通过动态系统和基于采样的MPC在联合空间中产生反应式无碰撞运动
Mikhail Koptev1, Nadia Figueroa2, Aude Billard1
1Learning Algorithms and Systems Laboratory (LASA), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
概括
本研究介绍了一种新的机器人运动规划方法,将动态系统 (DS) 与基于采样的模型预测控制 (MPC) 结合起来. 该方法有效地引导机器人绕过障碍物,即使在复杂的环境中,确保无碰撞和反应性的运动.
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:
- 基于动态系统 (DS) 的运动规划提供了反应性,无碰撞的路径,但与非凸起的障碍物和高维空间作斗争.
- 基于采样的模型预测控制 (MPC) 生成无碰撞路径,但是计算密集型,仅限于准反应场景.
研究的目的:
- 开发一种机器人运动规划方法,将DS和MPC的优势结合起来,在混乱的环境中提高反应能力和避开障碍物.
- 使机器人能够在复杂的,高维的联合空间中导航,同时避免静态和动态障碍.
主要方法:
- 调节关节空间DS与来自异步生成的MPC路径的障碍触角速度组件.
- 仅在检测到局部最小值时使用MPC,从而减少计算负载.
- 偏移名义DS与触点速度组件,以绕过障碍物和逃离局部最小值.
主要成果:
- 提出的方法成功地避免了形障碍物,并保持了局部吸引力的稳定性.
- 在半静态和高度动态的杂乱环境中表现出能力.
- 通过对7-DoF机器人进行模拟和现实世界的实验来验证.
结论:
- 混合DS-MPC方法提供了一个强大的解决方案,用于在复杂的环境中产生可行的,高度反应和无碰撞的机器人运动.
- 这种方法克服了传统的DS和MPC技术的局限性,特别是在具有非凸障碍和动态变化的场景中.
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