Abstract
To address the issues of poor endpoint convergence and suboptimal path quality in global path planning for unmanned surface vessels (USVs), this paper proposes an endpoint-convergence oriented improved genetic algorithm based on AIS data. Firstly, utilizing a genetic algorithm with an endpoint-convergence objective function, a set of paths with strong endpoint convergence is selected. Secondly, to overcome the limitations of a single optimization algorithm, a multi-objective path planning approach is employed using a genetic algorithm, thereby improving the distance and trajectory smoothness in global path planning for unmanned ships. Finally, the planned paths are evaluated by comparing them with the real paths from AIS data. The simulation results demonstrate that the proposed method outperforms other traditional algorithms in terms of average path turn count and turn angle, reducing them by an average of 35.41% and 35.72%, respectively. Moreover, the proposed method exhibits the smallest error compared to the real trajectories, with an average reduction of 18.26%. These results validate the effectiveness and rationality of the proposed approach in improving path quality, reducing path turn count, and achieving better alignment with actual trajectories.
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