Abstract
At present, the algorithms used in local path planning of unmanned ships mainly include simulated annealing algorithm, artificial potential field method and genetic obstacle is above the globally planned algorithm. Among them, genetic algorithm has strong spatial search ability and strong adaptive ability. However, due to the low efficiency of the traditional genetic algorithm, it cannot meet the needs of the real-time path planning of unmanned ships. To solve this problem, this paper designs an improved genetic algorithm based on dynamic fitness function to make up for the shortcomings of the traditional genetic algorithm. This method can improve genetic manipulation by guiding the direction of population evolution optimization and meet the needs of unmanned ship obstacle avoidance. Aiming at the complex dynamic environment in the local path planning of unmanned ship, a simulation experiment combining static environment and dynamic environment is designed. Simulation results show that the improved algorithm has a better obstacle avoidance effect in dynamic environment.
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Acknowledgement
This work was supported by National Nature Science Foundation of China (62076249), Key Research and Development Plan of Shandong Province (2020CXGC010701, 2020LYS11), and Natural Science Foundation of Shandong Province (ZR2020MF154).
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Liu, Y. et al. (2023). Local Path Planning Algorithm Designed for Unmanned Surface Vessel Based on Improved Genetic Algorithm. In: Pan, L., Zhao, D., Li, L., Lin, J. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2022. Communications in Computer and Information Science, vol 1801. Springer, Singapore. https://doi.org/10.1007/978-981-99-1549-1_3
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DOI: https://doi.org/10.1007/978-981-99-1549-1_3
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