Robust Trajectory Tracking Control for Multiple Mobile Robots

Abstract

Abstract This paper addresses the robust trajectory tracking control challenge for multiple mobile robots in complex environments, an increasingly critical issue as the number of robots grows and the demand for high tracking accuracy and efficiency increases. Existing methods are unable to strike a balance between safety and tracking precision in multi-robot trajectory tracking, with the requirement that robots should be as close as possible to their designated positions at all times during tracking. To bridge these gaps, we introduce Multi Mobile Robot Trajectory Model Predictive Control (MMRT-MPC) and the Trajectory Action Dependence Graph (TADG) framework. MMRT-MPC incorporates multiple indicators into the cost function to improve trajectory tracking accuracy and efficiency. Meanwhile, TADG ensures safety during trajectory tracking and is compatible with MMRT-MPC as well as other control algorithms. Simulations in Gazebo show that the TADG method ensures the safety of trajectory tracking control. Compared with applying TADG to Prioritized Trajectory Optimization (PTO) and Bellman Dynamic Programming with Model Predictive Control (BDP-MPC), MMRT-MPC+TADG reduces average delay by 17.7% and 11.6% respectively under different numbers of robots, and by 20.8% and 14.3% in the case of 30 robots with random delays added. Furthermore, the validity of our proposed method is confirmed through real-world experimental results.