Parallel Interacting Multiple Model-Based Human Motion Prediction for Motion Planning of Companion Robots

Donghan Lee, Chang Liu, Yi-Wen Liao, J. Karl Hedrick

Abstract: We propose in this paper an autonomous motion planning framework for companion robots to accompany humans in a socially desirable manner, which takes safety and comfort requirements into account. The overall framework consists of two parts: first, a novel parallel interacting multiple model-unscented Kalman filter (PIMM-UKF) approach is developed to simultaneously estimate human motion states and model mismatch, and then systematically predict the position and velocity of the human for a finite horizon. Second, based on the predicted human states, a nonlinear model predictive control (MPC) technique is utilized for the robot motion planning. The simulation results have demonstrated the superior performance in prediction using the PIMM-UKF approach. The effectiveness of the MPC planner is also shown by successfully facilitating the socially desirable companion behavior.