Manipulator motion planning using flexible obstacle avoidance based on model learning

Abstract

Traditional manipulator motion planning methods aim to find collision-free paths. But in highly cluttered environments, it is hard to find available solutions. We present a novel motion planning strategy which integrates the sampling-based path planning algorithm with the flexible obstacle avoidance approach for finding the efficient path through changing poses of movable obstacles. Following the resulting path, the manipulator can push the obstacles away and move to the target simultaneously. For dealing with the safety issue of the interaction between manipulator and obstacles, a learning-based motion modeling method is proposed for motion prediction of the obstacles being pushed by manipulator, and then the trained models are utilized in the motion planning. The results from both simulations and real robot experiments show that the proposed method can generate efficient paths which cannot be solved by traditional method.