外观
论文arXiv 2609.037941 个月前
用于交付精密 NDE 测量的移动机器人平台精度与重复性比较研究
研究用激光跟踪仪统一评估五种商用移动平台在大型航空结构 NDE 中的静态与轨迹定位精度。
Boston Dynamics Spot原文链接：A comparative study on the accuracy & repeatability of mobile robotic platforms for the delivery of precision NDE measurement（arXiv 2609.03794 · 发布日期：2026-09-03）
A comparative study on the accuracy & repeatability of mobile robotic platforms for the delivery of precision NDE measurement
研究用激光跟踪仪统一评估五种商用移动平台在大型航空结构 NDE 中的静态与轨迹定位精度。
核心要点
- 大型航空结构 NDE 需灵活平台，但基座定位精度及部署依据缺少统一外部参考评估。
- 提出激光跟踪仪工作流，结合多角点校准与最小二乘估计，测量静态和分段轨迹精度。
- 静态中位误差为 8.2 至 63.5 mm，动态横向误差为 6.9 至 112.1 mm，Husky 无法校准。
- 结果表明单靠基座均未达 0.2 至 1.0 mm 公差，可作为补充感知需求和平台选择依据。
验证范围与局限：验证范围为五种商用移动平台在统一协议下的静态定位和分段轨迹跟随，面向大型航空结构 NDE。
英文摘要 (Abstract)
Mobile robotic platforms offer a flexible alternative to fixed manipulators for non-destructive evaluation (NDE) of large aerospace structures, but their base-positioning accuracy and how that accuracy should inform deployment have not been assessed under a common, externally referenced protocol. This work presents a laser tracker-based evaluation workflow (ground truth approximately 6 micrometers) that measures the static and segmented trajectory positioning accuracy of five commercial mobile platforms (KUKA KMP-1500, KUKA KMR, MiR250, Boston Dynamics Spot, Clearpath Husky) under a common protocol. A coupled multi-corner calibration recovers the laser-to-robot transformation and reflector offsets; ordinary least squares over all poses is used, with robust estimation retained only as a blunder check. Static positioning accuracy ranged from a median of 8.2 mm (KMP-1500) to 63.5 mm (Spot), with the wheel-odometry-only Husky uncalibratable. Dynamic path following was characterised by cross-track error; the component was insensitive to temporal alignment, which ranged from 6.9 mm (KMP-1500) to 112.1 mm (Spot). Both accuracy and calibratability tracked localisation capability, from the newest LiDAR SLAM platform to map-free visual odometry. No configuration meets the 0.2 to 1.0 mm aerospace NDE tolerance from the base alone; the results are framed as a design input that sizes the supplementary sensing each platform requires: roughly one order of magnitude for the best platform and nearly two for the worst, providing a reproducible basis for platform selection rather than a feasibility claim.
作者：SeyedMohammadAmin Nabi Pour, S.Gareth Pierce, Randika K.W. Vithanage, Ehsan Mohseni, David Carswell, Matthew C. Shields
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