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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.


Robust global route planning for an AUV in a stochastic environment

Author(s):  Jiaxin ZHANG, Meiqin LIU, Senlin ZHANG, Ronghao ZHENG

Affiliation(s):  State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   zhangjiaxin@zju.edu.cn, liumeiqin@zju.edu.cn, slzhang@zju.edu.cn, rzheng@zju.edu.cn

Key Words:  Autonomous underwater vehicle (AUV), Route planning, Genetic algorithm (GA), Orienteering problem (OP), Stochastic path cost

Jiaxin ZHANG, Meiqin LIU, Senlin ZHANG, Ronghao ZHENG. Robust global route planning for an AUV in a stochastic environment[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

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%A Jiaxin ZHANG
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%I Zhejiang University Press & Springer
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A1 - Jiaxin ZHANG
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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), this paper proposes the greedy strategy-based GA, which includes a novel rebirth operator that maps infeasible individuals into the feasible solution space during evolution to improve the efficiency of the optimization, and a differential evolution planner is used to provide the deterministic local path costs. 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 the local paths. Monte Carlo experiments are used to verify the superiority and effectiveness of the planner. The promising simulation results show that the proposed greedy strategy-based GA (GGA) outperforms its counterparts by 4.7%–24.6%, and the sampling-based GGA route planner (S-GGARP) improves the average profit by 5.8%.

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