Optimization-Based Motion Planning in Joint Space for Walking Assistance With Wearable Robot

In this paper, we propose an alternative motion planning method for a wearable robot with a variable stride length and walking speed. Trajectories are planned in a joint space rather than a workspace to avoid an ill-posed problem with no solution in inverse kinematics, and to consider the joint's range of motion, maximum velocity, foot clearance, and backward balance. The joint trajectories are represented by minimum jerk trajectories. Two via-points are assigned, and the parameters (angle and angular velocity) at the via-points are determined by applying an inverted pendulum model or optimization to satisfy the constraints. The fastest gait pattern generated by the proposed algorithm was twice as fast as the pattern generated by the workspace-based planning method. We confirmed that the fastest walking pattern of 0.36 m/s was feasible on a treadmill, and a walking pattern of 0.27 m/s was found for walking across the floor with a walker. Furthermore, the proposed method required approximately 65% of the electric power for the workspace-based method for the same walking speed and stride length. These results suggest that the proposed motion planning method is effective at generating a high-speed and efficient gait pattern for a wearable robot.