Hierarchical Motion Planning of AUVs in Three Typical Marine Environments

Abstract

Broad waters, harbor waters, and waterway waters make up more than 90% of autonomous underwater vehicles (AUV) navigation area, and each of them has its typical environmental characteristics. In this paper, a three-layer AUV motion planning architecture was designed to improve the planning logic of an AUV when completing complex underwater tasks. The AUV motion planning ability was trained by the improved deep deterministic policy gradient (DDPG) combined with the experience pool of classification. Compared with the traditional DDPG algorithm, the proposed algorithm is more efficient. Using the strategy obtained from the training and the motion planning architecture proposed in the paper, the tasks of AUVs searching in broad waters, crossing in waterway waters and patrolling in harbor waters were realized in the simulation experiment. The reliability of the planning system was verified in field tests.