基于深度强化学习的端到端自主导航,具有生存惩罚函数
Shyr-Long Jeng1, Chienhsun Chiang2
1Department of Mechanical Engineering, Lunghwa University of Science and Technology, Taoyuan City 333326, Taiwan.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
本研究引入了深度强化学习方法,用于在未知的动态环境中实现自主导航. 该方法通过使用新的奖励功能来增强机器人的生存和目标实现,从而实现无碰撞路径规划.
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
- 机器学习 机器学习
背景情况:
- 在动态的,没有地图的环境中,自主导航带来了重大挑战.
- 传统的方法与稀疏的奖励和复杂的障碍回避作斗争.
研究的目的:
- 为自主导航提出一个端到端的深度强化学习 (DRL) 方法.
- 为了使非全方位的轮式移动机器人 (WMR) 能够有效地在动态的,没有地图的环境中导航.
- 为了解决稀疏奖励问题,并确保无碰撞的路径规划.
主要方法:
- 利用了两个行为者-关键 (AC) 框架:深度决定性政策梯度 (DDPG) 和双延迟的 DDPG (TD3).
- 引入了一个全面的奖励功能,包括一个生存惩罚,以引导WMR实现目标.
- 连接连续的事件,以增加障碍场景的累积罚款,防止训练失败.
主要成果:
- 在各种场景 (无障碍,停车场,十字路口,多重障碍) 中进行的模拟证明了该方法的效率和安全性.
- 与DDPG相比,TD3算法在训练过程中显示出更快的融合和更大的稳定性.
- 在评估过程中,TD3实现了更高的任务执行成功率.
结论:
- 拟议的DRL方法具有生存惩罚功能,可以有效地在具有挑战性的环境中实现自主导航.
- 与DDPG相比,TD3算法在训练效率和导航成功率方面提供了卓越的性能.
- 该方法为WMR无碰撞路径规划提供了强大的解决方案.
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