电子测量与仪器学报

李志锟,黄宜庆,徐玉琼.改进变步长蚁群算法的移动机器人路径规划[J].电子测量与仪器学报,2020,34(8):15-21

改进变步长蚁群算法的移动机器人路径规划

Path planning of mobile robot based on improved variable step size ant colony algorithm

DOI：

英文关键词:variable step size ant colony algorithm pheromone path planning

基金项目:国家自然科学基金(61572032)、安徽省高校自然科学研究重点项目(KJ2018A0110)、安徽工程大学研究生教育创新基金、安徽工程大学中青年拔尖人才项目(2016BJRC004)资助

作者	单位
李志锟	1. 安徽工程大学电气工程学院，2. 高端装备先进感知与智能控制教育部重点实验室
黄宜庆	1. 安徽工程大学电气工程学院，2. 高端装备先进感知与智能控制教育部重点实验室
徐玉琼	1. 安徽工程大学电气工程学院，2. 高端装备先进感知与智能控制教育部重点实验室

Author	Institution
Li Zhikun	1. College of Electrical Engineering, Anhui University of Engineering,2. Provincial Key Laboratory of Detection Technology and Energy Saving Devices
Huang Yiqing	1. College of Electrical Engineering, Anhui University of Engineering,2. Provincial Key Laboratory of Detection Technology and Energy Saving Devices
Xu Yuqiong	1. College of Electrical Engineering, Anhui University of Engineering,2. Provincial Key Laboratory of Detection Technology and Energy Saving Devices

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中文摘要:

针对移动机器人在蚁群算法路径规划中存在陷入局部收敛且无法做到路径最优的问题,提出了改进变步长蚁群算法, 使其能够在收敛迭代次数较少的情况下做到路径最优。针对蚁群算法应用在路径规划中的相关特性,优化信息素分配,降低局部信息素含量对算法的影响,避免蚁群在搜索路径时陷入局部最优,在转移概率公式中增加权重因子,提高移动机器人朝着终点方向移动的概率,有效减少蚁群收敛迭代次数,改变移动机器人移动步长,使其能在 360°内自由无碰撞移动,有效缩短路径长度。仿真结果表明,在简单环境下,改进变步长蚁群算法的收敛迭代次数及最优路径长度分别为 2 次及 28. 042 m,传统蚁群算法的收敛迭代次数及最优路径长度分别为 25 次及 29. 213 m;在复杂环境下,改进变步长蚁群算法的收敛迭代次数及最优路径长度分别为 2 次及 43. 960 2 m,改进势场蚁群算法的收敛迭代次数及最优路径长度分别为 16 次及 45. 112 7 m。仿真结果验证了改进变步长蚁群算法的有效性和优越性。

英文摘要:

In order to solve the problem that mobile robots fall into local convergence and cannot achieve the optimal path in the path planning of ant colony algorithm, this paper proposes an improved variable-step ant colony algorithm to enable it to achieve the path with fewer convergence iterations optimal. According to the relevant characteristics of ant colony algorithm that applied in path planning, it optimizes the allocation of pheromone, reduces the impact of local pheromone content on the algorithm, avoids the ant colony from falling into the local optimum when searching the path, adds the weighting factor in the transition probability formula and increases the probability of the mobile robot moving in the direction of the end point, it effectively reduces the number of ant colony convergence iterations, changes the mobile robot’s moving step length, enables it to move freely and without collision within 360 °, and effectively shortens the path length. The simulation results show that: in the simple environment, the convergent iteration times and the optimal path length of the improved variable step ant colony algorithm are 2 times and 28. 042 m respectively, while the convergent iteration times and the optimal path length of the traditional ant colony algorithm are 25 times and 29. 213 m respectively. In the complex environment, the convergence iteration times and the optimal path length of the improved variable step ant colony algorithm are 2 times and 43. 960 2 m respectively, and the convergence iteration times and the optimal path length of the improved potential field ant colony algorithm are 16 times and 45. 112 7 m respectively. The simulation results demonstrate the effectiveness and superiority of the improved variable step ant colony algorithm.

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