arXiv:2510.10468cs.ROcs.SY2025-10被引 9

将伽利略对称性引入机器人学,统一描述惯性运动与速度状态。

Galilean Symmetry in Robotics

  • 用伽利略矩阵李群建模两类含速度的状态表示:惯性帧与扩展姿态。
  • 在地球自转背景下的惯性导航、机械臂运动学与时间不确定传感融合中验证有效。
  • 适合研究运动建模、状态估计与多源数据融合的机器人学者参考。

伽利略对称性是牛顿力学中惯性运动的基本对称性。尽管刚体对称性在机器人学中已广泛应用,但伽利略对称性尚未被系统引入机器人领域。本文面向机器人研究者,基于其对刚体变换和位姿表示的熟悉,重新阐述伽利略对称性,避免物理文献中从相对论出发的传统路径。关键洞见在于:伽利略矩阵李群可描述两类含速度的位姿表示——使用惯性速度的伽利略帧,以及使用坐标速度的扩展姿态。我们通过三个实例展示了该方法的直接应用与深刻启示:地球自转背景下的惯性导航、机械臂运动学分析,以及存在时间不确定性的传感器数据融合。我们认为,机器人学界现在正适合重新发现并拓展这一经典理论,用于解决现代机器人问题。

原文摘要 · Abstract (English)

Galilean symmetry is the natural symmetry of inertial motion that underpins Newtonian physics. Although rigid-body symmetry is one of the most established and fundamental tools in robotics, there appears to be no comparable treatment of Galilean symmetry for a robotics audience. In this paper, we present a robotics-tailored exposition of Galilean symmetry that leverages the community's familiarity with and understanding of rigid-body transformations and pose representations. Our approach contrasts with common treatments in the physics literature that introduce Galilean symmetry as a stepping stone to Einstein's relativity. A key insight is that the Galilean matrix Lie group can be used to describe two different pose representations, Galilean frames, that use inertial velocity in the state definition, and extended poses, that use coordinate velocity. We provide three examples where applying the Galilean matrix Lie-group algebra to robotics problems is straightforward and yields significant insights: inertial navigation above the rotating Earth, manipulator kinematics, and sensor data fusion under temporal uncertainty. We believe that the time is right for the robotics community to benefit from rediscovering and extending this classical material and applying it to modern problems.

机器人学对称性状态估计运动建模

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