Path Planning for Autonomous Underwater Vehicle Based on Artificial Potential Field and Modified RRT

Abstract

Autonomous underwater vehicle(AUV) plays an important role in modern aquaculture. For example, it can be applied to monitor water quality as well as the fish biomass. In these activities, path planning is significant for AUV. This paper proposed a path planning algorithm for underwater vehicles based on improved rapidly expanding random tree(RRT). The artificial potential field(APF) algorithm is introduced into the heuristic function of bidirectional RRT to improve the expansion efficiency as a global path. To solve the problem of dynamic obstacles, global path planning and local path planning are combined. The dynamic window method is used in local path planning to deal with dynamic obstacles. This approach takes the robot's speed as the reference point. It sets the window and then selects a better path by the evaluation function. In this way, the path planning of underwater vehicle in a dynamic environment is realized. Finally, the algorithm was simulated in both static and dynamic environments. The results show that the proposed algorithm can provide effective paths for underwater vehicles quickly.