Abstract
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.
Abstract (translated)
工业机器人在切割和焊接等领域广泛应用，对末端执行器的轨迹精度提出了越来越严格的要求。然而，目前的误差补偿方法面临着几个关键挑战，包括过于简化的机制建模、数据驱动方法中物理一致性的缺乏以及大量数据的需求。这些问题使得同时实现高精度和强泛化能力变得困难。为了解决这些挑战，本文提出了一种基于空间-物理信息注意残差网络（SPI-BoTER）的方法。该方法将机器人操作臂的运动学方程与增强型稀疏自注意力掩码的Transformer架构相结合，并在训练过程中采用结合了空间和物理信息的参数自适应混合损失函数进行迭代优化，从而能够在小样本条件下实现高精度误差补偿。此外，还使用基于梯度下降的优化方法来进行反向关节角度补偿。 实验结果表明，在一个UR5机器人手臂的小样本数据集（724个样本，训练、测试和验证的比例为8:1:1）上，所提出的方法表现出优越性能。它实现了0.2515毫米的三维绝对定位误差，标准差为0.15毫米，相较于传统的深度神经网络方法，误差降低了35.16%。此外，反向角度补偿算法在平均147次迭代后收敛到0.01毫米精度。 这项研究提供了一个结合物理可解释性和数据适应性的解决方案，用于实现工业机器人的高精度控制，在智能制造中可靠地执行精密任务方面展现了巨大的潜力。
URL
https://arxiv.org/abs/2506.22788
