RoboWalk: Explicit Augmented Human-Robot Dynamics Modeling for Design Optimization
This work addresses the challenge of improving assistive exoskeleton design for elderly and ordinary people in daily activities, representing an incremental advancement in human-robot dynamics modeling and optimization.
The paper tackled the problem of undesirable disturbing forces in an under-actuated lower limb exoskeleton (RoboWalk) by developing explicit dynamics models for human and robot, and using optimization to maximize assistive behavior and reduce human joint torques, resulting in elimination of disturbing forces, lower torque demand, and lower weights.
Utilizing orthoses and exoskeleton technology in various applications and medical industries, particularly to help elderly and ordinary people in their daily activities is a new growing field for research institutes. In this paper, after introducing an assistive lower limb exoskeleton (RoboWalk), the dynamics models of both multi-body kinematic tree structure human and robot is derived separately, using Newton's method. The obtained models are then verified by comparing the results with those of the Recursive Newton-Euler Algorithms (RNEA). These models are then augmented to investigate the RoboWalk joint torques, and those of the human body, and also the floor reaction force of the complete system. Since RoboWalk is an under-actuated robot, despite the assistive force, an undesirable disturbing force exerts to the human. So, optimization strategies are proposed to find an optimal design to maximize the assistive behavior of RoboWalk and reduce joint torques of the human body as a result. To this end, a human-in-the-loop optimization algorithm will be used. The solution of this optimization problem is carried out by Particle Swarm Optimization (PSO) method. The designed analysis and the optimization results demonstrate the effectiveness of the proposed approaches, leading to the elimination of disturbing forces, lower torque demand for RoboWalk motors and lower weights.