Autonomous Obstacle Avoidance for UUV Based on Global Guidance Vector Field and Local Particle Swarm Optimization
Weining Li, Man Wang, Siyu Zhang, Junhao Zhou · 2024
Global trajectory planning and autonomous obstacle avoidance for unmanned underwater vehicles (UUVs) are crucial but difficult in unknown underwater environments. The paper proposes a comprehensive and efficient methodology, consisting of global planning with guidance vector field (GVF) and local autonomous obstacle avoidance with particle swarm optimization (PSO) algorithm. The initial GVF are first constructed based on pre-known currents and static obstacles, to guide an UUV to the destination or tracking a moving target along the shortest trajectory. In order to avoid unknown obstacles, the UUV must be equipped with forward-looking sonars (FLS) or other sensors processed in real time to provide obstacle detection information in the horizontal planes. The local PSO algorithm is introduced to modify the guidance vector field to guide UUV avoiding unknown obstacles safely. Finally, simulation results clearly demonstrate the proposed methodology enables UUVs to safely pass through obstacles in unknown environments.