Human joints enable precise bending for fine manipulation and complex movements. Similarly, robotic flexibility relies on bending structures, where accurate bending perception is crucial for precise control and enhanced humanrobot interaction. This paper proposes a C-shaped fiber optic array, embedding a fiber Bragg Grating sensor array into a 2 mm thick silicone layer, successfully achieving a highly sensitive (300 pm/N) and electromagnetic interference-resistant bending sensor. The flexible sensor can sensitively detect external stimuli, such as the touch of a 1g weight or a feather, and exhibits a good linear relationship with curvature, facilitating accurate curvature classification. Additionally, leveraging the wearable nature of the sensor, we achieved the detection of finger bending angles. Finally, by attaching the sensor to the wrist and combining it with deep learning algorithms, we achieved 100% gesture recognition accuracy. This sensor holds significant potential for applications in fields such as fruit size classification, rehabilitation healthcare, and human-robot interaction.

Human fingertips are densely distributed with sensory nerve endings, allowing them to perceive various physical characteristics, including pressure, roughness, etc. In this work, we develop a rigid-flexible coupled bionic robotic finger with perception decoupling and slip detection capabilities. Particularly, slip perception is important in grasping operations. Timely prediction of slippage and adjusting gripping force can improve gripping stability. Fiber Bragg gratings (FBGs) are embedded within both the rigid skeleton and flexible shell of the bionic fingertip. The fibers within the flexible shell are capable of sensing slight pressure, while the optical fibers embedded in the rigid skeleton can measure temperature changes. Firstly, this paper introduces the principles of distributed fiber optic sensors and the morphological design of the bionic fingertip. Then, the fabrication process of the bionic fingertip is described. Finally, we verify the multimodal sensory capabilities of the bionic fingertip through a series of experiments. The results demonstrate that the bionic finger can successfully sense whether the slip has occurred during grasping process. In summary, this rigid-flexible bionic finger is expected to play a significant role in dexterous manipulation, fruit picking and so on.

The collaboration of microrobot teams has attracted considerable attention, particularly in the field of micro/nano manipulation. Achieving independent control and motion planning of multiple magnetic microrobots for coordinated movements is one of the most important tasks that is still unsolved. In this paper, a$12\times 12$coil array system is developed to generate a series of localized magnetic fields that enable simultaneous control of multiple identical magnetic microrobots, allowing teams of microrobots to collaborate in parallel for micromanipulation tasks. First, the structure of the microcoil is optimized based on the finite element model to increase the strength and gradient of the magnetic field, which in turn enhances the driving performance of the system. Meanwhile, an improved multi-target tracking algorithm that utilizes kernel correlation filtering (KCF) and image contour detection (ICD) techniques is proposed to improve the tracking accuracy of microrobots. In addition, collaborative planning for multiple magnetic microrobots is also achieved with the combination of the conflict-based search (CBS) algorithm. Finally, the developed system is tested with extensive physical experiments. Especially, experiments on magnetic droplet transport with two microrobots are also conducted. The results impressively demonstrated the effectiveness of the devised system and the proposed methods. Note to Practitioners—This article is motivated by the recent wide interest in magnetic microrobots. Actuated by external magnetic field, magnetic microrobots can wirelessly perform targeted delivery/therapy and other micro-assembly tasks. To facilitate collaboration between microrobots, independent control of each microrobot is desirable. However, due to the interaction between magnetic microrobots and the global magnetic field, the collaboration of multiple microrobots presents great challenges. Therefore, several coil-array-based systems have been developed. In this paper, we develop a magnetic actuation system from both hardware and software aspects for the collaborative motion of multiple magnetic microrobots. The coil structure is optimized to enhance the driving performance of the devised system, and a fused multi-target tracking algorithm is proposed to improve the tracking accuracy. In combination with the CBS algorithm, collision-free paths are planned for multiple identical microrobots. The experimental results show that the constructed system and proposed methods can realize coordinated motion of multiple identical magnetic microrobots, which has enormous potential for some biomedical applications.

