Motion Control of Hyper Redundant Robots by Learning Control Based on Linear Combination of Error History and Initial Configuration Optimization with Backward Learning

Abstract

This paper describes the motion control of hyper redundant robots using a learning control scheme based on linear combination of error history. The learning control scheme is formulated with three elements: general solution of inverse kinematics with pseudo inverse of Jacobian matrix to achieve main task, condition to achieve several subtasks and compensation by linear combination of obtained time history of output error. In order to make planar serial manipulator with hyper redundancy achieving main task, tracking of desired trajectory by its output link while performing obstacle avoidance as subtask, several subtask setting schemes to prevent from partially singular configuration caused by interference between main task and subtasks are proposed. The backward learning scheme is also proposed to obtain optimum initial configuration for the proposed learning control scheme. Several simulations and experiments with a planar 10R serial manipulator demonstrate the effectiveness of proposed control scheme.