Research on AUV underwater path planning based on preference-driven interval multi-objective optimization algorithm

This study investigates the autonomous underwater vehicle (AUV) path-planning problem in complex and uncertain marine environments. Considering various factors such as dynamic ocean currents, terrain complexity, and the uncertainty of hazardous locations, an interval number approach is employed to model ocean current parameters and uncertain hazardous areas, thereby transforming uncertainties into interval constraints and formulating an interval multiobjective optimization problem. Based on this framework, the preference interval multiobjective particle swarm optimization algorithm (P-IMO-PSO) is proposed. The proposed algorithm integrates the decisionmaker’s preference information to guide the optimization process. This approach balances navigation time, path safety, and energy consumption while improving iteration efficiency. The results of MATLAB simulation experiments validate the performance of the proposed algorithm under different ocean current models and uncertain environmental conditions. The results show that, compared with the traditional IMO-PSO algorithm, the proposed P-IMO-PSO significantly enhances path-planning efficiency by reducing the mean navigation time interval by 20.85 %, while also optimizing navigation time, mitigating randomness, and accelerating convergence In addition, the paths generated by the proposed algorithm align better with decision-maker (DM) preferences, leveraging ocean currents advantageously while ensuring safety, thereby enhancing AUV navigation efficiency. These advantages highlight the superior applicability and robustness of the proposed method in complex underwater environments.