库伦力引导的深度强化学习,用于有效和可解释的机器人运动规划
Sirui Song1, Trevor Bihl1, Jundong Liu1
1Learning and Intelligent Systems Lab (LiSL), School of Electrical Engineering and Computer Science, Ohio University, Athens, OH, United States.
Frontiers in robotics and AI
|February 16, 2026
概括
本研究介绍了一种以物理为灵感的深度强化学习 (DRL) 框架,用于移动机器人导航. 这种新的方法使用库伦力和LiDAR数据在复杂环境中实现更安全,更易于解释的运动规划.
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:
- 在复杂环境中移动机器人导航对安全和效率提出了重大挑战.
- 深度增强学习 (DRL) 为自主导航提供了一个有希望的方法,但往往缺乏解释性.
- 数字双胞胎技术越来越多地用于训练和验证机器人系统.
研究的目的:
- 为移动机器人的有效和可解释的运动规划提出一套以物理为灵感的新型DRL框架.
- 通过结合吸引力和排斥力以及预先避免碰撞来提高机器人导航安全性.
- 在模拟和现实环境中验证框架的性能.
主要方法:
- 代表机器人,目的地和障碍物作为电荷,与库伦力模拟的相互作用.
- 将库伦力集成到DRL奖励函数中,以指导机器人的行为.
- 整合基于LiDAR的障碍物边界细分,用于预期避免碰撞的奖励.
- 在 Gazebo 模拟中训练 DRL 模型并将其部署在 TurtleBot v3 机器人上.
主要成果:
- 拟议的框架大大减少了机器人导航期间的碰撞.
- 始终保持与障碍物之间的安全距离.
- 该方法产生了更安全,更有效的轨迹,朝着指定的目的地.
- 这种以物理为灵感的方法提高了运动规划决策的可解释性.
结论:
- 灵感来自物理学的DRL框架有效地实现了移动机器人的安全和可解释的运动规划.
- 库伦力和基于LiDAR的奖励对于在复杂,动态的环境中进行可靠的导航至关重要.
- 在真实机器人上成功部署验证了框架的实际适用性和性能.
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