智能移动机器人在未知和复杂的环境中使用强化学习技术进行导航
1Faculty of Computer Science, Electronics, and Telecommunications, AGH University of Krakow, Aleja Adama Mickiewicza 30, Krakow, 30-059, Poland. raj@agh.edu.pl.
Scientific reports
|October 1, 2024
概括
本研究引入了一种用于移动机器人 (MR) 控制的新增强化学习 (RL) 方法. 深度Q学习方法提高了在未知的环境中导航和避开障碍的性能,优于传统策略.
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 (MR) 越来越多地用于制造,监控,医疗保健和仓库自动化.
- 有效的控制策略对于在动态环境中安全高效的MR运行至关重要.
- 为了使MR适应陌生的环境,需要先进的导航和避障能力.
研究的目的:
- 开发一种新的强化学习 (RL) 技术,用于控制移动机器人 (MR).
- 通过使用深度Q学习 (DQN) 在未知的环境中增强MR导航和避免碰撞.
主要方法:
- 为MR控制生成了一个数学模型.
- 使用RL训练了一个神经网络 (NN),特别是一个深度Q学习 (QL) 代理.
- 使用Epsilon-Greedy算法进行基于模拟的评估.
主要成果:
- 基于RL的方法使得自主学习能够避免障碍物和导航.
- 深度Q网络 (DQN) 成功引导MR到不熟悉地区的目标位置.
- 与传统的MR控制策略相比,拟议的方法显示出更高的效率和安全性.
结论:
- 强化学习,特别是深度Q学习,为先进的移动机器人控制提供了强大的解决方案.
- 开发的技术在复杂,未知的环境中显著改善了MR导航和安全.
- 这种方法有望在依赖移动机器人的各个行业中增强自动化.
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