Obstacle Avoidance for Redundant Nonholonomic Mobile Modular Manipulators via Neural Fuzzy Approaches

Abstract. This paper addresses an obstacle avoidance issue for redun-dant nonholonomic mobile modular manipulators. On the basis of mod-ular robot concept, an integrated dynamic modeling method is proposed, which takes both the mobile platform and the onboard modular manip-ulator as an integrated structure. A new obstacle avoidance algorithm is proposed which is mainly composed of two parts: a self-motion plan-ner (SMP) and a robust adaptive neural fuzzy controller (RANFC). One important feature of this algorithm lies in that obstacles are avoided by online adjusting self-motions so that the end-effector task will not be affected unless the obstacles are just on the desired trajectory. The RANFC does not rely on exact aprior dynamic parameters and can sup-press bounded external disturbance effectively. The effectiveness of the proposed algorithm is verified by simulations.