Trajectory generation for mobile manipulators using a learning method

Abstract

In this paper, the generalized trajectory of a mobile manipulator is obtained. The objective is to allow the end-effector track a given trajectory in a fixed world frame. The motion of the platform and that of the manipulator are coordinated by a pseudo neural network designed from the kinematics model of the system. A learning paradigm is used to produce the required reference variables for each of the mobile platform and the robot manipulator for an overall coordinate behavior. Simulation results are presented to show the effectiveness of the proposed scheme.