MOTION PLANNING FOR INDUSTRIAL MOBILE ROBOTS WITH CLOSED-LOOP STABILITY ENHANCED PREDICTION

Welcome! This website supplements our DSCC 2019 submission, in which we discuss the conditions to enable closed-loop stability and provide an example of a motion planning method with a closed-loop stability enhanced predictor in a MPC framework.

Real-time, safe, and stable motion planning in co-robot systems involving dynamic human robot interaction (HRI) remains challenging due to the time varying nature of the problem. One of the biggest challenges is to guarantee closed-loop stability of the planning algorithm in dynamic environments. Typically, this can be addressed if we have a perfect predictor that can precisely predict the future motions of the obstacles. Unfortunately, a perfect predictor is not possible to achieve. In HRI environments in this paper, human workers and other robots are the obstacles for the motion planner of the ego robot. We discuss necessary conditions for the closed-loop stability of a planning problem using the framework of model predictive control (MPC). It is concluded that the predictor needs to be able to detect the workers’ movement mode change within a time delay allowance and the MPC needs to have a sufficient prediction horizon and a proper cost function. These allow MPC to have closed-loop stability when the worker’s movement is within an uncertainty tolerance, and still avoid collision when the movement is not within the tolerance. Also, the closed-loop performance is investigated using a notion of M-convergence, which guarantees finite local convergence (at least M steps ahead) of the open-loop trajectories toward the closed-loop trajectory. With such notion, we verify the performance of the proposed MPC with environment observation at every MPC time step through simulations and experiments. With the proposed method, the robot can better deal with dynamic environments and closed-loop cost is reduced.