Path planning for Ackermann-steered robots based on bidirectional A* and timed elastic band algorithms

Abstract To address the issues of path roughness, low search efficiency, and the difficulty of simultaneously considering Ackermann vehicle kinematic constraints in practical path planning, this study developed a coordinated global–local path planning framework tailored for Ackermann-steered robots. In the global planning stage, a bidirectional A* search framework was enhanced with a dynamic heuristic function, improving the geometric quality of the global path while maintaining search efficiency. Path smoothing was applied to further align the path with Ackermann kinematic constraints. In the local planning stage, the Timed Elastic Band (TEB) algorithm was adapted for Ackermann steering by incorporating minimum turning radius and steering-related constraints, ensuring that the generated trajectories were both collision-free and executable. Python-based simulations on constructed maps were used to compare different heuristic strategies in terms of path length and search efficiency. Results showed that the proposed global planning strategy reduced path length by 34.62% compared with the traditional bidirectional A* algorithm and improved search efficiency by approximately 2.1%. Furthermore, Robot Operating System (ROS) simulations and real-robot tests were conducted to analyze curvature characteristics, velocity variations, and path-tracking performance under different global–local planning combinations. The experimental results demonstrated that the proposed planning framework offered strong engineering practicality and provided a feasible solution for real-world Ackermann-steered robots.