Abstract
Collision avoidance technology is very important in the research of unmanned ship path planning. Aiming at the problem that the existing global path planning algorithm of unmanned vehicle is easy to fall into the local optimal solution and the target point is unreachable, a path planning algorithm based on improved artificial potential field was designed. According to the route requirement of USVS, the power function of target distance change is introduced to improve the repulsive field model. The dangerous target points are screened by using the target point judgment method based on the safe arrival radius. Finally, by dynamically adjusting the artificial potential field coefficient, the unmanned vehicle can jump out of the local optimal trap. Based on the theoretical research, the simulation experiment of path planning is designed. The simulation results show that the algorithm can jump out of the local minimum point trap, and the path planned by the algorithm can successfully reach the target point under the condition of multiple obstacles.
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You, Y., Chen, K., Zhang, Y., Feng, J., Huang, Y. (2023). Research on Global Collision Avoidance Algorithm for Unmanned Ship Based on Improved Artificial Potential Field 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_28
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DOI: https://doi.org/10.1007/978-981-99-1549-1_28
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