A fast collision-free motion planning method for underactuated robots based on genetic algorithm

A new approach of fast collision-free motion planning for underactuated robots based on genetic algorithm is proposed. The collision avoidance problem is formulated and solved as a position-based force control problem. Virtual generalized force representing the intrusion of the arm into the obstacle dangerous zone is computed in real time using a virtual spring-damper model. The partly stable controllers are adopted and the energy based fitness function is built, then the best switching sequence of partly stable controllers is obtained by genetic algorithm. Because the proposed method does not make any hypothesis about the degree of freedom, it can be used without modification for arms with a large number of degree of freedom. At last, numerical simulations which are carried on the planar 3R underactuated robots show the effectiveness of the proposed method.