AUV Local Path Planning Based on an Improved DWA Algorithm in Complex Dynamic Environments
Bo Liu, Haichuan Zhang, Muxin Nian, Hong Ouyang, Shuyi Xu · 2025
The ocean is a critical resource for human development, with Autonomous Underwater Vehicles (AUVs) playing a key role in underwater exploration. Traditional global path planning methods fall short in dynamic environments, highlighting the need for effective local path planning. This paper proposes a novel Optimized Dynamic Window Approach (NODWA) to overcome limitations of the conventional DWA, which struggles with fixed parameters and poor adaptability to moving obstacles. NODWA introduces relative velocity and limiting distance, incorporating motion information of both the AUV and obstacles to enhance dynamic obstacle avoidance. It also considers heading deviation and target distance to improve motion guidance and path optimization. Additionally, an energy consumption evaluation function is designed to support energy-efficient navigation in dynamic ocean currents. Simulation results confirm that NODWA significantly improves efficiency and path quality compared to the traditional DWA in dynamic scenarios.