Safe and Robust Human Following for Mobile Robots Based on Self-Avoidance MPC in Crowded Corridor Scenarios

The robot could provide convenient service for humans by following them. But it is not easy for the robot to follow a person while avoiding various objects in real crowded corridors. In this paper, a self-avoidance model predictive controller (SA-MPC) for human following in crowded environments is proposed. An obstacle avoidance optimization item is designed to enable the generalized controller to avoid collisions. Another adaptive method of selecting waypoints is introduced to make the robot move at the specific linear and angular velocities. We verified our method on the pedestrian simulation platform. Qualitative and quantitative simulations demonstrate that the proposed SA-MPC achieves a higher scene pass rate and improves safety and robustness in dense environments. Simulations are implemented in dense corridors and complex scenarios with random obstacles. SA-MPC can serve as a generalized controller for various mobile robots.