Wen Shiguang, Jiang Yufan, Cui Ben, Gao Ke, Wang Fei
Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110169, China.
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China.
Sensors (Basel). 2022 Mar 18;22(6):2367. doi: 10.3390/s22062367.
In this paper, a novel path planning algorithm with Reinforcement Learning is proposed based on the topological map. The proposed algorithm has a two-level structure. At the first level, the proposed method generates the topological area using the region dynamic growth algorithm based on the grid map. In the next level, the Multi-SARSA method divided into two layers is applied to find a near-optimal global planning path, in which the artificial potential field method, first of all, is used to initialize the first Q table for faster learning speed, and then the second Q table is initialized with the connected domain obtained by topological map, which provides the prior information. A combination of the two algorithms makes the algorithm easier to converge. Simulation experiments for path planning have been executed. The results indicate that the method proposed in this paper can find the optimal path with a shorter path length, which demonstrates the effectiveness of the presented method.
本文提出了一种基于拓扑地图的具有强化学习的新型路径规划算法。所提出的算法具有两级结构。在第一级，该方法基于网格地图使用区域动态增长算法生成拓扑区域。在第二级，应用分为两层的多智能体 SARSA 方法来寻找近似最优的全局规划路径，其中首先使用人工势场法初始化第一个 Q 表以加快学习速度，然后用拓扑地图获得的连通域初始化第二个 Q 表，该连通域提供了先验信息。两种算法的结合使算法更易于收敛。已执行路径规划的仿真实验。结果表明，本文提出的方法能够找到路径长度更短的最优路径，证明了所提方法的有效性。
