile applications.

Existing polar robots are constrained by limited energy supply, making it difficult to carry out long-term scientific exploration missions, which highlights an urgent demand for energy conservation. An energy-efficient multi-mode motion polar robot is proposed to address this challenge. Both increasing external assistance and reducing the driving force are critical for lowering energy consumption. A foldable sail is designed to provide external assistance. When unfolded, the sail generates assistive force. When folded, it maintains stability in extreme polar climates. The sail shape is designed based on a symmetrically extended NACA0018 airfoil, and the influence of different sail parameters on performance is discussed. The transformable tracks realize switching between traction and sliding modes through the separation of the track and teeth chain, using the sliding mode to reduce driving force. The effect of teeth parameter variations on traction performance is analyzed. The system kinematics and dynamics are model, and stability conditions are determined. Based on this, an energy-saving motion control framework for multi-mode motion is proposed. Finally, experiments are conducted to evaluate the energy-saving contribution of each independent mode under different configurations. Comprehensive experiments in multi-mode motion demonstrate an overall energy-saving rate of approximately 24%, verifying the effectiveness of the energy-saving motion control strategy. With its energy-saving advantages, this robot shows strong potential for enabling long-term scientific exploration in polar regions.

Preference-based optimizing methods have shown their advantages and potential in exploring individual, comfortable, and effective control strategies and assistance parameters of exoskeletons during locomotion. Research indicates that compared with naive wearers, knowledgeable wearers with abundant exoskeleton assistance experience have obvious advantages in speeding up the parameters exploration process and improving the assistant performance. However, there is no existing method that could utilize the human-exoskeleton locomotion interaction experience (HELIE) to assist naive wearers during the exploration process. In this work, we propose a novel preference-based human-exoskeleton locomotion interaction experience transfer (LIET) framework, which could speed up the exploration of human-preferred parameters and acquire more satisfying results for naive wearers via the HELIE acquired from knowledgeable wearers. In addition, based on the proposed LIET framework, we establish the mathematical expression of the HELIE transfer during exoskeleton assistance. This will promote the research that concerns utilizing HELIE for exoskeleton control parameters optimizations in the future. Finally, experiments demonstrate the proposed LIET framework could speed up the exploration process and acquire more satisfying optimized results for naive wearers.

Research has indicated that exoskeletons can assist human movement, but due to the influence of additional weight and challenges in control strategy design, only a few exoskeletons effectively reduce the wearers’ metabolic costs during running. This paper proposes an innovative and efficient hip-assisted running exoskeleton (HARE) designed to facilitate the flexion and extension movements of the joint along the sagittal plane. In the field of structural engineering, we propose implementing an active-passive combination constant force suspension system, hereinafter referred to as CFS, to effectively mitigate the impact of inertial forces during running. The decoupled transmission mechanism allows the CFS and assist mechanisms to operate independently, ensuring the tension of the cables. The flexible structural design can reduce the locomotion limitation on human bodies and reduce the additional energy burden on the body. In control strategy designing, the joint torque-generating strategy provides personalized assistance strategies for wearers to actively optimize the control parameters. Meanwhile, the safety control strategy based on abnormal gait recognition can ensure human safety. Experiments have shown that compared to not wearing exoskeletons, this device can reduce the energy consumption of the human body by 5.33 % at a speed of 9 km/h. This demonstrates its potential in human motion assistance processes.

Inspired by visual-tactile cross-modal bidirectional mapping of the human brain, this paper introduces a novel approach to bidirectional mapping between visual and tactile data, an area not fully explored in the predominantly unidirectional existing studies. First, we adopt separate Variational AutoEncoder (VAE) models for visual and tactile data. Furthermore, we introduce a conditional flow model built on the VAE latent feature space, enabling cross-modal bidirectional mapping between visual and tactile data using one model. The experimental results show that our method achieves excellent performance in terms of the similarity between the generated data and the original data (Structural Similarity Index (SSIM) of visual data: 0.58, SSIM of tactile data: 0.80), the classification accuracy on generated data (visual data: 91.60%, tactile data: 88.05%), and the zero-shot classification accuracy between generated data and language (visual data: 44.49%, tactile data: 45.03%). To the best of our knowledge, the method proposed in this paper is the first one to utilize a single model to achieve bidirectional mapping between visual and tactile data. Our model and code will be made public after the acceptance of the paper.

