用物理约束+稀疏注意力，小样本下让工业机器人定位误差降35%
SPI-BoTER: Error Compensation for Industrial Robots via Sparse Attention Masking and Hybrid Loss with Spatial-Physical Information
- 融合机械臂运动方程与稀疏注意力的Transformer网络
- 小样本（724条）下定位误差仅0.2515mm，比传统方法低35.16%
- 适合追求高精度、数据少的智能制造场景
工业机器人在切割、焊接等场景中对末端轨迹精度要求日益提高。现有误差补偿方法存在模型简化、数据驱动缺乏物理一致性及数据需求量大等问题，难以兼顾高精度与强泛化能力。为此，本文提出空间-物理信息感知注意力残差网络（SPI-BoTER），将机械臂运动学方程与改进的Transformer架构结合，采用参数自适应混合损失函数，融合空间与物理信息进行迭代优化，实现小样本条件下的高精度误差补偿。通过基于梯度下降的逆向关节角补偿算法，在UR5机械臂小样本数据集（共724样本，训练:测试:验证=8:1:1）上实验表明，该方法三维绝对定位误差达0.2515 mm，标准差0.15 mm，相较传统深度神经网络（DNN）降低35.16%；逆向补偿算法平均147次迭代收敛至0.01 mm精度。本研究为智能制造中高精度控制提供了兼具物理可解释性与数据适应性的解决方案。
原文摘要 · Abstract (English)
The widespread application of industrial robots in fields such as cutting and welding has imposed increasingly stringent requirements on the trajectory accuracy of end-effectors. However, current error compensation methods face several critical challenges, including overly simplified mechanism modeling, a lack of physical consistency in data-driven approaches, and substantial data requirements. These issues make it difficult to achieve both high accuracy and strong generalization simultaneously. To address these challenges, this paper proposes a Spatial-Physical Informed Attention Residual Network (SPI-BoTER). This method integrates the kinematic equations of the robotic manipulator with a Transformer architecture enhanced by sparse self-attention masks. A parameter-adaptive hybrid loss function incorporating spatial and physical information is employed to iteratively optimize the network during training, enabling high-precision error compensation under small-sample conditions. Additionally, inverse joint angle compensation is performed using a gradient descent-based optimization method. Experimental results on a small-sample dataset from a UR5 robotic arm (724 samples, with a train:test:validation split of 8:1:1) demonstrate the superior performance of the proposed method. It achieves a 3D absolute positioning error of 0.2515 mm with a standard deviation of 0.15 mm, representing a 35.16\% reduction in error compared to conventional deep neural network (DNN) methods. Furthermore, the inverse angle compensation algorithm converges to an accuracy of 0.01 mm within an average of 147 iterations. This study presents a solution that combines physical interpretability with data adaptability for high-precision control of industrial robots, offering promising potential for the reliable execution of precision tasks in intelligent manufacturing.
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