Magnetic field-driven microrobots have shown high potential in the field of medical applications. Autonomous navigation is a crucial concern for magnetic microrobots, however, the path planning, actuation and control of magnetic microrobots still remain challenging, especially for complex and large-workspace human body environments. Depending on the specific task and environmental conditions, it is important to employ appropriate planning and control architectures for the magnetic navigation systems. In light of this objective, this paper introduces a novel navigation framework, using an improved path planning and following control method. An evolutionary strategy based RRT (ES-RRT) planner is designed to achieve a shorter, smoother and safer path. Furthermore, an extended state observer (ESO)-based controller is specifically designed for the path tracking process of the microrobots. This controller enables the microrobots to accurately follow the computed path. Experiments demonstrate the effectiveness of the proposed strategy: Feasible path in different conditions and environments can be obtained with short and smooth enough, and autonomous navigation following of microrobots is realized with satisfactory path tracking control accuracy.Note to Practitioners—In contrast to macroscale robots, microrobots face challenges when it comes to integrating onboard components such as processors and power sources. Consequently, alternative methods have been developed, including optical, chemical, and biological actuation. Among these approaches, the utilization of magnetic fields is particularly favorable due to its ability to penetrate deep tissues while ensuring high safety. Additionally, magnetic fields offer diverse propulsion options, such as rotating fields and oscillating fields, along with excellent controllability. Despite significant advancements in the fabrication, functionalization, and locomotion of magnetic microrobots, autonomous navigation remains an area that requires further development. In the medical application, the planning path of the microrobots needs to be short and smooth enough. Besides, considering the unknown dynamics and external disturbances of the system, it is extremely important for microrobots to complete the precise path following control of the planned path. The motivation of this work is to develop an effective navigation scheme for microrobots which consisting of a path planner and motion controller. A shorter and smoother path will be planned based on a novel improved RRT planner. The precise control of the path following then will be achieved by adopting an extended state observer (ESO) controller. The proposed methods would enable microrobots to act safely and greatly enhance robots’ capabilities.

The parafoil is a flexible aircraft with good load characteristics and endurance. Compared with common rotor Unmanned Aerial Vehicles (UAV), its excellent gliding characteristics make it widely used in fixed-point airdrop missions in the aviation field. However, while the flexible flight structure brings excellent flight characteristics, the homing of the parafoil will be seriously disturbed by wind. This paper studies the wind disturbance problem of parafoils, and proposes a dynamic homing planning strategy derived from the forward model based on the parafoil dynamic model. Focusing on the homing process of the parafoil, the wind repulsion potential field is established for the disturbance of the low-altitude wind field by Artificial Potential Field (APF). Recursive navigation planning is carried out based on Dynamic Path Rapid-exploration Random Tree (DP-RRT). We validate the dynamic programming algorithm through simulation and experimental environments. The results show that the dynamic programming algorithm can effectively improve the parafoil homing process’s flight control stability and homing accuracy.

Programmable manipulation of fluid-based soft robots has recently attracted considerable attention. Achieving parallel control of large-scale ferrofluid droplet robots (FDRs) is still one of the major challenges that remain unsolved. In this article, we develop a distributed magnetic field control platform to generate a series of localized magnetic fields that enable the simultaneous control of many FDRs, allowing teams of FDRs to collaborate in parallel for multifunctional manipulation tasks. Based on the mathematical model using the finite element method, we first evaluate the distribution properties of the local magnetic fields as well as the gradients generated by individual electromagnets. Meanwhile, the locomotion and deformation behavior of the FDR is also characterized to verify the actuation performance of the developed system. Subsequently, a vision-based closed-loop feedback control strategy is then presented, which aims to achieve path tracking of multiple robot formations. Thermal analysis shows that the system's low output power enables reliable and sustained long-term operation. Finally, the developed system is tested through extensive physical experiments with different numbers of FDRs. The results demonstrate the potential of the designed setup in manipulating dozens of FDRs for digital display, message encoding, and microfluidic logistics. To the best of our knowledge, this is the first attempt that allows independent control of such scale droplet robots (up to 72) for cooperative applications.