In ophthalmic surgery, realigning retinal field is a common practice to check the lesion area by rotating the eyeball. It can be a challenging task for teleoperated surgical robots as it requests high precision of hands coordination comparing with traditional surgery. Current ophthalmic surgical robots are mainly designed with one-to-one mapping mode, which requires surgeons to operate with two hands. This article proposes a single master and dual-slave control method to improve the coordination of slave robots. Firstly, motion constraints between slave arms are analyzed. To solve the motion equation of master-slave mapping, Gauss-Newton Iteration is implemented to achieve the real-time motion coordination. Secondly, an adaptive impedance control is adopted to ensure the contact force between end-effectors and sclera within the threshold, preventing the eyeball from stretching and compressing. Finally, the effectiveness and practicability of the proposed method are verified on the established ophthalmic surgical robot platform by fixed trajectory and free trajectory experiments.Note to Practitioners—This paper was motivated by the problem of dual-arm control in robot-assisted retinal surgery. Related previous studies mainly focus on the control of teleoperated surgical robots based on one-to-one mapping mode between the master and slave robots. However, to the limited coordination of human hands, the master robots may perform poor coordination so that one task is hard to be accomplished by salve robots at same time. Especially when the requirement of task is complex and operation is precise, the accuracy and safety may not be guaranteed by one-to-one mapping mode. To solve these problems, this article seeks to develop a single master and dual-slave control method to improve the coordination of slave robots. The proposed method can be integrated into eye surgical robot to help the surgeons perform surgery. Our method will significantly facilitate its practical applications in improvement of surgery safety and reduction of the operation burden and training cost of the surgeons.

This article applies high-order differential estimation to the motion planning of humanoid robots for the first time. A multiobjective optimization model and the corresponding optimal policy are designed from the perspective of solving time-varying linear equations. This method can avoid the calculation of the Jacobian matrix pseudo-inverse and its derivative, reduce energy consumption, and achieve smooth human-like robot motions. High-order differential estimation is realized by cascading multiple integration-enhanced differentiators, which estimate the first derivative based on hybrid error and quasi-sliding mode techniques. The merits of the differentiator include high accuracy in estimating high-order derivatives and the elimination of chattering. Theoretical analyses verify that the proposed differentiator and the differentiator-based solver have asymptotic convergence. Simulations prove that the integration-enhanced differentiator and the differentiator-based method have excellent performance. Experiments illustrate that the designed solver for the motion planning of a humanoid upper-body robot can track desired trajectories and perform carrying tasks.

The field of soft manipulators requires a more promising solution, including efficient structures and controllers. This article presents a novel cable–pneumatic hybrid-driven tapered soft manipulator (TSM) design and control scheme to enhance the performance in actual tasks. This article is the first to present the design with a Bowden tube as a driving tendon and propose a composite tendon with Bowden tubes and cable tendons (BTCTs). Leveraging the principles of hybrid-driven antagonism, the compact TSM integrates the composite tendon with BTCTs and pneumatically actuated tapered bellows. This new hybrid-driven form provides the TSM with excellent resistance to axial extension, tangential bending, and torsion, enhancing the stiffness of the TSM. The variable-stiffness range of the TSM was quantified in tests, including axial stiffness (0.57–10.77 N/mm), tangential bending stiffness (0.01–0.45 N/mm), and torsion stiffness (0.02–0.044 N$\cdot$m/$^\circ$) tests. A deep learning-based neural network approach was utilized to model the inverse kinematics of the TSM. For more precise motion control, using position and orientation feedback from the sensor at the tip, we have designed a closed-loop iterative feedback controller incorporating three algorithms. Experiments on spatial point positioning, trajectory tracking with different constraints, orientation control, and disturbance experiments were conducted on the TSM. Experimental results [spatial point positioning error (mean error of stable region: 0.17 mm), circular trajectory tracking error (mean and standard deviation (SD) of 100 trials: 0.87$\pm$0.57 mm), orientation control error (less than 1$^{\circ }$), and the performance in disturbance experiment] demonstrated that our approach has high control accuracy and strong robustness against external disturbances. We conducted experiments involving teleoperation control, collision-free precise operations in cluttered and constrained environments, and disturbance-adaptive board cleaning testing, ensuring both stability and safety during contact with humans. These experiments intuitively demonstrate the potential of this TSM for executing complex tasks in real-world environments, promising to become a safe collaborative assistant for humans in the future.

