Artificial Intelligence Path Planning for Marine Environment

Abstract

Artificial intelligence path planning and its optimization method is an important topic of the times, but the research of path planning for marine environment is less. Under the background of the popularization of offshore operation platform and the urgent development of marine resources, it is very important to analyze the artificial intelligence marine path planning and study the multi strategy path planning optimization algorithm. In view of this, this study proposes a multi strategy optimization ant colony algorithm (ACA-Mso), and realizes the artificial intelligence path planning of marine environment by updating the population pheromone strategy, implementing the preview strategy of local forced exchange optimization, and the symmetric path optimization strategy. Its main advantage is that it not only overcomes the shortcomings of the basic ant colony algorithm, such as long convergence time, too many iterations of the optimal solution, and the limitations of fuzzy symmetric path selection rules, but also ensures the enhancement of pheromone among nodes, the optimization of local search path potential, and the improvement of search ability of path planning algorithm, so as to realize the comprehensive path planning of marine environment Optimization.