兵工自动化2025，Vol.44Issue(4)：96-100,5.DOI:10.7690/bgzdh.2025.04.019
基于无建图的强化学习人工势场法编队
Artificial Potential Field Formation Method Based on Reinforcement Learning without Graph Construction
丁磊 1骆云志 2洪华杰 3黄杰 3樊鹏 2赵伟 4陈斯灏2
作者信息
- 1. 中国兵器装备集团自动化研究所有限公司系统总体部,四川 绵阳 621000||国防科技大学智能科学学院,长沙 410073
- 2. 中国兵器装备集团自动化研究所有限公司系统总体部,四川 绵阳 621000
- 3. 国防科技大学智能科学学院,长沙 410073
- 4. 陆装驻广元地区军代室,四川 广元 628000
- 折叠
摘要
Abstract
For simultaneous localization and mapping(SLAM)technology has the disadvantages of high demand for computing resources,limited environmental adaptability,cumulative error problem,high system complexity,high cost,limited large scene processing capacity and lack of effective loop detection mechanism,so a method combining artificial potential field method and deep reinforcement learning is proposed.The graph theory is used to simulate the interaction between robots and the potential force between robots and the destination,and the twin delayed deep deterministic policy gradient algorithm is used to optimize the robot's perception and processing of obstacle information.The simulation results show that the method can make the robot locate and move quickly and accurately in the unknown environment,while maintaining the stability and consistency of the formation.关键词
人工势场法/强化学习/双延时确定策略梯度/图论Key words
artificial potential field method/reinforcement learning/two-delay deterministic policy gradient/graph theory分类
信息技术与安全科学引用本文复制引用
丁磊,骆云志,洪华杰,黄杰,樊鹏,赵伟,陈斯灏..基于无建图的强化学习人工势场法编队[J].兵工自动化,2025,44(4):96-100,5.