Humans use tools to complete impact-aware tasks such as hammering a nail or playing tennis. The postures adopted to use these tools can significantly influence the performance of these tasks, where the force or velocity of the hand holding a tool plays a crucial role. The underlying motion planning challenge consists of grabbing the tool in preparation for the use of this tool with an optimal body posture. Directional manipulability describes the dexterity of force and velocity in a joint configuration along a specific direction. In order to take directional manipulability and tool affordances into account, we apply an optimal control method combining iterative linear quadratic regulator (iLQR) with the alternating direction method of multipliers (ADMM). Our approach considers the notion of tool affordances to solve motion planning problems, by introducing a cost based on directional velocity manipulability. The proposed approach is applied to impact tasks in simulation and on a real 7-axis robot, specifically in a nail-hammering task with the assistance of a pilot hole. Our comparison study demonstrates the importance of maximizing directional manipulability in impact-aware tasks.

Water jumping motion extends the robot's movement space and flexibility. However, the jumping performance is influenced by multiple factors such as driving force, rowing trajectory, and robot structure. The interaction force between the robot and water surface is complicated due to water deformation, and the difficulty of the water jumping increases with the robot's scale. This article designs a miniature water jumping robot with rowing driving legs. The hydrodynamic model between driving legs and water is established based on the modified Wagner theory with consideration of water surface deformation. Particularly, the dynamic model of the robot for the whole jumping process is also developed related to multiple factors. Then, the jumping performance is improved by optimizing the energy storage modality, rowing trajectory, and supporting leg shapes through the theoretical analysis and experiments. The fabricated robot weights 91 g, and its length, width, and height are 220, 410, and 95 mm, respectively. The maximum water jumping height and distance are 241 and 965 mm.

Existing in-pipe robots have insufficient adaptability when dealing with accidents in unfamiliar pipe environments. Developing a pipe robot that can be designed and manufactured quickly is one solution. The tensegrity structure is a self-stressing spatial structure formed by the interaction of rigid members and flexible cables, which has the advantages of simple structure, good flexibility, deformability, and impact resistance. Inspired by this structure, we design a novel worm-like tensegrity robot for different pipe environments, which can be manufactured rapidly at low cost. Firstly, a robotic module based on the tensegrity structure is designed inspired by the motion patterns of worm-like organisms. Then, the design process of the module is presented based on the mathematical analysis of the deformation. Finally, a prototype of the tensegrity robot is developed using simple and low-cost parts in less than an hour. To test the motion performance, load performance, and inspection capability of the tensegrity robot, we designed a series of experiments on horizontal pipes, vertical pipes, elbows, and steel pipes. Experimental results show that the worm-like tensegrity robot is simple in structure, easy to manufacture, low in cost, and good in performance.

Daily manipulation tasks are characterized by geometric primitives related to actions and object shapes. Such geometric descriptors are poorly represented by only using Cartesian coordinate systems. In this paper, we propose a learning approach to extract the optimal representation from a dictionary of coordinate systems to encode an observed movement/behavior. This is achieved by using an extension of Gaussian distributions on Riemannian manifolds, which is used to analyse a set of user demonstrations statistically, by considering multiple geometries as candidate representations of the task. We formulate the reproduction problem as a general optimal control problem based on an iterative linear quadratic regulator (iLQR), where the Gaussian distribution in the extracted coordinate systems are used to define the cost function. We apply our approach to object grasping and box opening tasks in simulation and on a 7-axis Franka Emika robot. The results show that the robot can exploit several geometries to execute the manipulation task and generalize it to new situations, by maintaining the invariant characteristics of the task in the coordinate system(s) of interest.

