机器人智能化吊装技术研究

Investigation on Intelligent Lifting Technology of Robot

鉴于OpenPose进行肢体识别复杂度较高,提出基于TfPose完成人体骨架提取,并采用神经网络集成学习方法对吊装指令肢体信号进行识别,完成智能化吊装操作。首先,采用D-H法对吊装机器人进行正运动学分析,确定卷扬机构工作空间范围,并使用共形几何代数方法求解其逆运动学,完成吊装机器人从当前位置运动到目标位置的数学建模;然后,基于TfPose获取人体骨架向量和RGB骨架图,以BP神经网络和InceptionV3网络为基分类器,采用神经网络集成学习方法确定最优化权重,完成吊装指令肢体信号识别;最后,将识别的吊装指令肢体信号通过UDP通信传送给吊装机器人控制模块,以完成吊装操作。实验结果表明,该方法平均肢体识别精度达0.977,提高了吊装效率。

In view of the high complexity of limb recognition in OpenPose,it was proposed to complete the human skeleton extraction based on TfPose,and the neural network integrated learning method was used to perform limb recognition on the robot lifting instructions to complete the intelligent lifting operation.Firstly,the D-H method was used to perform the forward kinematics analysis of the hoisting robot to determine the working space range of the hoisting mechanism,and the inverse kinematics was solved by the conformal geometric algebra method.The hoisting robot was mathematically modeled from the current position to the target position;then it was obtained based on TfPose.The human skeleton vector and RGB skeleton map were based on BP neural network and InceptionV3 network.The neural network integrated learning method was used to determine the optimal weight to complete the hoisting signal identification.Finally,the identified hoisting command limb signal was transmitted to the hoisting robot through UDP communication.The module was controlled to complete the lifting operation.The experimental results showed that the average limb recognition accuracy of the method was 0.977,which solved the large cargo lifting occasions such as ports,docks and mines,and greatly improved the hoisting and loading efficiency.