| 改进遗传算法与领航跟随法的机器人编队方法 |
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| 引用本文： | 江涛,张志安,程志,李金芝,陆静.改进遗传算法与领航跟随法的机器人编队方法[J].计算机工程与应用,2020,56(3):240-245. |
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| 作者姓名： | 江涛 张志安 程志 李金芝 陆静 |
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| 作者单位： | 南京理工大学 机械工程学院，南京 210094 |
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| 摘 要： | 针对传统遗传算法在求解机器人路径规划问题时存在的收敛速度慢、路径不平滑问题，对其进行了改进，在适应度函数中加入了路径平滑度因素，选择操作时平滑度较好的路径更容易被选中。在种群选择时将最优个体直接复制到下一代，有效地保留了父代优良基因。在领航机器人规划路径阶段，使用改进的遗传算法为领航机器人规划出一条安全无碰撞且平滑度较好的最优路径。在跟随机器人跟随阶段，使用领航跟随法控制每一个跟随机器人使其与领航者保持特定的距离与角度，从而形成设定的队形。最后通过MATLAB软件建立栅格地图进行仿真，验证了该算法的可行性，与传统遗传算法相比，改进遗传算法收敛速度更快，且路径更加平滑。
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| 关 键 词： | 多机器人 编队控制 遗传算法 领航跟随法 |
Robot Formation Method with Improved Genetic Algorithm and Leader-Follower |
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| JIANG Tao,ZHANG Zhi’an,CHENG Zhi,LI Jinzhi,LU Jing.Robot Formation Method with Improved Genetic Algorithm and Leader-Follower[J].Computer Engineering and Applications,2020,56(3):240-245. |
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| Authors: | JIANG Tao ZHANG Zhi’an CHENG Zhi LI Jinzhi LU Jing |
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| Affiliation: | School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China |
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| Abstract: | To solve the problems of slow convergence speed and non-smooth path that exist in traditional genetic algorithm when solving robot path planning problem,the following improvements are made,the factor of path smoothness is added into the fitness function,and the path with better smoothness is easier to be selected.In population selection,the best individuals are directly copied to the next generation,effectively preserving the good genes of the father generation.In the stage of path planning for leader robot,the improved genetic algorithm is used to plan an optimal path without collision and with good smoothness.In the following stage of follower robot,the leader-follower method is used to control each following robot to maintain a specific distance and angle from the pilot,so as to form a set formation.Finally,a raster map is established by MATLAB for simulation,which verifies the feasibility of the algorithm.Compared with the traditional genetic algorithm,the improved genetic algorithm has faster convergence speed and smoother path. |
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| Keywords: | multi-robot formation control genetic algorithm leader-follower |
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