连续机器人的统一形状和外部负载状态估计
James M Ferguson1, D Caleb Rucker2, Robert J Webster1
1Vanderbilt University, Nashville, TN 37235, USA.
概括
这项研究引入了一种新的方法,用于连续机器人同时估计形状和外部负载. 这种方法通过将传感器数据与机器人机制集成,改善了狭窄空间中的导航和交互.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 机械工程 机械工程
- 控制系统 控制系统
背景情况:
- 连续机器人在复杂的环境中导航,需要精确的形状传感.
- 当前的方法往往间接估计形状和外部负载,限制性能.
- 由于它们的结合性质,同时估计形状和负载至关重要.
研究的目的:
- 开发一种统计学原则的方法,用于连续机器人的同时形状和外部负载估计.
- 提高在受限制环境中运行的机器人的状态估计的准确性和可靠性.
- 为了证明该方法对各种连续机器人配置的适用性.
主要方法:
- 在弧度域中应用连续时间批量估计.
- 融合离散,噪音传感器测量与连续机器人力学模型作为统计先验.
- 为估计框架开发了一个通用连续机器人模型.
主要成果:
- 在机器人的弧度长度上实现了形状和负载函数的连续后部估计,包括不确定性.
- 通过使用各种传感器配置的Cosserat棒模型数值验证了方法.
- 实验验证了动连续机器人对点负荷的精确形状和外部负载估计.
结论:
- 形状和负载的同时估计为连续机器人提供了更强大,更准确的状态表示.
- 开发的方法对各种连续机器人模型和执行类型有效.
- 这种方法提高了连续机器人的能力,在具有挑战性的环境中实现安全和合规的交互.
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