基于图像梯度向量映射的机械臂姿态估计方法

摘 要:［目的］为了提高机械臂姿态估计精度和实时性，提出基于RGB图像梯度特征向量映射的机械臂姿态重建方法。［方法］首先，采用方向梯度直方图算法（ HOG）计算系列机械臂图像纹理梯度特征，再通过训练深度神经网络（ DNN）建立图像特征向量与机械臂关节角度向量之间的映射关系；然后，使用用于预训练的向量映射模型对机械臂运动帧图像进行快速姿态估计；最后，采用合成数据技术生成模型的训练和测试数据集。［结果］试验结果显示，目标机械臂3个关节的角度预测误差平均值为 2.92° ，单帧图像姿态估计耗时0.08 s。［结论］研究表明，所提方法具有较好的预测速度和精度，仅利用RGB图像信息可实现端到端的机械臂姿态估计。关键词：焊接机器人；机械臂姿态估计；机器视觉；向量映射模型；方向梯度；深度神经网络中图分类号: U671.99文献标志码:A DOI：10.19693/j.issn.1673-3185.02929

Robot arm pose prediction method based on image gradient vector mapping

DING Yuhang, CHEN Zhen*

State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Abstract: ［Objectives］In order to solve the problems of the complexity of the existing robot arm pose prediction algorithm model and its over-reliance on the parameters of the camera and robot, a new robot arm pose prediction method based on RGB image gradient vector mapping is proposed. ［Methods］First, a series of robot arm image texture gradient features is calculated based on the Histogram of Oriented Gradient (HOG) algorithm. The mapping relationship between the image features and joint angles of the robot arm is then established by training Deep Neural Networks (DNNs). Finally, the pre-trained vector mapping model is used to quickly predict the pose of the robot arm in a motion frame image. The training and test datasets of the model are generated by synthetic data techniques. ［ Results］ The results show that the average error of the angle prediction of the three joints of the target robot arm is 2.92°, and the pose prediction time of a single image is about 0.08 s. ［ Conclusions］The results show that the proposed pose prediction method has better prediction speed and accuracy, and only uses RGB image information to achieve end-to-end pose prediction.

Key words: welding robot；robot arm pose estimation；machine vision；vector mapping model；oriented gradient；deep neural network

0 引 言

当前，焊接机器人在船舶制造领域已得到广泛应用，极大地提高了船舶结构焊接的精度和效率[1]。随着数字孪生[2-3] 和智能传感技术的快速发展，通过获取生产现场机械臂的实时姿态信息，可视化监测机械臂动作状况，快速诊断异常故障，对于保障焊接机器人的高效作业具有重要意义。与接触式传感器相比，利用机器视觉技术感知机械臂位姿具有成本低、灵活性高、安装便捷等优势[4]，更加适合于船舶建造车间的生产环境，因此研究基于机器视觉的机械臂姿态估计方法具有重要的工程应用价值。根据实现方式的不同，机械臂姿态机器视觉