覆盖路径规划使用演员批判性深度强化学习学习
Sergio Isahí Garrido-Castañeda1, Juan Irving Vasquez1, Mayra Antonio-Cruz2
1Centro de Innovación y Desarrollo Tecnológico en Cómputo (CIDETEC), Instituto Politécnico Nacional (IPN), Mexico City 07700, Mexico.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
本研究引入了用于移动机器人覆盖路径规划的深度强化学习方法. 像A2C和PPO这样的关键演员方法有效地训练机器人以有效地探索和绘制未知的环境.
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:
- 移动机器人探索至关重要,但在完全覆盖环境方面面临挑战.
- 尽管有进展,现有的覆盖路径规划方法仍然是一个开放的问题.
研究的目的:
- 为移动机器人覆盖路径规划提出一个深度强化学习框架.
- 训练和评估关键演员算法,以实现高效的环境探索.
主要方法:
- 利用深度强化学习与优势行为者-关键 (A2C) 和近接政策优化 (PPO) 算法.
- 定义的环境状态,观测和奖励功能,为机器人探索量身定制.
- 对于移动机器人而言,训练有素的政策可以在有障碍的环境中导航.
主要成果:
- 使用A2C和PPO算法生成了优化的策略.
- 评估证明了关键演员方法在引导机器人探索中的有效性.
- 拟议的框架可以有效地覆盖未知的地形.
结论:
- 行为关键的强化学习方法能够为移动机器人覆盖路径规划制定有效的政策.
- 这种方法为机器人提供了一个可行的解决方案,可以自主探索和绘制新环境的地图.
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