告知循环场:用于机器人操纵器的全球反应式障碍回避框架
Marvin Becker1, Philipp Caspers1, Torsten Lilge1
1Institute of Automatic Control, Leibniz University Hannover, Hannover, Germany.
Frontiers in robotics and AI
|January 20, 2025
概括
这项研究引入了一个全新的全球反应性运动规划框架,用于动态环境中的机器人. 它通过将全球轨迹规划与本地反应性控制相结合,提高了避开障碍的性能,优于现有的方法.
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:
- 机器人操纵器需要先进的运动计划,以便在复杂,动态的环境中安全导航.
- 现有的方法经常在实时适应和全面避开障碍方面扎.
- 整合全球和地方规划策略对于稳健的业绩至关重要.
研究的目的:
- 开发一个统一的框架,用于机器人操纵器的全球反应性运动规划.
- 通过将全球轨迹见解与本地反应控制相结合,增强避障能力.
- 提高机器人适应性和安全性,在不可预测,人类居住的工作空间工作.
主要方法:
- 使用无局部最小值的圆形场来生成反应式控制命令.
- 利用来自配置空间运动规划器的全球环境信息来指导轨迹.
- 扩展虚拟代理框架以模拟多个机器人轨迹,以增强避免.
- 整合视觉反用于现实世界的机器人控制和适应.
主要成果:
- 拟议的框架在超过4000个模拟场景中始终优于现有的运动计划器.
- 在现实世界实验中与Franka Emika机器人进行了有效和快速的适应,以应对不可预测的人类运动.
- 实现了整个机器人操纵器身体的全面避障.
结论:
- 开发的全球反应性运动规划框架为动态环境提供了强大的多功能解决方案.
- 统一的方法有效地将全球规划与本地反应控制相结合,以获得卓越的性能.
- 通过广泛的模拟和现实世界机器人实验验证实有效性.
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