提出可适配机器人配置差异的运动学校准方法，显著提升全工作空间精度。
Configuration-Dependent Robot Kinematics Model and Calibration
- 用局部POE模型在多姿态下建模，通过傅里叶函数插值融合为全局模型
- 在两台6轴工业机器人上使最大定位误差降低超50%，达亚毫米级精度
- 比神经网络更高效，适合对精度要求高的工业场景如冷喷涂
精确的机器人运动学对关节型机器人精准定位工具至关重要，但非几何因素会引入依赖配置的模型偏差。本文提出一种配置依赖的运动学校准框架，以提升整个工作空间的精度。选取参数连续性良好的局部乘积-指数（POE）模型，在多个配置点识别后，通过傅里叶基函数插值构建全局模型，其参数化基于肩关节与肘关节角度。该方法在精度上媲美神经网络与自编码器，但训练效率显著更高。在两台6自由度工业机器人上的验证表明，该方法使最大定位误差降低超过50%，达到冷喷涂制造所需的亚毫米级精度。配置依赖偏差较大的机器人获益更明显。双机器人协同任务实验证明了该框架的实际可用性与重复性。
原文摘要 · Abstract (English)
Accurate robot kinematics is essential for precise tool placement in articulated robots, but non-geometric factors can introduce configuration-dependent model discrepancies. This paper presents a configuration-dependent kinematic calibration framework for improving accuracy across the entire workspace. Local Product-of-Exponential (POE) models, selected for their parameterization continuity, are identified at multiple configurations and interpolated into a global model. Inspired by joint gravity load expressions, we employ Fourier basis function interpolation parameterized by the shoulder and elbow joint angles, achieving accuracy comparable to neural network and autoencoder methods but with substantially higher training efficiency. Validation on two 6-DoF industrial robots shows that the proposed approach reduces the maximum positioning error by over 50%, meeting the sub-millimeter accuracy required for cold spray manufacturing. Robots with larger configuration-dependent discrepancies benefit even more. A dual-robot collaborative task demonstrates the framework's practical applicability and repeatability.
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