An Autonomous Robot for Collision-Free Person Following through Model Predictive Control

Abstract

A crucial aspect of human-robot integration is the implementation of person-following robots. However, autonomous robots continue to face challenges in tracking individuals within complex environments with dynamic obstacles. Traditional approaches to combining global and local planning are inadequate for this task due to the high level of uncertainty in the environment and the flexible behavior of the following target. To address these challenges, this paper proposes a framework for motion planning for wheeled robots. This method integrates dynamic obstacle avoidance and dynamic target following into an optimization problem based on model predictive control (MPC) with terminal constraint set to guarantee the safety of the following task in dynamic obstacle environments. Therefore, in this paper, a high-frequency state estimator is used to predict human behavior, and a spring model is used to model dynamic obstacles in the environment to keep the robot away from the obstacles. The effect of the person following is thoroughly tested in simulation with multiple scenarios and the comparison experiment, verifying the real-time effectiveness of the method.