This paper describes a route planner that enables an autonomous underwater vehicle to selectively complete part of the predetermined tasks in the operating ocean area when the local path cost is stochastic. The problem is formulated as a variant of the orienteering problem. Based on the genetic algorithm (GA), we propose the greedy strategy based GA (GGA) which includes a novel rebirth operator that maps infeasible individuals into the feasible solution space during evolution to improve the e?ciency of the optimization, and use a differential evolution planner for providing the deterministic local path cost. The uncertainty of the local path cost comes from unpredictable obstacles, measurement error, and trajectory tracking error. To improve the robustness of the planner in an uncertain environment, a sampling strategy for path evaluation is designed, and the cost of a certain route is obtained by multiple sampling from the probability density functions of local paths. Monte Carlo simulations are used to verify the superiority and effectiveness of the planner. The promising simulation results show that the proposed GGA outperforms its counterparts by 4.7％–24.6％ in terms of total profi t, and the sampling-based GGA route planner (S-GGARP) improves the average profi t by 5.5％compared to the GGA route planner (GGARP).

State Key Laboratory of Industrial Control Technology,Zhejiang University,Hangzhou 310027,China;College of Electrical Engineering,Zhejiang University,Hangzhou 310027,China;Institute of Artificial Intelligence and Robotics,Xi'an Jiaotong University,Xi'an 710049,China

[1]Jiaxin ZHANG;Meiqin LIU;Senlin ZHANG;Ronghao ZHENG-.Robust global route planning for an autonomous underwater vehicle in a stochastic environment)[J].信息与电子工程前沿（英文）,2022(11):1658-1672

orienteering,rebirth,profi,GGARP

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