Tactile sensing is pivotal for enabling effective human-robot interaction, especially in unstructured environments. This work introduces an innovative bioinspired soft robotic finger endowed with shape-adaptive and multi-modal tactile perception capabilities, drawing inspiration from diverse biological tactile sensing modalities. Through an advanced Fin Ray structure, the soft finger features tactile whiskers on its fingertips, facilitating perception of obstacle orientation, fingertip pressure, surface roughness, and grasping ball size. Leveraging distributed optical fiber sensing technology, we develop a sophisticated multi-point, multi-modal tactile perception neural network tailored for the soft finger. Meticulous integration via advanced 3D printing and silicone coating techniques seamlessly embeds optical fiber sensors within the soft robotic finger, creating an intelligent perception-capable bioinspired mechanical system. Experimental validation confirms the soft robotic finger’s sensitive and precise force perception and curvature recognition abilities, achieving accuracies of up to 100%. In summary, our bioinspired robotic finger holds significant promise for applications in intelligent sensing, non-destructive grasping, and fruit classification within unstructured environments, thus advancing the field of robotics and human-robot interaction.

Central Pattern Generators (CPG) nonlinear oscillation network is being increasingly used in the control of multi-joint collaborative robots. The motion attitude of robots can be effectively adjusted by tuning parameters of the CPG neural network. However, the mapping from CPG parameters to motion attitude is relatively complicated. To improve the motion performance, an optimization method combining computational fluid dynamics (CFD) and CPG network is proposed. In this work, we design a three-joint biomimetic soft robot fish following the body structure of trevally and an improved CPG network based on the Hopf model is incorporated into the control system. Directly optimizing the swimming performance through experiments is time consuming and complex, a mode of first adjusting parameters on the simulation platform and then refining on the robot is usually adopted. Therefore, a CFD simulation platform using hydrodynamic solutions has been established to assist in analyzing the swimming effect. Finally, the experimental results show that the swimming simulation by the CFD is highly similar to the real test, and the swimming performance after the improved CPG network optimization has been significantly increased.

Active fault diagnosis (AFD) designs inputs to excite the system to obtain more operation information for fault diagnosis. Since both AFD and control are required to design systems’ inputs, individually designing inputs for fault diagnosis will restrict the systems’ control performance. In order to achieve satisfactory performance for both AFD and control, this paper presents a novel set-based input design method for simultaneous AFD and control. The new method adopts a decomposition-composition idea to integrate AFD and control for better control performance during the AFD stage. Particularly, the proposed method first considers each single system mode separately and establishes an optimization problem, combining the AFD and control objectives, to design the optimal input. Then, all possible system modes are comprehensively considered to select the final input from all inputs designed under the single mode by an adaptive selection strategy. Meanwhile, the proposed method realizes AFD by maximizing the separation trend of all output sets online, without considering the strict set separation conditions. Hence, the method in this paper has a simple mathematical form and lower computational complexity. At the end, simulation results based on two different examples are presented to verify the effectiveness and portability of the proposed method.Note to Practitioners—Set-based AFD methods have two main features. The first one is that they only require the bounds of system uncertainties, such as modelling errors, process disturbances and measurement noise, and do not require their specific distributions and values. This requirement can be easily satisfied by varieties of engineering systems. The second one is that they design inputs to actively excite the system to obtain more system operation information for fault diagnosis. This feature enables AFD to achieve high fault diagnosis sensitivity and detect more faults, such as incipient faults, small faults, etc. Thus, when AFD methods are used in active fault-tolerant control (FTC) systems, the whole FTC performance can be improved. However, set-based AFD has conflicting requirements on input design with control, while there only exist few works in the literature on the integrated design of set-based AFD and control. This paper proposes a novel integrated design method for AFD and control, which can achieve satisfactory performance for both AFD and control with low computational complexity. Besides, although this paper only considers discrete linear time-invariant (LTI) systems, the proposed method can be extended to more complex systems such as linear parameter-varying (LPV) systems, linear time-varying systems, etc. Moreover, based on LPV modelling techniques, equilibrium linearization techniques, etc., the proposed method can be used for fault diagnosis and FTC of some nonlinear systems as well. Therefore, the proposed method has advantages in online fault diagnosis and FTC applications of engineering systems such as unmanned aerial vehicles, unmanned ships, driverless automobiles, space systems, robots, process industries, etc., which own values and potential to improve safety and reliability of varieties of engineering systems.

