Path Planning and Tracking Control for Mobile Robot Based on Improved A* and MPC Algorithms

To enhance the overall performance of autonomous mobile robot in path planning and tracking control, including planning efficiency, path quality, and obstacle avoidance capability, this paper introduces an innovative approach to path planning and tracking control by combining an enhanced A* algorithm with Model Predictive Control (MPC). Primarily, a dynamic weighting factor is incorporated into the conventional A* algorithm to enable adaptive modulation of the cost function. Concurrently, a mechanism for repeated node detection and updating within the Open list is devised to ensure path consistency and effectively reduce redundant node expansions. Subsequently, the initial path is refined using B-spline curve smoothing, thereby enhancing its continuity and feasibility. In the final stage, during path tracking, the MPC algorithm is employed for trajectory control, with Control Barrier Function (CBF) embedded into the control model to facilitate effective obstacle avoidance. Simulation results demonstrate that, in terms of path planning, the proposed improved A* algorithm significantly enhances path generation efficiency and quality, producing shorter, smoother paths with fewer redundant nodes. In terms of path tracking, the incorporation of CBF into the MPC controller improves obstacle avoidance capability against dynamic obstacles while maintaining tracking accuracy. Overall, the proposed method outperforms the traditional A* algorithm and MPC combination in both the path planning and tracking control stages.