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目前，各种智能算法在机器人路径规划中的应用已经取得了一定的成果，主要表现在路径规划、避障导航和协同控制等方面，以提高机器人在复杂环境中的工作能力、协作能力和工作效率。原始的单个算法如粒子群优化(Particle Swarm Optimization, PSO)算法、蚁群(Ant Colony Optimization, ACO)算法和动态窗口算法(Dynamic Window Approach, DWA)等在机器人路径规划中存在一些问题，如收敛速度较慢、易陷入局部最优解等。因此，许多学者对各种算法做出改进，主要可以归纳为4类：基于搜索的路径规划算法、基于采样的路径规划算法、基于学习的路径规划算法和基于智能仿生的路径规划算法。这些算法不仅可以单独使用，也可以互相结合，以提高算法的适应性、收敛速度和全局搜索等能力。通过对机器人路径规划中的算法进行综述，为相关研究人员了解算法在移动机器人路径规划中的研究及算法应用提供一定的参考。
Abstract：At present, the application of various intelligent algorithms in robot path planning has achieved certain results, mainly in path planning, obstacle avoidance navigation, collaborative control and so on, in order to improve the robot's working ability, collaborative ability and working efficiency in complex environment. However, the original single algorithms such as Particle Swarm Optimization(PSO), Ant Colony Optimization(ACO) and Dynamic Window Approach(DWA) also have some problems in robot path planning, such as slow convergence speed, easy to fall into local optimal solution and so on. Therefore, many scholars have improved various algorithms. These algorithms can be mainly classified into four categories, including search-based path planning algorithm, sampling-based path planning algorithm, learning-based path planning algorithm and intelligent bionic path planning algorithm. These algorithms can not only be used separately, but also be combined with each other to improve the adaptability, convergence speed and global search ability of the algorithms. By summarizing the algorithms in robot path planning, some references are provided for relevant researchers to understand the research and application of algorithms in mobile robot path planning.
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基本信息:
中图分类号:TP242
引用信息:
[1]郭小莹,赵淑苹.机器人路径规划中的算法研究综述[J].无线电工程,2024,54(11):2664-2671.
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