Path Planning of Multi-Objective Underwater Robot Based on Improved Sparrow Search Algorithm in Complex Marine Environment

Autonomous underwater vehicle (AUV) path planning in complex marine environments meets many chanllenges, such as influencing factors, models and the performance of optimization algorithm to be improved. To find a with minimum cost actual environmental threats, navigation height changes energy consumption AUV, an improved sparrow search is designed under impact time-varying characteristics current environment on AUV physical constraints movement. In proposed algorithm, adaptive weight factor balance strategy introduced into position update discoverer improve convergence speed ability. follower position, variable spiral based formula enhance local jumping ability algorithm. After update, Levy flying added, Cauchy–Gaussian mutation performed optimal individual increase population diversity, which improves anti-stagnation Finally, global sense multi-objective weighting obtained. The simulation results show that can plan less quickly converge optimized point, finally it meet requirements when navigating sea areas.