arXiv:2602.21450cs.ROcs.SY2026-02被引 2

提出一种在李群上实现机器人路径跟踪的向量场方法,支持实时控制。

Vector Fields for Path Following on Lie Groups with Application in Robot Control

  • 设计李群上的引导向量场,确保从多数初始状态收敛
  • 控制输入为最小化表示,更贴近工程应用需求
  • 适用于机械臂等复杂姿态路径跟踪,开源可复现

许多机器人系统可独立控制位置与姿态(位姿),如全向飞行器、水下机器人和机械臂末端。在诸多应用中,这些系统需沿连续位姿序列运动,形成路径跟踪问题。相比轨迹跟踪,路径跟踪具有实际优势。本文聚焦于李群上的路径跟踪问题,将机器人视为三维空间中的刚体,将其路径跟踪问题转化为在矩阵李群SE(3)上设计引导向量场。本文提出一个通用的向量场框架,适用于连通矩阵李群,其中SE(3)是典型特例。所提向量场能保证从几乎所有初始条件收敛至期望参数曲线,并保持沿路径的连续运动。此外,与以往工作不同,控制输入在表示上“最小化”,更接近工程实际(如SE(3)中的体旋)。在建立通用框架后,进一步将其特化至SE(3),获得适合实时机器人控制的高效算法。实验表明,该方法在机械臂跟踪复杂位姿路径时有效,且提供开源实现。

原文摘要 · Abstract (English)

Many robotic systems allow independent control of position and orientation (pose), including omnidirectional aerial vehicles, underwater robots, and manipulator end-effectors. In many applications, these systems must follow a continuous sequence of poses, leading to either trajectory-tracking or path following formulations. Compared to trajectory-tracking, path following offers important practical advantages. In particular, we focus on the problem of path following on Lie groups. Considering the robots as rigid bodies moving in the 3D space, this path-following problem can be posed as a problem of designing guiding vector fields on the matrix Lie group SE(3). In this paper, we develop a general vector-field framework for path following on connected matrix Lie groups, of which SE(3) is a prominent special case. The proposed vector field guarantees convergence to a desired parametric curve from almost all initial conditions while ensuring continuous motion along the path. Furthermore, another interesting feature is that, as opposed to previous works, the control input is "minimal" in terms of representation and closer to the engineering application (e.g., the body twist in the case SE(3)). After establishing the general case, the framework is then specialized to SE(3), of special interest in robotics, yielding an efficient algorithm suitable for real-time robotic control. Experiments with a robotic manipulator tracking complex pose paths demonstrate the effectiveness of the approach. An open-source implementation is also provided.

路径跟踪李群机器人控制向量场

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