Redundancy Resolution for Singularity Avoidance of Wheeled Mobile Manipulators

Mobile robots consist of a mobile platform with manipulator can provide interesting functionalities in a number of applications, since, combination of platform and manipulator causes robot operates in extended work space. The analysis of these systems includes kinematics redundancy that makes more complicated problem. However, it gives more feasibility to robotic systems because of the existence of multiple solutions in a specified workspace. This paper presents a novel combination of evolutionary algorithms and artificial potential field theory for motion planning of mobile manipulator which guaranteed collision and singularity avoidance. In the proposed algorithm, the developed concepts of potential field method are applied to obstacle avoidance and interaction of mobile base with manipulator is used as a new idea for singularity avoidance ability of intermediate links for mobile operations. For this purpose, kinematic and dynamic modeling is derived to define redundant solutions. Afterward, methods from potential field theory combine with evolutionary algorithms to provide an optimum solution among possibly of redundancy resolution scheme. A controller based on dynamic feedback linearization is augmented to track the selective motion trajectory. Simulation results verify obstacle avoidance, singularity avoidance for the manipulators and asymptotic convergence of the robots errors.