Retractable worm robots excel in navigating confined spaces that are inaccessible or harmful to humans, making them an innovative solution for inspection tasks in aerospace craft inspections, planetary exploration, and industrial maintenance. This article introduces RW-Robot, a novel retractable robot featuring a codesigned mechanical structure and control strategy to improve locomotion speed without sacrificing terrain adaptability. The robot utilizes a cascaded M-Canfield parallel mechanism, combining rigid stability with significant deformability for directional pointing and segment retraction. To mitigate rigidity limitations, a spatial locomotion strategy is proposed, synthesizing rectilinear gait and configuration adaptivity. First, a dual-mode bioinspired rectilinear gait integrates caterpillar-inspired cautious movement and inchworm-inspired rapid progression, enabling dynamic mode switching based on task urgency. Second, a foothold model predictive contouring control (MPCC-F) algorithm optimizes the robot's configuration and joint solutions, effectively balancing locomotion speed and terrain compliance, particularly during sharp turns. Simulation and experimental validation demonstrate high-speed locomotion (0.34 body length per second), agile turning capabilities (radius of 0.32 body length), and slope climbing up to 75(degrees), confirming its effectiveness for adaptive inspection tasks. Comprehensive comparisons with related worm robots highlight RW-Robot's superior directional flexibility and rapid inspection capabilities, indicating its suitability for urgent or time-sensitive inspections in confined environments.

Cable-driven serpentine manipulators (CSMs), due to their unique flexibility of movement, have broad application prospects in unstructured and confined environments. To enhance adaptability to different environments and tasks, the design of variable stiffness structures has long been a research focus for CSMs. Inspired by spatial folding mechanisms, such as umbrellas, we propose a novel variable-diameter-stiffness cable-driven serpentine manipulator (VDS-CSM). The standout feature of this innovation is its ability to achieve integrated control over both the outer diameter and the stiffness of the manipulator. First, we present the structural design of the novel VDS-CSM, whose outer diameter and stiffness can be continuously adjusted. Second, we establish the kinematics, statics, and stiffness models for VDS-CSM. Based on this, we conduct an in-depth study of the manipulator's stiffness characteristics. Simulation data indicate that the change ratio of the manipulator's end stiffness is approximately proportional to the square of the change ratio in the manipulator's outer diameter. Finally, we build a VDS-CSM experimental system. Through experiments, the accuracy of the proposed model for VDS-CSM is verified. The experimental results show that the outer diameter and stiffness of the manipulator can vary by 200% and 400%, respectively.

The control effectiveness of model predictive current control (MPCC) for surface mounted permanent magnet synchronous motor (SPMSM) usually deteriorates with parameter mismatch and model uncertainties under different operation conditions, which lead to large current ripples, current tracking errors, and dynamic response degradation. To obtain excellent steady-state and dynamic performance, a robust MPCC based on a newly designed adaptive switching hybrid cost function is proposed. The adaptive switching hybrid cost function combines the merits of linear extended state observer (LESO) and improved prediction error correction (IPEC) strategies. A LESO with lower bandwidth is designed to be implemented in the steady-state to reduce the tracking errors and ripples of the current. To obtain excellent dynamic response and reduce the current tracking errors, an IPEC with a proportional-integral structure is proposed, integrating the accumulated error into the prediction error correction (PEC) strategy. In addition, an adaptive switching function (ASF) with a sliding window is designed to identify the current state and maintain a smooth switch between the LESO and IPEC. Hence, the proposed method inherits the excellent dynamic response of IPEC and the superior steady-state performance of LESO through the ASF. Finally, the control performance of the proposed method is evaluated by the experiment of parameters mismatch and switching methods.

To promote the drive performance of surface-mounted permanent magnet synchronous motor (SPMSM), such as responsiveness, tracking accuracy, and antidisturbance capability, an enhanced deadbeat predictive current control (DPCC) algorithm combining super-twisting terminal sliding mode is proposed in this article. First, a novel SPMSM model considering parameter perturbation is derived. Subsequently, a super-twisting terminal sliding mode control (STSMC) scheme with a super-twisting observer (STO) is proposed to enhance the performance of the speed loop, with stability validated using the Lyapunov theory. In addition, two extended state observers (ESOs) are developed to estimate the predictive error caused by parameter mismatch in the dq axis, respectively, and the estimated values are compensated with feedback to DPCC. Finally, the proposed control methods are implemented on an SPMSM platform and compared with conventional control methods under various operating conditions, and the comparison results prove the superiority.