All-around, real-time navigation and sensing across the water environments by miniature soft robotics are promising, for their merits of small size, high agility and good compliance to the unstructured surroundings. In this paper, we propose and demonstrate a mantas-like soft aquatic robot which propels itself by flapping-fins using rolled dielectric elastomer actuators (DEAs) with bending motions. This robot exhibits fast-moving capabilities of swimming at 57mm/s or 1.25 body length per second (BL/s), skating on water surface at 64 mm/s (1.36 BL/s) and vertical ascending at 38mm/s (0.82 BL/s) at 1300 V, 17 Hz of the power supply. These results show the feasibility of adopting rolled DEAs for mesoscale aquatic robots with high motion performance in various water-related scenarios.

Nanomaterials possess superior mechanical, electrical, and optical properties suitable for device applications in different fields such as nanoelectronics, photonics, and sensors. Characterizing the multiphysical properties of single nanomaterials and nanostructures provides experimental guidelines for synthesis and device applications of functional nanomaterials. Nanomanipulation techniques under scanning electron microscopy (SEM) have enabled the testing of mechanical and electrical properties of various nanomaterials. However, the introduction of micro-photoluminescence ($\mu $-PL) measurement into an SEM setup for in-situ single nanomaterial characterization is still experimentally challenging; in particular, the seamless integration of the mechanical, electrical, and$\mu $-PL testing techniques inside an SEM for multi-field-coupled characterization of single nanostructures is still unexplored. In this work, we report the first SEM-based nanomanipulation system for multiphysical characterization of single nanomaterials. A custom-made, optical-microfiber-based$\mu $-PL setup is integrated onto a nanomanipulation system with four nanomanipulators inside an SEM. The system is also equipped with a conductive nanoprobe and a conductive atomic force microscopy (AFM) probe for electrical nanoprobing and electroluminescence (EL) measurement of single nanomaterials with contact force feedback. Using the system, field-coupled characterization (i.e., optomechanical, optoelectronic, electromechanical, and mechano-optoelectronic testing) of single InGaN/GaN nanowires (NWs) are conducted; and, for the first time, the effect of mechanical compression applied to individual InGaN/GaN NWs on its optoelectronic property is revealed. Note to Practitioners—With the rapid advances of nanophotonics and nanoelectronics, the optical and optoelectronic characterization of semiconductive nanomaterials becomes widely used for guiding the material synthesis and improving the nanodevice performance. However, few studies on optical-relevant characterization were carried out in SEM, mainly due to the limited space of an SEM chamber, making it challenging to integrate optical components for effective optical excitation and luminescence measurement. To address this issue, space-saving optical microfibers were integrated into the SEM chamber for in-situ optoelectronic characterization of semiconductor NWs, along with the seamless integration of mechanical and electrical nanoprobing tools for electromechanical characterization. The developed nanomanipulation system will greatly facilitate the multiphysical testing of semiconductor nanomaterials, and thus expedite their synthesis optimization processes and broaden their optoelectronic device applications.

