Purpose: To conduct a survey on the research and development of cable-driven rehabilitation devices (CDRDs). Method: This review searches in the databases of PubMed, IEEE Xplore Digital Library, Science Direct, and Google Scholar using various combinations of the following keywords: cable, wire, rehabilitation, assistance, therapy, training, robot, elastic, and pneumatic. Searches in the above databases for references cited by the above-searched references are also conducted to include a larger context of CDRDs. Results: CDRDs are classified into four categories, namely, serial exoskeleton-based, parallel exoskeleton-based, serial end-effector-based, and parallel end-effector-based CDRDs. Each category of CDRDs are further grouped based on the part of the human body to be rehabilitated. All four categories of CDRDs are examined and compared and their advantages and shortcomings are discussed based on popular rehabilitation requirements on weight, adaptability, versatility, misalignment, and safety. Open issues of CDRDs are also discussed. Conclusions: Each category of CDRDs has its own advantages and shortcomings. The selection of a CDRD highly depends on the specific application. Regarding the convenience of setting up a CDRD for rehabilitation, parallel CDRDs usually have better adaptability than serial ones. However, uncertainties come with parallel CDRDs, which makes the control of parallel CDRDs more challenging. Moreover, the strategy of inherent safety has a great potential to further improve the safety of CDRDs.

A Cable-Driven Parallel Robot (CDPR) with Mobile Bases (MBs) can modify its geometric architecture and is suitable for manipulation tasks in constrained environments. In manipulation tasks, a CDPR with MBs inevitably encounters obstacles, including dynamic obstacles. However, the high dimensional state space and a considerable number of constraints caused by multiple cables and MBs make the real-time dynamic obstacle avoidance of a CDPR with MBs challenging. This letter proposes a Reinforcement Learning (RL)-based dynamic obstacle avoidance method for a CDPR with MBs to deal with dynamic obstacles in real time. To explain the RL-based dynamic obstacle avoidance method, this letter focuses on a CDPR with four fixed-length cables connected to four MBs. An RL-based Obstacle Avoidance Controller (OAC) is developed and integrated into a trajectory tracking controller to address the dynamic obstacle avoidance problem of a CDPR with MBs tracking a target trajectory. To explain and evaluate the RL-based dynamic obstacle avoidance method further, an RL-based OAC is trained in a Mujoco simulator and transferred to a CDPR with four fixed-length cables connected to four MBs in the real world.

An increasing number of Autonomous Mobile Robots (AMRs) are used in warehouses and factories in recent years. The risk of some of the AMRs being out of control is surging. Although Reinforcement Learning (RL)-based approaches have achieved dramatic success in the motion planning of a large number of AMRs, the available RL-based motion planning approaches cannot provide a safety guarantee for the remaining functional AMRs if some of the AMRs are out of control. To this end, this paper develops a scalable Multi-agent RL (MARL) with Control Barrier Function (CBF)-based shields algorithm. The MARL with CBF-based shields algorithm can address complex high-level tasks by MARL and deal with the safety issue of every single functional AMR by a low-level CBF-based shield. A CBF-based shield is designed for every single functional AMR to ensure that the action of the functional AMR is safe, even if an uncontrollable AMR is pursuing the functional AMR. Experiments are conducted based on simulated warehouse environments to evaluate the effectiveness and scalability of a safe RL-based motion planning approach (The safe RL-based motion planning approach developed in this study is demonstrated in a video: https://youtu.be/I7ja5nFVpY4). developed according to the MARL with CBF-based shields algorithm.

The movable anchor points make reconfigurable cable-driven parallel robots (RCDPRs) advantageous over conventional cable-driven parallel robots with fixed anchor points, but the movable anchor points also introduce an inherent problem—reconfiguration planning. Scholars have proposed reconfiguration planning approaches for RCDPRs, taking into account the statics and kinematics of RCDPRs. However, a real-time reconfiguration planning approach that considers the dynamics of an RCDPR and is computationally efficient enough to be integrated into the RCDPR's dynamic controller is still not available in the literature yet. This paper develops a real-time reconfiguration planning approach for RCDPRs. A novel reconfiguration value function is defined to reflect the “value” of an RCDPR configuration and provide a reference index for the reconfiguration planning of an RCDPR. And then, the developed approach conducts reconfiguration planning based on the value of RCDPR configurations. The developed approach is computationally efficient, reducing the reconfiguration planning time by more than 93%, compared to single iteration of a box-constrained optimization-based reconfiguration planning approach. Such a high efficiency allows the developed approach to be integrated into an RCDPR's dynamic controller that usually runs with a high frequency. Integrating reconfiguration planning and dynamic control enhances the control performance of the RCDPR. To verify the effectiveness of the developed approach and the integration of reconfiguration planning and dynamic control for RCDPRs, a case study of an RCDPR with seven cables and four movable anchor points is conducted.