多机器人层次安全增强学习自主决策策略,基于统一的最终局限性约束
Huihui Sun1,2,3, Hui Jiang4, Long Zhang5,6
1School of Mechanical and Electrical Engineering, Huainan Normal University, Huainan, 232038, China.
Scientific reports
|February 18, 2025
概括
本研究介绍了多机器人系统的统一的最终界限受约束的分层安全增强学习策略 (UBSRL). UBSRL提高了决策的安全性和效率,确保机器人的行动保持在安全的边界内.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 控制理论 控制理论
背景情况:
- 深度强化学习对于多机器人系统至关重要,但在动态环境中与安全性作斗争.
- 自主决策缺乏安全性,使多机器人系统面临风险和损害.
研究的目的:
- 为自主多机器人系统制定以安全为中心的强化学习策略.
- 在动态和非结构化环境中应对安全挑战.
主要方法:
- 通过使用受约束的马尔科夫决策流程,提出了一个事件触发的层次安全增强学习框架.
- 集成的上层进化和下层恢复网络,以提高安全性和效率.
- 集成Lyapunov安全成本网络和拉格朗奇乘数用于战略优化.
主要成果:
- 证明了行动轨迹可以在有限的时间内恢复到安全空间.
- 统一的最终界限受约束的分层安全增强学习策略 (UBSRL) 有效地限制安全指标低于值.
- 在标准化场景中显著提高稳定性和任务完成率.
结论:
- 在理论上,UBSRL战略保证了多机器人系统安全约束的有效性.
- 在自主系统中实现了决策安全性和效率之间的平衡.
- 该方法为复杂环境中的安全导航和任务执行提供了强大的解决方案.
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