Functional nanomaterials possess exceptional multi-physical (e.g., mechanical, electrical and optical) properties compared with their bulk counterparts. To facilitate both synthesis and device applications of these nanomaterials, it is highly desired to characterize their multi-physical properties with high accuracy and efficiency. The nanomanipulation techniques under scanning electron microscopy (SEM) has enabled the testing of mechanical and electrical properties of various nanomaterials. However, the seamless integration of mechanical, electrical, and optical testing techniques into an SEM for triple-field-coupled characterization of single nanostructures is still unexplored. In this work, we report the first SEM-based nanomanipulation system for high-resolution mechano-optoelectronic testing of single semiconductor InGaN/GaN nanowires (NWs). A custom-made optical measurement setup was integrated onto a four-probe nanomanipulator inside an SEM, with two optical microfibers actuated by the nanomanipulator for NW excitation and emission measurement. A conductive tungsten nanoprobe and a conductive atomic force microscopy (AFM) cantilever probe were integrated onto the nanomanipulator for electrical nanoprobing of single NWs for electroluminescence (EL) measurement. The AFM probe also served as a force sensor for quantifying the contact force applied to the NW during nanoprobing. Using this unique system, we examined, for the first time, the effect of mechanical compression applied to an InGaN/GaN NW on its optoelectronic properties.

This paper reports the electrostatic regulation of surface traction of a quadruped soft robot to improve its locomotion efficiency. The soft robot, containing five pneumatic channel networks (PneuNets) in different parts of its body, is actuated to achieve undulated locomotion. Electrostatic adhesion is applied to the bottom surface of each robot leg, by using a thin elastomeric adhesion pad embedded with interdigitated comb electrodes. The adhesion pad is fully compatible with the soft robot structure, and is able to adjust the level of surface traction on the robot leg during locomotion. We calibrate the adhesion force generated by the pad as a function of its size and the applied electrostatic voltage. We demonstrate the control of the moving direction and speed of the soft robot on horizontal surfaces with different frictional and electrical characteristics, by adjusting the level of electrostatic adhesion. With the electrostatic traction control, the robot can also climbing up an inclined metal surface with a low coefficient of friction, which cannot be achieved by the same robot without adhesion pads. This work illustrates the important role of surface friction on locomotion of the soft robot, and provides an efficient solution to surface traction control of soft robots.

This paper reports a microelectromechanical systems (MEMS)-based microgripper, integrating two-axis actuators and force sensors, for microscale compression and shear testing of soft materials. The device employs V-beam electrothermal actuators to drive an active gripping arm and compress or shear a microscale sample grasped at the gripping tips, and two triplate differential capacitive sensors to measure the compression and shear forces applied to the sample with nanonewton resolution (compressive force resolution: 7.7 nN, and shear force resolution: 57.5 nN). Using the microgripper, we demonstrate, for the first time, on-chip compression and shear testing of polydimethylsiloxane (PDMS) microstructures prepared at different crosslinking levels. We believe that this device will be useful for accurately characterizing mechanical properties of a variety of microscale soft materials.

Many micromanipulation systems employ sensorless actuators and possess unknown modeling errors, feedback measurement noise, and time delays. Conventional modelbased control schemes ignore some of these characteristics, and thus sacrifice the control performance of the system. This paper presents a new model compensation-prediction scheme for control of micromanipulation systems, which estimates the unknown modeling errors from single noisy feedback measurement and predicts and compensate the system time delay. This approach combines two modeling errors into a single equivalent error through mathematical transformation, and estimates the combined term using a noise-insensitive extended high-gain observer (EHGO). After removing the unknown term, the system is then transformed into a time invariant form, and a Smith predictor is implemented to predict and compensate the time delay. The effectiveness of the proposed compensation-prediction scheme is demonstrated by both numerical simulation and experiments of two typical micromanipulation systems. The results show that this method is able to significantly improve the control performance of a conventional PID controller by simultaneously reducing the settling time and overshoot of the system.