在动态场景下进行室内自主导航的传统和基于深度强化学习的算法之间的比较研究
Diego Arce1, Jans Solano1, Cesar Beltrán1
1Engineering Department, Pontificia Universidad Católica del Perú, San Miguel, Lima 15088, Peru.
Sensors (Basel, Switzerland)
|December 23, 2023
概括
在动态环境中选择传统和人工智能 (AI) 算法之间的移动机器人自主导航需要仔细考虑. 这项研究比较了基于动态窗口方法 (DWA),定时弹性带 (TEB),基于深度强化学习 (DRL) 的CADRL和软演员-关键 (SAC) 算法.
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:
- 移动机器人的自主导航需要在传统控制和人工智能 (AI) 算法之间做出选择.
- 这个决定是复杂的,受机器人的计算能力,传感器数据和环境动态的影响.
- 选择合适的算法对于有效的导航至关重要,特别是在复杂,不断变化的环境中.
研究的目的:
- 审查和确定适合在动态环境中运行的移动机器人的自主导航算法.
- 将传统算法与基于深度强化学习 (DRL) 的方法进行比较.
- 根据机器人开发要求,为算法选择提供建议.
主要方法:
- 进行了对自主导航算法的全面审查.
- 选择的算法,包括动态窗口方法 (DWA),定时弹性带 (TEB),CADRL和软演员-关键 (SAC) 进行了比较.
- 性能评估是在机器人平台上进行的,以评估优缺点.
主要成果:
- 该研究确定了特定的传统和基于DRL的算法,最适合动态环境.
- 对DWA,TEB,CADRL和SAC进行的比较分析显示了不同的性能特征,优势和缺点.
- 结果表明,最佳算法选择取决于特定的应用程序和机器人的特性.
结论:
- 没有单一的算法是普遍优越的;选择取决于特定应用程序的需求和机器人的能力.
- 像DWA和TEB这样的传统算法在某些条件下提供可行的解决方案.
- 基于DRL的算法,如CADRL和SAC,显示出复杂的动态导航任务的巨大潜力.
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