Evolvable Motion-planning Method using Deep Reinforcement Learning

A motion-planning method that can adapt to changes in the surrounding environment is proposed and evaluated. Automation of work is progressing in factories and distribution warehouses due to labor shortages. However, utilizing robots for transport operations in a distribution warehouse faces a problem; that is, tasks for setting up a robot, such as adjustment of acceleration for stabilization of the transportation operation, are time consuming. To solve that problem, we developed an "evolvable robot motion-planning method." The aim of this method is to reduce the preparation cost by allowing the robot to automatically learn the optimized acceleration according to the weight and center of gravity of the objects to be transported. It was experimentally demonstrated that the proposed method can learn the optimized acceleration control from time-series data such as sensor information. The proposed method was evaluated in a simulator environment, and the results of the evaluation demonstrate that the learned model reduced the inertial force due to the acceleration of robot motion and shortened the transport time by 35% compared with the conventional method of manual adjustment. The proposed method was also evaluated in a real machine environment, and the evaluation results demonstrate that the method can be applied to a real robot. Since the speed of the robot does not need to be adjusted in the case of the proposed method, the adjustment man-hours can be reduced.