The vibration suppression for rigid-flexible coupling hoisting robots (RFCHRs) under compound motion has always been a focus in the engineering. Most vibration suppression control methods are overly complex in design, making them hard to effectively apply. Some methods that are relatively easy to deploy often fail to adapt well to changes in system uncertain parameters and external environmental disturbances, raising concerns about their robustness. To address this problem, a robust input shaping vibration suppression control method based on deep reinforcement learning (DRL) is proposed. Based on real-time environmental state feedback, this method utilizes the strategy gradient optimization capability of the proximal policy optimization with clipping (PPO-Clip) algorithm, enabling real-time updates of the shaper parameters according to changes in system parameters and external environmental. It can achieve faster vibration suppression for RFCHR, while also possessing strong adaptability to system parameter changes and anti-interference capability. Finally, experiments verify its effectiveness and superiority.

This paper proposes a new teleoperated telepresence robot system with high-fidelity haptic feedback for interaction work, such as pre-operation of explosive ordnance disposal (EOD). The system includes a master device and a slave collaborative robot. The master device enables the human operator to feel the operation procedure when performing the environmental interaction task. The system’s kinematics and dynamics models are derived, laying the foundation for the control scheme design. To meet the requirements of EOD missions, a hybrid motion mapping method including position-velocity (PV) and position-position (PP) mapping modes is presented to achieve a balance between working efficiency and manipulation accuracy. A hybrid haptic force rendering method is introduced to facilitate the human’s feeling of the interacting force and control the slave robot in PP and PV mapping modes, respectively. Experimental results reveal that the developed system exhibits good position and velocity tracking performance with root-mean-square (RMS) errors of 0.004 m and 0.007 m/s, respectively. The haptic tracking is realized with an RMS force error of 0.14 N. Moreover, the developed system can improve the EOD working efficiency by 10.7% with accurate operation while ensuring the fidelity of the haptic feedback to feel the contact process. The reported telepresence robot system provides a promising solution to delicate remote interaction operations.Note to Practitioners—Teleoperated robotic systems are commonly utilized to perform hazardous tasks such as EOD tasks. However, the existing EOD robots controlled by the joystick have limited dexterity. Moreover, visual feedback alone cannot provide enough telepresence for an operator, which leads to low efficiency. This paper proposes a new teleoperated robot system with high-fidelity haptic feedback assistance intended for EOD tasks. The system integrates the motion mapping method that combines PP and PV modes to improve the working efficiency while maintaining manipulation accuracy. The haptic sensations feedback can switch the rendering mode according to different control modes. The haptic feedback mechanism enables the operators to feel reliable contact under PP mapping mode and ensures safety and stability under PV mapping mode. Performance testing and application experiment verified the feasibility of the reported robotic system.

Frequent and high-load manual material handling (MMH) tasks often cause back injuries to the workers, and backsupport exoskeletons are developed for individuals with MMH tasks. However, these exoskeletons usually cannot adapt well to the movements of the wearer's spine. This paper introduces a new bio-inspired 5-DOF origami, and via mechanical design, a unique rigid-flexible coupled bio-inspired origami mechanism is proposed. This origami mechanism is compact and lightweight, and it has stable kinematic behaviors. With the designed origami mechanisms, a novel active origami-based robotic spine assistive exoskeleton (OSAE) is developed to assist individuals with MMH tasks during the symmetric and asymmetric lifting. The OSAE is actuated by a cable-driven module through an under-actuated spine module that consists of seven origami mechanisms. With the designed spine module, the OSAE can adapt well to the wearer's spine motions during MMH tasks. Modeling of the 5- DOF origami is described, and an adaptive control strategy is proposed for the exoskeleton to adapt to different lifting methods and objects with different weights. The experimental results demonstrate the effectiveness of the proposed OSAE. During the symmetric lifting of a 10-kg object, a reduction of 41.28% of the average muscle activity of the wearer's lumbar erector spinae muscle (LES) is observed, and reductions of 30.15% and 39.54% of the average muscle activities of the wearer's left and right LES are observed, respectively, during the asymmetric lifting of a 10- kg object.

Robotic hand exoskeletons hold immense potential for enhancing human hand functionality, addressing the hand's strength limitations and fatigue during physically-demanding tasks. However, most existing hand exoskeletons are motorized, being weak in generating high supporting force for gripping augmentation. We present a non-motorized hand exoskeleton based on magnetorheological (MR) actuators to provide high gripping support and elevate grip endurance. Meanwhile, it ingeniously harnesses human energy for actuation and energy storage, enhancing grip strength without external power. The MR actuator demonstrates a peak holding force of 1046 N with merely 5 W power input, boasting a force-to-power ratio one-order-of-magnitude higher than conventional approaches, and 97.7% energy reduction for same holding force compared to other approaches. Participants wearing the hand exoskeletons experience a 41.8% enhancement in grip strength without external power and reduced hand muscle fatigue during prolonged physical labor. In rescuing scenarios such as post-earthquake rescue, debris clearance, and casualty evacuation, our exoskeleton effectively supports gripping and improves working efficiency.

This paper proposes a fuzzy proportion integration differentiation (PID) control strategy based on an adaptive whale optimization algorithm (FPID-AWOA) for trajectory tracking of a cable-driven parallel robot (CDPR). A mechanical prototype, and kinematic and dynamic models of the CDPR are established. Thus, new fuzzy rules are developed and a new fuzzy PID controller is designed. Subsequently, the AWOA is introduced to optimize quantization and scale factors of the fuzzy PID controller to obtain the optimal solution. Among them, AWOA is an improvement on WOA. Numerical examples show that the fuzzy PID control strategy based on adaptive whale optimization algorithm (FPID-AWOA) has higher CDPR trajectory tracking accuracy than the traditional fuzzy PID control strategy, the fuzzy PID control strategy based on whale optimization algorithm (FPID-WOA), and the fuzzy PID control strategy based on particle swarm optimization (FPID-PSOA). In comparison with the FPID and FPID-PSOA, the experimental results show that the trajectory tracking error of the proposed FPID-AWOA is reduced by 51.2% and 19.5% in the$X$-axis direction, respectively, 64.2% and 49.7% in the$Y$-axis direction, respectively, and 29.1% and 12.2% in the$Z$-axis direction, respectively.Note to Practitioners—The motivation of this article stems from the need to develop efficient trajectory tracking control algorithms for practical applications of CDPRs. Fuzzy PID control is widely used in CDPRs because of its good robustness and fast response speed. However, the fuzzy parameter selection depends on experience, and the efficiency is low. In order to obtain high quality quantization and scale factors quickly, we propose FPID-AWOA. It uses AWOA to find the optimal quantization and scale factors, which makes the fuzzy PID control get better performance. FPID-AWOA can also be applied to control other robots. In future research, we will extend the proposed approach to multiple CDPRs working collaboratively as well as to mobile operational requirements.