An on-line neural network-based approach to dynamic path planning and coordination of two robot arms

We present an on-line decentralized approach to collision-free path planning for two robot arms. During the real-time planning, each arm represents a dynamic obstacle for another one, which allows to treat the motion of the latter, as well as the motion of other objects in the workspace in a unified fashion. The motion for each arm is planned independently, and the only information which is "shared" is the intended configuration of each robot. The approach relies therefore exclusively on the dynamic, explorative path generation, which is performed using the dynamic wave expansion neural network. Our simulative experiments for the case of two robot arms with 3-DOFs in 3D reveal that the proposed approach, without any complicated heuristics, any priority assignment, and any global optimization of an objective cost function, is capable of producing feasible paths "on-the-fly". The robustness and efficiency of the method are demonstrated statistically through a number of random tests.