This paper presents a new variable stiffness actuator based on a second-order lever mechanism which has wide stiffness regulation range. By employing a novel symmetric structure design and improving the load capacity of the stiffness regulation module, the proposed actuator also shows well performance in load capacity, stiffness regulation response, and elastic hysteresis. On this basis, a variable stiffness actuated manipulator is developed. The experimental results demonstrate that the presented manipulator possesses abilities in fast stiffness tracking, shock-absorbing and explosive movement. It is also verified that the manipulator can withstand accidental impact, which illustrates the structure stability of the proposed design.

This paper presents a real-time kinematic control strategy to realize fast tracking of redundant robot manipulators in a time-varying environment. An obstacle avoidance method based on the law of conservation of energy is proposed to adjust the motion states of robot manipulators in real time. This method defines that the total energy for the end effector consists of an energy toward object (ETO) and an energy around obstacle (EAO), and that the total energy for each critical point on manipulator composes a relative kinematic energy (RKE) and an energy memory (EM). The total energies remain constant at each sampling period, and the conversions between the ETO and the EAO or between the RKE and the EM are recognized to obey a distance-related S-function. Such considerations ensure the smooth movement of the manipulator and avoid collisions with obstacles. In real-time planning, an unsupervised single neuron PID model is raised to adaptively increase the convergence ratio of moving object tracking via the online learning of the principal component analysis. Then, combined with the dynamic obstacle avoidance method based on conservation of energy, the kinematic control strategy is established for redundant manipulators to track a moving object rapidly in the presence of multiple dynamic obstacles. Theory analysis and various contrast experimental results show that the proposed kinematic control strategy is feasible and has fast convergence.

The amazing ability of water striders on water surface has attracted many scholars. Especially the flexible driving mechanism enable the driving legs conform to the deformation of the water surface, which effectively improving water striders’ floating ability and stability. However, the current research on water striders has never designed a flexible driven robot prototype like water striders. This paper proposes a new water strider robot that can walk on water surface based on flexible driving mechanism. The robot’s driving legs are designed with flexible materials and possess ellipse-like spatial trajectories like water striders through a limit pin-linkage mechanism. Based on microelement cantilever method, the flexible driving effect was analyzed with different elastic modulus and diameter. It shows that the flexible legs can row at a higher frequency before puncturing the water surface and achieve bigger work in one period compared with the rigid one. At last, the skating experiment of the robot under different stiffness and rowing frequency was carried out. The results verified that the limit frequency of the flexible driving legs and maximum moving speed of the robot are about 41.3% and 36.2% higher than those with rigid legs, respectively. Moreover, a similarity analysis of hydrodynamic characteristic constants reveals that the locomotion of the flexible driving robot is more analogous to the biological water striders than the rigid one.

Pathological tremor is defined as a roughly sinusoidal movement and usually occurs in the upper limb impacting individuals activities of daily livings. Functional electrical stimulation (FES) is proposed as a potential alternative for cancelling the pathological tremor. However, the feasibility and accuracy of FES depends on the estimation of amplitude and frequency of tremor signals measured by sensors. In this study, a novel algorithm incorporating a sliding fast Fourier transform (SFFT), an interpolation procedure and a limitation module of frequency range is developed to estimate tremor frequency and separate the tremor components from raw data. Based on the artificial signals and the actual tremor signals, the performance of the proposed algorithm is evaluated. The experimental results indicate that the developed algorithm could quickly adapt to the unknown dominant frequency and extract the tremor components with high accuracy. Therefore, this method could be employed in the tremor suppression by FES without affecting the voluntary movement.

Aiming at mimicking the jumping locomotion of water striders, a new continuous jumping robot on water is proposed. Compared with the horizontal rowing motion, the jumping capability of water striders is challengeable to imitate, since the impact force on water is easy to cause the sinking of the robot. In this paper, a jumping mechanism based on springs is designed to produce a large thrust for the robot to jump. The shape of supporting legs and center of gravity of the robot are carefully designed so that the robot can jump on the surface continuously and smoothly. Influences of several critical factors, including the area of supporting legs, spring stiffness and jumping angle, on jump performance are analyzed by means of dynamic simulation and experiments. The fabricated robot weighs about 10.2 g and can continuously jump on water with the maximum leap height and length of 120 mm and 410 mm, respectively.

