摘要
针对多障碍复杂环境下移动机器人路径规划问题,提出了一种基于萤火虫算法的Q-learning算法。在求解算法中,为提高算法的收敛速度,使用萤火虫算法初始化Q-learning框架下Q值;为平衡算法搜索的随机性和目的性,结合贪婪搜索与玻尔兹曼搜索,设计了混合选择策略,使得算法可以动态选择搜索策略,并通过仿真实验验证所提算法的有效性。仿真实验结果表明所提算法在计算时间和路径平滑度等指标上优于Q-learning算法和Sarsa算法。
Aiming at the path planning problem of mobile robot in multi-obstacle complex environment, a Q-learning algorithm based on firefly algorithm is proposed. In the solution algorithm, in order to improve the convergence speed of the algorithm, the firefly algorithm is used to initialize the Q value under the Q-learning framework;In order to balance the randomness and purpose of search, combined with greedy search and Boltzmann search, a hybrid selection strategy is designed, so that the algorithm can dynamically select the search strategy. The effectiveness of the proposed algorithm is verified by simulation experiments. Simulation results show that the proposed algorithm is better than Q-learning algorithm and Sarsa algorithm in terms of calculation time and path smoothness.
作者
王付宇
张康
谢昊轩
陈梦凯
WANG Fu-yu;ZHANG Kang;XIE Hao-xuan;CHEN Meng-kai(School of Management Science and Engincering,Anhui University of Technology,Maanshan 243032,China;Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes,Anhui University of Technology Maanshan 243002,China)
出处
《系统工程》
北大核心
2022年第4期100-109,共10页
Systems Engineering
基金
国家自然科学基金资助项目(71872002)
安徽省高校人文社科研究重大项目(SK2020ZD16)
“复杂系统多学科管理与控制”安徽普通高校重点实验室开放基金项目(CS2019-ZD02)。