Design, Development, and Control of an Assistive Robotic Exoskeleton Glove Using Reinforcement Learning-Based Force Planning for Autonomous Grasping

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Virginia Tech


This dissertation presents a comprehensive exploration encompassing the design, development, control and the application of reinforcement learning-based force planning for the autonomous grasping capabilities of the innovative assistive robotic exoskeleton gloves. Exoskeleton devices have emerged as a promising avenue for providing assistance to individuals with hand disabilities, especially those who may not achieve full recovery through surgical interventions. Nevertheless, prevailing exoskeleton glove systems encounter a multitude of challenges spanning design, control, and human-machine interaction. These challenges have given rise to limitations, such as unwieldy bulkiness, an absence of precise force control algorithms, limited portability, and an imbalance between lightweight construction and the essential functionalities required for everyday activities. To address these challenges, this research undertakes a comprehensive exploration of various dimensions within the exoskeleton glove system domain. This includes the intricate design of the finger linkage mechanism, meticulous kinematic analysis, strategic kinematic synthesis, nuanced dynamic modeling, thorough simulation, and adaptive control. The development of two distinct types of series elastic actuators, coupled with the creation of two diverse exoskeleton glove designs based on differing mechanisms, constitutes a pivotal aspect of this study. For the exoskeleton glove integrated with series elastic actuators, a sophisticated dynamic model is meticulously crafted. This endeavor involves the formulation of a mathematical framework to address backlash and the subsequent mitigation of friction forces. The pursuit of accurate force control culminates in the proposition of a data-driven model-free force predictive control policy, compared with a dynamic model-based force control methodology. Notably, the efficacy of the system is validated through meticulous clinical experiments. Meanwhile, the low-profile exoskeleton glove design with a novel mechanism engages in a further reduction of size and weight. This is achieved through the integration of a rigid coupling hybrid mechanism, yielding pronounced advancements in wearability and comfortability. A deep reinforcement learning approach is adopted for the real-time force planning control policies. A simulation environment is built to train the reinforcement learning agent. In summary, this research endeavors to surmount the constraints imposed by existing exoskeleton glove systems. By virtue of advancing mechanism design, innovating control strategies, enriching perception capabilities, and enhancing wearability, the ultimate goal is to augment the functionality and efficacy of these devices within the realm of assistive applications.



Wearable Devices, Mechanical Design, Kinematics, Dynamics and Control, exoskeleton, Motion Planning, Reinforcement Learning