Reachability-Aware Collision Avoidance for Tractor-Trailer System with Non-Linear MPC and Control Barrier Function

This paper proposes a reachability-aware model predictive control with a discrete control barrier function for backward obstacle avoidance for a tractor-trailer system. The framework incorporates the state-variant reachable set obtained through sampling-based reachability analysis and symbolic regression into the objective function of model predictive control. By optimizing the intersection of the reachable set and iterative non-safe region generated by the control barrier function, the system demonstrates better performance in terms of safety with a constant decay rate, while enhancing the feasibility of the optimization problem. The proposed algorithm improves real-time performance due to a shorter horizon and outperforms the state-of-the-art algorithms in the simulation environment and on a real robot.