基于游戏的近似最佳动作计划,用于安全的人群交互
IEEE transactions on cybernetics
|January 1, 2024
概括
这项研究引入了一种新的游戏理论方法,用于安全的人类群交互,确保机器人自主调整不安全的命令和障碍,以实现强大的合作.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制理论 控制理论
- 游戏理论 游戏理论
背景情况:
- 人群交互的安全性至关重要和具有挑战性.
- 现有的方法需要高的实时计算来实现受约束的优化.
- 在人机系统中需要强大和自主安全解决方案.
研究的目的:
- 开发一个安全和强大的框架,用于人群交互.
- 解决当前安全方法的实时局限性.
- 为了实现对不安全的人类指挥的自主轨道修改.
主要方法:
- 在整个时间域中用Stackelberg-Nash游戏来制定这个问题.
- 为追随机器人设计最佳响应控制器 (纳什平衡).
- 开发一个类似于Lyapunov的控制屏障功能,以确保领导者的安全,以及一个基于学习的控制器,用于形成跟踪.
主要成果:
- 机器人群成功地保持所需的阵列,同时跟踪人类的命令.
- 控制器自主修改轨迹,以确保对不安全命令的安全.
- 即使存在动态障碍,也保持安全,通过模拟和实验验证.
结论:
- 拟议的斯塔克尔伯格-纳什游戏方法有效地确保了人群交互中的安全.
- 该方法通过优化整个时间域来克服实时约束.
- 该系统表现出强大的合作行为和自主安全调整.
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