In this paper, the task-space cooperative tracking control problem of networked robotic manipulators without task-space velocity measurements is addressed. To overcome the problem without task-space velocity measurements, a novel task-space position observer is designed to update the estimated task-space position and to simultaneously provide the estimated task-space velocity, based on which an adaptive cooperative tracking controller without task-space velocity measurements is presented by introducing new estimated task-space reference velocity and acceleration. Furthermore, adaptive laws are provided to cope with uncertain kinematics and dynamics and rigorous stability analysis is given to show asymptotical convergence of the task-space tracking and synchronization errors in the presence of communication delays under strongly connected directed graphs. Simulation results are given to demonstrate the performance of the proposed approach.

Aiming at mimicking water strider's water-surface locomotion, this study proposes a new miniature surface tension-driven robot. A key feature of this robot is that its actuating legs possess ellipse-like spatial trajectories like water strider by using a cam-link mechanism, and never pierces water surface when rowing. A set of simple models and equations are proposed to analyze the interaction forces between leg and water as well as the critical condition for a leg penetrating a water surface. The final fabricated robot weights about 3.9 g with a load capacity of 5.6 g. By controlling the motions of actuating legs, the robot can freely and stably walk on water with different gaits. The maximum forward and turning speeds of the robot are measured as 16 cm/s and 23 °/s, respectively. Moreover, a similarity analysis with Bond Number and Weber Number reveals that the locomotion of this robot is quite analogous to that of a water strider: surface tension force dominates the lifting force and plays a major role in the propulsion.

This article presents an approach and a systematic design methodology to path following control based on motion decoupling for high-performance ducted fan unmanned aerial vehicles (UAVs). The decoupling is performed according to the principle of regarding the attitude motion as a virtual input of the lateral longitudinal flight dynamics. This allows the attitude and flight controllers to be designed individually without mutual interference. Considering that dynamics of the ducted fan UAV is uncertain, an estimation method of system function based on the smooth saturation function and the state variable integral is proposed. This method has the advantage of less computation while keeping the high estimation performance. The stability analysis and the simulation results showing the practical feasibility of the proposed control scheme to ducted fan UAVs are given.

This paper presented a novel compliant actuator used for lower limb exoskeletons. The compliant joint consists of a series elastic actuator (SEA) and parallel elastic (PE) unit. SEA has various advantages as the actuator of assistive exoskeletons, such as low output impedance, impact absorption, precise force control and high stability. We designed and fabricated a novel SEA as the primary joint actuator which is compact, adjustable and low-cost. Meanwhile an additional elastic unit is installed in parallel with the SEA to improve energy utilization by storing and releasing energy during motion cycles. An adaptive stable controller is designed to realize the joint following motion to a virtual limb. The algorithm can identify and compensate the undetermined contact stiffness between the joint output and the virtual limb. Finally, the performance of the actuator is evaluated through motion tracking and energy-conservation experiments. Preliminary results indicate the validity of the design and imply its potential usage in lower limb exoskeletons.

This article presents the nonlinear dynamics and the posture stabilization control scheme for the single-wheeled pendulum robot (SWPR). Considering the maneuverability of SWPR, the steering is realized through the control for the inertia pendulum (IP) installed horizontally on the middle part of robot body. The feature of the control system modeling consists in a technique for which the posture stabilization control design is based on the parameterization of the dynamic interactions (DIT) between the lateral dynamics, the longitudinal dynamics, and the rotational dynamics. Simulation results showed the feasibility of the SWPR model and the control algorithm.

