Lin Liu

Cable-driven parallel robots (CDPRs) are attractive for large-space manipulation because of their lightweight structure, large workspace, and reconfigurability. However, existing systems still face three practical challenges: limited modularity of the mechanical architecture, repeated calibration after reconfiguration, and insufficient integration between visual perception and grasp execution. To address these issues, this paper presents a modular cable-driven parallel robot (MCDPR), together with its kinematic modeling, vision-based self-calibration, and visual grasping methods. First, a modular mechanical architecture is developed in which the drive, sensing, and cable-guiding functions are integrated to support rapid assembly/disassembly, convenient debugging, and cable anti-slack operation. Second, a pulley-considered multilayer kinematic model is established, and a vision-based self-calibration method is proposed to identify the structural parameters after assembly using onboard sensing and AprilTag observations, thereby reducing the number of recalibrations required during robot operation after reconfiguration. Third, a vision-guided bin-picking method is developed by combining RGB-D perception, coordinate transformation, and the calibrated robot model. Simulation and prototype experiments are conducted to validate the proposed system. A software/hardware combined validation framework is established, in which the CoppeliaSim-based simulation and the hardware prototype are used together to verify the proposed design and methods. In simulation, self-calibration reduces the Euclidean grasping position error from 0.371 mm to 0.048 mm and the orientation error from 0.071° to 0.004°. In experiments, the relative position error is reduced by 58.33% after self-calibration.

This letter presents the development of a multiple-input multiple-output (MIMO) coupling controller for the independent-setup variable stiffness actuator (VSA). The dynamic model of the independent-setup VSA is typically considered decoupled due to the slight coupling between the joint and stiffness actuation. However, the model can be formulated in a coupled manner by taking into account the various uncertainties arising from physical stiffness variations. To describe the coupling, we present a linear parameter-varying (LPV) model. In this LPV model, the time-varying parameters are defined to capture the interaction between the two actuation components. Then, we introduce a robust MIMO coupling controller designed to ensure reliable operation. The controller is synthesized using the $H_{\infty }$ mixed-sensitivity loop-shaping and LPV technique. This synthesis ensures the quadratic stability of the closed-loop system in the presence of time-varying parameters, along with effective tracking of joint and stiffness trajectories. Additionally, the generalized extended state observer is employed as a design paradigm to verify whether the proposed baseline LPV controller can be integrated with other advanced control strategies previously explored in VSA control. The effectiveness of all control schemes is experimentally validated on an updated mechanical-rotary variable impedance actuator prototype.

Adaptive grasping (AG) of objects with unknown shapes and contact force sensing during grasping are two critical issues in object grasping. However, most of the current grippers that rely on passive grasping mechanisms do not consider both of these functions at the same time. In this article, we design a multibranch compliant flexible gripper (MBCFG), propose a method for intrinsic force perception and extrinsic deformation measurement without tactile sensor measurement, and finally achieve stable grasping of irregular objects and “force–deformation” sensing in real time. Furthermore, based on the above perceptual model, the envelope degree of grasping and the grasping force constraint model are established, which in turn indicates the evaluation method of grasping quality (GQ). Experimental results show that the flexible gripper (FG) has high sensing accuracy (i.e., its average error is less than 0.78 mm) for the external force applied at the two end nodes, but the error occurs much more when the external force is applied at its middle node (i.e., its average error is about 2.76 mm). In addition, the FG assembled at the end-effector of the robot successfully grasped a variety of objects with different shapes. Besides, the experiments of pick-and-place operation showed that the grasping performance based on “force–deformation” perception was good. Moreover, the fitted envelope curve and the shape curve of the FG also matched very well, which further proved the AG and contact force perception capability of the FG for objects with unknown shapes.

This paper addresses the trajectory tracking problem for a class of uncertain manipulator systems under the effect of external disturbances. The main challenges lie in the input constraints and the lack of measurements of joint velocities. An extend‐state‐observer is utilized to estimate the velocity signals; then, a neural‐network‐based adaptive controller is proposed to solve the problem, where a term based on the nominal model is included to enhance the tracking ability, and the effect of uncertainties and disturbances are compensated by a neural‐network term. Compared with the existing methods, the main distinctive features of the presented approach are: (i) The control law is guaranteed to be bounded by design, instead of directly bounded by a saturation function. (ii) The trade‐off between the performance and robustness of the presented controller can be easily tuned by a parameter that depends on the size of model uncertainties and external disturbances. By virtue of the Lyapunov theorem, the convergence properties of the proposed controller are rigorously proved. The performance of the controller is validated via both simulations and experiments conducted on a two‐degree‐of‐freedom robot manipulator.