A cable-driven segmented manipulator (CDSM) has considerable potential in narrow space operations because it has a slender and light body with flexible mobility. However, the existing CDSM segment driving mechanisms are coupled to each other. The driving distance of the rear segment cable is superimposed with that of the front segment cable, which renders the cables’ drive distance inconsistent. Moreover, the system kinematics, dynamics, and control become extremely complex. In this article, a novel decoupling driving mechanism is proposed to solve the coupling problem, simplifying the modeling and control of the CDSM. The routing of the driving cable is designed based on the characteristics of the symmetrical offset (i.e., the same magnitude but opposite in direction) of the cable length applicable to joints with one and two degrees of freedom. By modifying the direction of the driving cable in the middle of the proximal segment, the driving cable length of the distal segment is unaffected by the change of the angle of the front segment. Moreover, to increase the drive stroke, a multiturn winding mechanism is designed, reducing the volume and mass of the driving box. Accordingly, an improved forward and backward reaching inverse kinematics is proposed for CDSM based on virtual joints. Compared with the Jacobian pseudo-inverse method, the computational efficiency is improved. Finally, the proposed mechanisms and methods are verified via a CDSM prototype. The results indicate that the proposed manipulator compared with typical manipulators has larger movement range, higher end velocity, and guaranteed accuracy due to the proposed decoupled driving and fast kinematics resolution.

In recent years, there has been a growing demand for robotic manipulators to perform tasks in various unstructured environments and situations requiring precision and force control. However, traditional robotic arms have limitations in fully leveraging their advantages in such scenarios. To address this demand, we have designed a cable-driven serpentine manipulator (CDSM) that combines force and precision motion control. This control method allows for precise manipulation of forces and torques at the end-effector, particularly in applications like electric vehicle charging and narrow-space exploration. It also enables independent control in multiple configurations. We achieve force-position hybrid control in task space, ensuring accurate control of end-effector force while achieving precise position control in other directions. Additionally, we implement joint angle closed-loop control in joint space to reduce the impact of cable elasticity deformation and friction on joint motion accuracy. Finally, servo control is applied at the lowest motor level. This paper investigates the modeling, sensing, and control of CDSM within a unified framework of hybrid motion/force control. Through experiments and simulations, we demonstrate the high accuracy and practicality of this control method in various scenarios.

One of the typical purposes of using lower-limb exoskeleton robots is to provide assistance to the wearer by supporting their weight and augmenting their physical capabilities according to a given task and human motion intentions. The generalizability of robots across different wearers in multiple tasks is important to ensure that the robot can provide correct and effective assistance in actual implementation. However, most lower-limb exoskeleton robots exhibit only limited generalizability. Therefore, this paper proposes a human-in-the-loop learning and adaptation framework for exoskeleton robots to improve their performance in various tasks and for different wearers. To suit different wearers, an individualized walking trajectory is generated online using dynamic movement primitives and Bayes optimization. To accommodate various tasks, a task translator is constructed using a neural network to generalize a trajectory to more complex scenarios. These generalization techniques are integrated into a unified variable impedance model, which regulates the exoskeleton to provide assistance while ensuring safety. In addition, an anomaly detection network is developed to quantitatively evaluate the wearer's comfort, which is considered in the trajectory learning procedure and contributes to the relaxation of conflicts in impedance control. The proposed framework is easy to implement, because it requires proprioceptive sensors only to perform and deploy data-efficient learning schemes. This makes the exoskeleton practical for deployment in complex scenarios, accommodating different walking patterns, habits, tasks, and conflicts. Experiments and comparative studies on a lower-limb exoskeleton robot are performed to demonstrate the effectiveness of the proposed framework.

Retractable worm robots possess hyper-flexibility, allowing them to work in confined spaces that are difficult for humans. However, the spatial locomotion control of these robots remains challenging due to the robots’ large degrees of freedom. To address this challenge, we propose a phase synthesis (PS) scheme for retractable worm robots. The scheme combines an undulating gait inspired by caterpillars with three-dimensional movement commands. We first introduce the kinematics model and real-world prototype of our retractable worm robot, called RW-Robot, and then we introduce footstep phases to express the timing of segments’ spatial movement. According to the length of movement periods, we classify the movement into short-term movements and long-term movements and compress their patterns in the frequency domain. Our PS scheme aligns the patterns according to the footstep phases to generate new gaits of spatial locomotion. We evaluate the scheme in real-world experiments, including steering and climbing a slope. The experimental results indicate that our scheme allows the RW-Robot to perform flexible spatial locomotion from simple user input.

Data-centric prognostics is beneficial to improve the reliability and safety of proton exchange membrane fuel cell (PEMFC). For the prognostics of PEMFC operating under dynamic load, the challenges come from extracting degradation features, improving prediction accuracy, expanding the prognostics horizon, and reducing computational cost. To address these issues, this work proposes a data-driven PEMFC prognostics approach, in which Hilbert-Huang transform is used to extract health indicator in dynamic operating conditions and symbolic-based gated recurrent unit model is used to enhance the accuracy of life prediction. Comparing with other state-of-the-art methods, the proposed data-driven prognostics approach provides a competitive prognostics horizon with lower computational cost. The prognostics performance shows consistency and generalizability under different failure threshold settings.