摘要
首先,针对传统人工势场算法存在死锁及局部路径欠优等问题,对其进行改进。利用障碍物检测算法识别出有效障碍物和有效路径中间点,通过引力场和边界条件规划出起点到中间点的局部路径,将中间点置为新的起点进行反复迭代,直至起点与目标点重合则规划完成。其次,针对蚁群算法容易陷入局部最优以及收敛速度较慢等问题,对其进行改进。以改进人工势场算法规划出的路径启发蚁群进行路径搜索,从而避免算法早期由于盲目搜索而导致的路径交叉及收敛速度慢等问题,同时以收敛次数构建负反馈通道,使全局信息素和局部信息素的更新速率跟随收敛次数的变化自适应调节,从而保证了算法全程中收敛速度与全局搜索能力的协调与统一。最后,在Matlab中对本文算法、基本蚁群算法以及文献[23]所述算法分别进行仿真实验。结果表明:在相同的环境模型下,本文算法的收敛速度和搜索能力均优于另两种算法;在给定的简单环境模型下进行路径规划时,本文算法的迭代次数为3次,运行时间为0. 892 s,最优路径长度为28. 627 m;在给定的复杂环境模型下进行路径规划时,本文算法的迭代次数为8次,运行时间为3. 376 s,最优路径长度为31. 556 m,所寻路径对环境的覆盖率为73. 63%。
Addressing the problems of deadlock and poor local path in traditional artificial potential field algorithm, some improvement measures were put forward. The obstacle detection algorithm was used to identify one effective obstacle and one intermediate point. Then a local path from starting point to the intermediate point was planed according to the gravitational field and boundary conditions. Setting the intermediate point to a new starting point and repeating this process until each local path was planed one by one. Secondly, aiming at the disadvantage of slow convergence rate and easy to fall into local optimum in basic ant colony algorithm, some improvements were proposed. The result of artificial potential field algorithm was used to build heuristic information of ant colony, so as to avoid the problems of path crossover and slow convergence. At the same time, a negative feedback loop was built to adaptively adjust the renewal speed of global pheromone and local pheromone through the iteration number. Finally, simulation experiment on three different algorithms was conducted. The results showed that under the same environment model, the proposed algorithm had fewer iterations, shorter running time and better global search ability than other two algorithms. In the given simple environment model, the iteration times of the algorithm was 3, the running time was 0.892 s, and the optimal path length was 28.627 m. In the given complex environment model, the iteration was 8 times, the running time was 3.376 s, the optimal path length was 31.556 m, and the global coverage of paths was 73.63%.
作者
张强
陈兵奎
刘小雍
刘晓宇
杨航
ZHANG Qiang;CHEN Bingkui;LIU Xiaoyong;LIU Xiaoyu;YANG Hang(College of Engineering and Technology,Zunyi Normal College,Zunyi 563006,China;State Key Laboratory of Mechanical Transmission,Chongqing University,Chongqing 400044,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2019年第5期23-32,42,共11页
Transactions of the Chinese Society for Agricultural Machinery
基金
贵州省科技计划项目(黔科合LH字[2016]7004号
黔科合LH字[2017]7081号
黔科合LH字[2017]7082号)
贵州省教育厅项目(黔教合KY字[2016]254号)
关键词
移动机器人
路径规划
人工势场
蚁群算法
负反馈
mobile robot
path planning
artificial potential field
ant colony algorithm
negative feedback
作者简介
张强(1989—),男,讲师,主要从事智能机器人控制研究,E-mail:fyzqiang@126.com.