Objective : Although there have been studies conducted on the instantaneous remote center of motion (RCM) mechanism, the general closed-loop control method has not been studied. Thus, this paper fills that gap and employs the advantages of this mechanism to develop a novel injection system. Methods : The injection prototype involves the instantaneous RCM mechanism, insertion unit and injection unit. The RCM system is investigated in the presence of time-varying axial stiffness of the screw drive and underactuated case. For safe interaction, compliance control is designed in the insertion system. The stability of all separate systems is investigated with the bounded parameter variation rate. The injection prototype and a robot end-effector were then combined to perform injection. Results : Our RCM prototype can achieve a large workspace, and its control effectiveness was verified by multiple frameworks and comparison with previous studies. Compliance-controlled insertion can achieve accurate depth regulation and zero-impedance control for manually operating the needle. With the help of three-dimensional reconstruction and hand/eye calibration, the manipulator can guide the injection prototype to a proper pose for injection of a face model. Conclusion : The injection prototype was successfully designed. The effectiveness of the whole control system was verified by simulations and experiments. The particular robotic injection task can be performed by the prototype. Significance : This paper provides alternative schemes for developing an instantaneous RCM system, screw drive-based surgical tool, and robotic insertion with small needles.

The cascade impedance control (CIC) is a wellknown architecture framework for rehabilitation exoskeleton with compliant joint (e.g., a series elastic actuator). The cascaded position-torque-velocity control loop of the CIC system can deal with specific issues, such as stiction or the accuracy of output impedance. In this paper, the control loops of the CIC are extended by the traditional iterative learning control (ILC), and then we examine and compare three types of frameworks, named torque learning, impedance learning, and trajectory learning. Their advantages, such as reducing the lag of the output trajectory with a low-gain impedance controller (safety), are discovered. Furthermore, the exoskeleton system is upgraded with the ability of a variable impedance. In this part, a fuzzy logic system is proposed. This system employs the electromyography signals of the subject and the exoskeleton torque as input, and the decisions on the variable impedance as output. The experiment verifies that the proposed algorithm can effectively decrease the impedance of the exoskeleton when detecting a spasticity from the subject, and can maintain the original dynamics of the system when the subject performs a normal movement. Afterwards, the effectiveness of the fuzzy logic system together with the ILC-based CIC is experimentally verified in the case of subject-exoskeleton collaborative.

Active lower limb exoskeletons are increasingly used in rehabilitation therapy. An important aspect and ongoing research topic in developing such assistive devices is safe human–machine interaction. Another major challenge in the control of exoskeletons is determining the subject's intention to move for providing cooperative assistance. To address both challenges, we first present the design of a new lower limb exoskeleton with variable stiffness actuators for compliant coupling between human and robot. The exoskeleton's actuated hip, and knee joints are controlled via a stiffness-dependent torque control. In a second step, we derive a detailed model for the swing phase using the Lagrange formalism. We utilize the model and an interaction torque measurement based on the deflection of the serial elasticity to estimate the subject's motion intention. This estimation is realized by nonlinear state estimation using an unscented Kalman filter (UKF). The UKF is able to track the hip and knee torque contributed by the subject during normal gait in real time and provides more robust results than the inverse dynamics approach.

With the development of economy, electronic products are playing an increasingly important role. People often need to stare at electronic screens to use electronic products. It can be effective in correcting vision by glasses or surgery for patients with ametropia. But these methods don't work for patients with high-order aberrations. In this work, a method of digital image processing is proposed for these patients. With wavefront aberrations of patient eyes obtained, source images can be pre-compensated to counteract the wavefront aberrations so that the patients can observe the clear images directly. In order to solve the contrast problem caused by the method, an algorithm based on a method of single defogging and Contrast-limited adaptive histogram equalization with subsequent processing is proposed. The algorithm is compared with some traditional methods qualitatively and quantitatively.

We present a framework for achieving a robust stability test of the variable stiffness actuator (VSA) exoskeleton programmed with the gain scheduling-based variable impedance control (GSVIC). In this brief, the focused impedance control framework involves the cascaded position torque control loop, in which both control loops are closed, and the index of the GSVIC is related to the joint torque of the human. However, there is a lack of research on the variation rate of such a biofeedback signal. This lack of information is necessary when performing the robust stability test of the linear parameter-varying (LPV) system. To acquire a bounded variation rate, the mechanical stiffness variation component of the VSA is used to transfer the biofeedback information into the GSVIC. In this case, the LPV system has only one variable parameter with the known and bounded variation rate, i.e., the physical stiffness of the VSA. The operating range of the impedance controller, determined from the test of robust stability via parameter-dependent Lyapunov functions, can meet our application requirements. The whole control system follows the paradigm of the variable impedance task in accordance with human intention. The effectiveness of the control scheme is experimentally verified on a VSA prototype and two subjects wearing the VSA exoskeleton.