风险意识强化学习与动态安全过器用于移动机器人导航中的碰撞风险缓解
Bingbing Guo1, Guina Wang1, Yiyang Chen1
1School of Mechanical and Electrical Engineering, Soochow University, Suzhou 215137, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
移动机器人现在可以使用一种新的风险意识方法安全地在动态环境中导航. 这种方法将政策优化与障碍功能相结合,以有效避免障碍并改善任务成功.
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
- 控制系统 控制系统
背景情况:
- 传统的移动机器人导航方法在动态环境中与安全性和适应性作斗争.
- 在导航中预先定义的规则限制实时,在复杂的场景中灵活避开障碍.
研究的目的:
- 开发一种新的方法,用于在动态环境中运行的移动机器人中安全和适应性避开障碍.
- 通过先进的政策优化和控制障碍功能,提高移动机器人导航的安全性和效率.
主要方法:
- 提出了一种风险意识,动态,自适应的监管障碍政策优化 (RADAR-BPO) 方法.
- 综合近距离政策优化 (PPO) 探索性行动与控制屏障功能 (CBF) 实时安全过.
- 利用二次编程来最大限度地减少风险行动,并确保导航效率.
主要成果:
- 在复杂,动态,多障碍环境中实现了近90%的避障成功率.
- 在ROS Gazebo模拟中证明了改进的总体任务成功率.
- 在具有挑战性的动态场景中验证了RADAR-BPO的稳定性和有效性.
结论:
- 雷达-BPO方法有效地解决了移动机器人导航中的安全性和适应性限制.
- 集成PPO和CBF提供了一个强大的解决方案,可以实时,安全地避开障碍.
- 拟议的方法在复杂的动态环境中显著提高移动机器人的性能.
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