Path planning for underwater robots by integrating an improved A-star algorithm with the APF algorithm

Path planning algorithms for Autonomous Underwater Vehicles (AUVs) often suffer from low efficiency, long planning times, poor dynamic obstacle avoidance, and unsmooth trajectories. To address these challenges, this paper proposes a hybrid approach that integrates an improved A-star algorithm with an enhanced artificial potential field (APF) algorithm. The proposed method incorporates AUV kinematic constraints. By dynamically adjusting the weights in the A-star evaluation function, planning time is reduced. To prevent local minima, a goal-regulating factor is added to the APF model. Furthermore, a velocity-based repulsive function is integrated to enable effective dynamic obstacle avoidance. For path smoothing, cubic uniform B-spline curves are applied to eliminate sharp turns, improving path smoothness and feasibility. Simulation results demonstrate that the proposed hybrid algorithm can plan superior paths in complex environments. The generated trajectories are more efficient and smoother and better aligned with AUV motion characteristics.