Similar articles

Abstract: 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%.