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融合Q学习算法和人工势场算法的无人机航迹规划方法

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针对基于Q学习算法规划出的航线存在与静态障碍物发生碰撞危险的问题,提出融合Q学习算法和人工势场算法的航迹规划方法。该方法首先利用Q学习算法规划出一条航线,其次根据地图统计该航线每个航段内包含的障碍物,最后对每个包含障碍物的航段采用改进的人工势场法进行重新规划。实验结果显示,提出的融合方法能够在牺牲少量轨迹长度和时间的情况下,得到与静态障碍物避免发生碰撞的最短路径。
UAV Route Planning Method Based on Fusion of Q-learning Algorithm and Artificial Potential Field Algorithm
Aiming at the problem of collision with static obstacles when using a flight route planned by Q-learning algorithm,a UAV route planning method based on fusion of Q-learning algorithm and artificial potential field algorithm is proposed.The method first uses the Q-learning algorithm to plan a route,then counts the obstacles contained in each flight leg of the route according to the map,and finally applies the improved artificial potential field method to re-plan the flight leg containing obstacles.The experimental results show that the proposed fusion method can plan the shortest route to avoid collision with static obstacles at the cost of a small amount of time and trajectory length.

route planningQ-learning algorithmartificial potential fieldUAV

刘冬、余文泉、霍文健、李瑞、姜伟月

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北方自动控制技术研究所,太原 030006

航迹规划 Q学习算法 人工势场 无人机

2024

火力与指挥控制
火力与指挥控制研究会,火力与指挥控制专业情报网

火力与指挥控制

CSTPCD北大核心
影响因子:0.312
ISSN:1002-0640
年,卷(期):2024.49(2)
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