一种用于单关节手臂运动康复的自对准末端执行器机器人的设计与表征

IF 2.9 Q2 ROBOTICS Robotics Pub Date : 2023-11-07 DOI:10.3390/robotics12060149
Prem Kumar Mathavan Jeyabalan, Aravind Nehrujee, Samuel Elias, M. Magesh Kumar, S. Sujatha, Sivakumar Balasubramanian
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引用次数: 0

摘要

传统的手臂康复末端执行器机器人通常安装在手上,主要侧重于多关节的协调训练。现有的末端执行器机器人并不总是能够对严重中风幸存者进行单个关节水平的治疗。手臂康复机器人(AREBO)是一种末端执行器机器人,设计用于提供单关节和多关节辅助训练,同时保留传统末端执行器机器人的优点,如易于使用,紧凑和便携性,以及潜在的成本效益(与外骨骼相比)。这项工作介绍了用于训练手臂单关节运动的AREBO的设计、优化和特性。AREBO有三个驱动和三个非驱动自由度,允许它在其端点向任意方向施加力,并在其工作空间内自对准任意方向。AREBO的连杆长度进行了优化,以最大限度地提高其工作空间和可操作性。AREBO提供无辅助和自适应重量支持模式的单关节训练,使用人体手臂模型来估计人体手臂的运动学和动力学,而不使用额外的传感器。利用人体肩关节的二自由度机电一体化模型,对机器人控制器进行了表征,并给出了人体手臂参数的估计算法。结果表明:(a)可以使用人体手臂和机器人的运动学模型来估计人体手臂的运动,(b) AREBO具有与文献中现有的手臂治疗机器人相似的透明度,以及(c)自适应重量支撑模式控制可以适应手臂不同程度的损伤。这项工作展示了如何设计一个适当的末端执行器机器人可以用于单关节训练,它可以很容易地扩展到多关节训练。未来的工作将集中于评估该系统对任何需要手臂训练的神经系统疾病患者的影响。
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Design and Characterization of a Self-Aligning End-Effector Robot for Single-Joint Arm Movement Rehabilitation
Traditional end-effector robots for arm rehabilitation are usually attached at the hand, primarily focusing on coordinated multi-joint training. Therapy at an individual joint level of the arm for severely impaired stroke survivors is not always possible with existing end-effector robots. The Arm Rehabilitation Robot (AREBO)—an end-effector robot—was designed to provide both single and multi-joint assisted training while retaining the advantages of traditional end-effector robots, such as ease of use, compactness and portability, and potential cost-effectiveness (compared to exoskeletons). This work presents the design, optimization, and characterization of AREBO for training single-joint movements of the arm. AREBO has three actuated and three unactuated degrees of freedom, allowing it to apply forces in any arbitrary direction at its endpoint and self-align to arbitrary orientations within its workspace. AREBO’s link lengths were optimized to maximize its workspace and manipulability. AREBO provides single-joint training in both unassisted and adaptive weight support modes using a human arm model to estimate the human arm’s kinematics and dynamics without using additional sensors. The characterization of the robot’s controller and the algorithm for estimating the human arm parameters were performed using a two degrees of freedom mechatronic model of the human shoulder joint. The results demonstrate that (a) the movements of the human arm can be estimated using a model of the human arm and robot’s kinematics, (b) AREBO has similar transparency to that of existing arm therapy robots in the literature, and (c) the adaptive weight support mode control can adapt to different levels of impairment in the arm. This work demonstrates how an appropriately designed end-effector robot can be used for single-joint training, which can be easily extended to multi-joint training. Future work will focus on the evaluation of the system on patients with any neurological condition requiring arm training.
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来源期刊
Robotics
Robotics Mathematics-Control and Optimization
CiteScore
6.70
自引率
8.10%
发文量
114
审稿时长
11 weeks
期刊介绍: Robotics publishes original papers, technical reports, case studies, review papers and tutorials in all the aspects of robotics. Special Issues devoted to important topics in advanced robotics will be published from time to time. It particularly welcomes those emerging methodologies and techniques which bridge theoretical studies and applications and have significant potential for real-world applications. It provides a forum for information exchange between professionals, academicians and engineers who are working in the area of robotics, helping them to disseminate research findings and to learn from each other’s work. Suitable topics include, but are not limited to: -intelligent robotics, mechatronics, and biomimetics -novel and biologically-inspired robotics -modelling, identification and control of robotic systems -biomedical, rehabilitation and surgical robotics -exoskeletons, prosthetics and artificial organs -AI, neural networks and fuzzy logic in robotics -multimodality human-machine interaction -wireless sensor networks for robot navigation -multi-sensor data fusion and SLAM
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