利用受 Kubelka-Munk 模型启发的人工神经网络解决喷漆机器人轨迹优化中的涂料颜色效果预测问题
Solve paint color effect prediction problem in trajectory optimization of spray painting robot using artificial neural network inspired by the Kubelka Munk model
AI总结：
针对多色喷涂轨迹优化缺乏像素级颜色效果预测的问题，本文融合 Kubelka-Munk 模型、3D机器视觉与改进MLP，提出可替代厚度仿真的颜色效果预测方法。
摘要：
当前，以喷漆质量为目标的喷漆机器人轨迹规划技术主要适用于单色喷涂。传统基于模拟厚度优化喷枪轨迹的方法只能定性反映颜色分布，无法在像素级模拟喷漆的颜色效果。因此，无法精确控制颜色覆盖区域及区域边缘的渐变，也难以处理多种颜色涂料组合喷涂的情况。为解决上述问题，本文受 Kubelka-Munk 模型启发，结合三维机器视觉方法与人工神经网络，提出一种喷漆颜色效果预测方法。该方法能够从喷漆后工件表面颜色的维度，以像素级精度预测喷枪轨迹的执行效果。在此基础上，该方法可用于替代传统厚度仿真方法，建立喷枪轨迹优化问题的目标函数，从而解决多色涂料组合喷涂的喷枪轨迹优化难题。本文首先通过分析 Kubelka-Munk 漆膜显色模型，确定喷漆颜色效果预测问题的数学模型；同时借助深度相机和点云处理算法建立喷漆颜色效果数据集。随后，利用门控结构和残差结构改进多层感知机模型，并将其用于颜色预测任务。为验证……
英文摘要：
Currently, the spray-painting robot trajectory planning technology aiming at spray painting quality mainly applies to single-color spraying. Conventional methods of optimizing the spray gun trajectory based on simulated thickness can only qualitatively reflect the color distribution, and can not simulate the color effect of spray painting at the pixel level. Therefore, it is not possible to accurately control the area covered by the color and the gradation of the edges of the area, and it is also difficult to deal with the situation where multiple colors of paint are sprayed in combination. To solve the above problems, this paper is inspired by the Kubelka-Munk model and combines the 3D machine vision method and artificial neural network to propose a spray painting color effect prediction method. The method is enabled to predict the execution effect of the spray gun trajectory with pixel-level accuracy from the dimension of the surface color of the workpiece after spray painting. On this basis, the method can be used to replace the traditional thickness simulation method to establish the objective function of the spray gun trajectory optimization problem, and thus solve the difficult problem of spray gun trajectory optimization for multi-color paint combination spraying. In this paper, the mathematical model of the spray painting color effect prediction problem is first determined through the analysis of the Kubelka-Munk paint film color rendering model, and at the same time, the spray painting color effect dataset is established with the help of the depth camera and point cloud processing algorithm. After that, the multilayer perceptron model was improved with the help of gating and residual structure and was used for the color prediction task. To verify ...
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