In the paper, a concept of novel self-reconfigurable robotic system made of the autonomous robotic modules has been reviewed. Each robotic module is made of simple structure and few degrees of freedom; however, a group of the modules is able to change its connective configuration by changing their local connections and has functionality of robotic system which is capable of generating complicated motions and accomplishing a large variety of tasks, such as: transportation, exploration, inspection, construction and in-situ resource utilization. Multimode locomotion and self-reconfiguration are the basic and essential abilities for the self-reconfigurable robotic system. Based on this concept, a new self-reconfiguration system, UBot robotic system that combines the advantages from the chain-based and lattice-based robots has been proposed. Each UBot module which is cubic structure based on universal joint has two rotational DOF and four connecting surfaces that can connect to or disconnect from adjacent modules. The smart structure and the reliable connecting mechanism of the modules make the robot flexible enough to complete multimode locomotion and self-reconfiguration. This paper demonstrates the design philosophy of the UBot module and a solution for multimode motions and self-reconfiguration using the UBot system. The system can complete motion in the modes of quadruped, chain and loop configuration. Besides, the system can deform from one mode to the other though self-reconfiguration. All the proposed methods have been verified though simulations and real hardware experiments.

The passive dynamic walking model, which can only depend on the gravity and its own inertia, presents stable, high-efficient, natural periodic gait on a slight slope. The stable periodic gait of the robot has a delicate balance of energy conversion, which makes the gait adjust itself as the parameters of the model change. In our work, the cell mapping method is combined with Newton-Raphson iteration to obtain the limit cycle of the periodic gait in the model, the track stability of the limit cycle is analyzed, and the eigenvalues change rule of Poincare Jacobi matrix is deduced. The influence of changing parameters on the gait is analyzed and discussed by simulations on the model with different sets of parameters. The result suggests that, the location of the center of leg mass too high or too low, foot radius increase or decrease, the slope or moment of inertia increase, will lead to the occurrence of bifurcation of the gait period and chaos; while the way the gait enters chaos from period doubling bifurcation, which results from different parameters change, obeys the law all the period doubling bifurcation share, that is, it has the same Feigenbaum constant. Furthermore, the dynamic features of the robot at the entrance of the chaos are obtained by the rule of the period doubling bifurcation of the gait; meanwhile, it can be found by the analysis of the gait features in the chaos area that there is also certain periodic law in the chaotic gait.

Significant research have been devoted into the field of multi-robot teleoperation system over Internet. However, the low safety and efficiency due to time delay and complexity between multiple robots and operators prevent the teleoperation technology from developing. Specially, the possible collision between multiple robots has a great effect on the Internet-based multi-operator-multi-robot teleoperation system. In this paper, a task planner based on Petri net theory was proposed to assist multi-operators coordinate multi-robots. The conception of implicit elementary operation and explicit elementary operation were introduced to realize modeling and automatic plan of the system. In the multi-operator multi-robot system built by us, a supervisory controller was designed based on the place invariants not only to lead two operators' manipulation with virtual environment but also to restrict the state of two robots' motion during their work. An experiment via network was carried out to evaluate the validity of the task planner for the medical research of highly dangerous virus such as SARS, bird flu etc. The experimental results show that the safety and efficiency of the system can be improved using the task planner

A closed loop self-calibration method for Delta mechanism and 3-RRR mechanism used in a double parallel haptic device was presented. This approach blocks successively one joint of each parallel mechanism using a simple lockup device and reforms the 3-DOF mechanism to be a 2-DOF one. The parameter errors of the mechanism are identified according to the differences between the sensor output of the redundant chain and nominal model output. Both the condition number and singular value of the error propagation Jacobian are synthesized as a new selection principle of calibration set such that the calibrating process is insensitive to the noise and the calibration precision is increased efficiently. Experiments show that the ratio of maximal error before and after calibrating is 9.6, namely, the setting accuracy of the two mechanisms are increased about 10 times and the error of re-position is less than 0.1 mm and 0.1deg

Time delay over the Internet deteriorates the performance of multi-operator-multi-robot teleoperation system (MOMR). At present, several methods aiming at the problems of the Internet-based multi-telerobot collaboration only depend on the master side. In this paper, a coordinated controller was presented based on the idea of shared control that combined the intelligence of the master side with the slave side. It is composed of three modules, task coordination, motion planning and communication, which can not only increase the efficiency and safety of the system but also relieve the burden and the requirements to the operators. The controller also can cope with the collision between two telerobots in the common environment arising from the time delay. A simulation experiment via an Ethernet local area network (LAN) subject to simulated time delay was carried out to protect the medical staff from highly dangerous virus such as SARS, bird flu etc. And the validity of the coordinated controller was evaluated in the system.