运动规划
蚁群优化算法
适应性
计算机科学
趋同(经济学)
弹道
路径(计算)
蚁群
数学优化
人工智能
机器人
数学
生态学
物理
天文
经济
生物
程序设计语言
经济增长
作者
Shengchao Su,Xiang Ju,Chaojie Xu,Yufeng Dai
出处
期刊:IEEE Transactions on Intelligent Transportation Systems
[Institute of Electrical and Electronics Engineers]
日期:2023-03-07
卷期号:25 (3): 2792-2802
被引量:7
标识
DOI:10.1109/tits.2023.3250756
摘要
To improve the online collaboration and planning capabilities between autonomous vehicles, this paper proposes a novel collaborative motion planning method. In this method, the ant colony algorithm was introduced and improved to achieve collaborative motion planning for multiple autonomous vehicles. First, independent subpopulations of the same size were generated according to the number of autonomous vehicles. Then, a multi-objective optimization function was established to optimize spatial collaboration and trajectory costs, and to update the pheromone in the ant colony algorithm. Meanwhile, the evaporation coefficient in the algorithm was adaptively adjusted to enhance the global search ability and improve the convergence speed of the algorithm. Finally, a feasible path was planned for each autonomous vehicle based on the path of each subpopulation. Simulation results show that the proposed method is effective and it can achieve stronger adaptability than the artificial potential field motion planning algorithm.
科研通智能强力驱动
Strongly Powered by AbleSci AI