从示范路径规划中学习的N维缩小算法
Juliana Manrique-Cordoba1, Miguel Ángel de la Casa-Lillo1, José María Sabater-Navarro1
1Bioengineering Institute, Miguel Hernandez University of Elche, 03202 Elche, Spain.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
本研究介绍了用于机器人路径规划的n维缩小算法,增强了使用隐藏马尔科夫模型 (HMM) 进行复杂,高维数据的轨迹简化. 该方法有效地将学习的行为概括为改进机器人学习.
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:
- 机器人路径规划中的高维数据具有相当大的复杂性.
- 现有的轨迹简化方法可能无法充分捕捉多维运动特征.
研究的目的:
- 开发和评估一个n维缩小算法从演示学习 (LfD).
- 为了提高机器人轨迹的简化和一般化在高维空间.
主要方法:
- 扩展了Douglas-Peucker算法,包括速度和方向与位置.
- 实现基于大小的规范化,以保持尺寸比例.
- 利用隐藏的马尔科夫模型 (HMMs) 进行轨迹分离和学习.
主要成果:
- 在2D和3D环境中,n维算法显著改善了轨迹简化.
- 结合速度和方向可以保存关键的运动信息.
- 生成的基于HMM的模型成功地从演示数据中概括了学习的行为.
结论:
- 拟议的算法有效地解决了用于机器人路径规划的LfD中高维数据的挑战.
- 该方法展示了强大的轨迹简化和概括能力.
- 参数选择对于优化轨迹学习模型的分辨率和性能至关重要.
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