Ma Tian, Lyu Jiahao, Yang Jiayi, Xi Runtao, Li Yuancheng, An Jinpeng, Li Chao
College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, China.
Sensors (Basel). 2022 Aug 8;22(15):5910. doi: 10.3390/s22155910.
How to generate the path planning of mobile robots quickly is a problem in the field of robotics. The Q-learning(QL) algorithm has recently become increasingly used in the field of mobile robot path planning. However, its selection policy is blind in most cases in the early search process, which slows down the convergence of optimal solutions, especially in a complex environment. Therefore, in this paper, we propose a continuous local search Q-Learning (CLSQL) algorithm to solve these problems and ensure the quality of the planned path. First, the global environment is gradually divided into independent local environments. Then, the intermediate points are searched in each local environment with prior knowledge. After that, the search between each intermediate point is realized to reach the destination point. At last, by comparing other RL-based algorithms, the proposed method improves the convergence speed and computation time while ensuring the optimal path.
如何快速生成移动机器人的路径规划是机器人技术领域的一个问题。Q学习（QL）算法最近在移动机器人路径规划领域的应用越来越广泛。然而，在早期搜索过程中，其选择策略在大多数情况下是盲目性的，这减缓了最优解的收敛速度，尤其是在复杂环境中。因此，在本文中，我们提出了一种连续局部搜索Q学习（CLSQL）算法来解决这些问题，并确保规划路径的质量。首先，将全局环境逐步划分为独立的局部环境。然后，利用先验知识在每个局部环境中搜索中间点。之后，实现各中间点之间的搜索以到达目标点。最后，通过与其他基于强化学习的算法进行比较，该方法在确保最优路径的同时提高了收敛速度和计算时间。
