Area Complete Coverage Path Planning for Offshore Seabed Organisms Fishing Autonomous Underwater Vehicle Based on Improved Whale Optimization Algorithm
Han Bao, Yanyan Wang, Haitao Zhu, Dian Wang
Abstract
Autonomous underwater vehicles (AUVs) can replace human beings in seabed organisms fishing in complex and dangerous ocean environments, with high fishing efficiency and economic benefits. Therefore, the design of a reasonable area complete coverage path planning (CCPP) algorithm is the key technology for realization of autonomous fishing. To address this problem, this article proposes a new generalized complete coverage path planner. First, this planner discretizes the seabed topography of the fishing area and establishes a 2-D grid map model containing height information. Second, a global path planning algorithm based on the improved whale optimization algorithm (IWOA) is introduced, and a local path optimization is performed for the turning portion of the planned path based on the dynamics and fishing characteristics of offshore fishing AUV. Then, for the engineering practical problem that offshore fishing AUVs have limited self-contained energy and difficulty in energy replenishment, an adaptation fitness function with multiple constraints is established as the evaluation model of the optimal path by considering the length of the navigation path, turning angle, altitude change, as well as coverage and repetition rate. Finally, the performance of the proposed CCPP algorithm is simulated and verified.