Research on Complete Coverage Path Planning Algorithm for Fishing Underwater Robot Based on Improved Gray Wolf Optimization Algorithm

Autonomous underwater vehicles (AUVs) are becoming increasingly important tools for ocean exploration and fishing. The ability to perform complete coverage path planning (CCPP) is a crucial factor in determining the efficiency of AUV fishing. In response to this issue, this paper first establishes a three-dimensional (3-D) grid map of the sea fishing area, and then proposes an improved grey wolf optimization (IGWO) algorithm with multiple improvement strategies. The algorithm takes into account various practical issues such as path length, turn number, height change, coverage rate, and duplication rate, and establishes a multi-constraint fitting function as the evaluation model for the optimal path. Finally, the effectiveness and superiority of the improved algorithm were verified through simulation experiments and comparisons with various algorithms.