基于遗传算法优化神经网络的机器人臂重力补偿研究  

Research of the Robot Arm Gravity Compensation based on the Genetic Algorithm for Optimization of Neural Network

作　　者：杨源[1] 曹彤[1] 刘达[2] 

机构地区：[1]北京科技大学机械工程学院,北京100083 [2]北京航空航天大学机器人研究所,北京100083

出　　处：《机械传动》2017年第2期37-40,共4页Journal of Mechanical Transmission

摘　　要：利用遗传算法优化的神经网络,对机器人臂的重力补偿进行研究。首先,根据力学基本知识和D-H参数建模法得出机器人臂各关节转矩的重力项理论计算公式;其次,在Solid Works仿真软件中,得到个别位姿下的重力项仿真值,并验证理论公式的正确性;最后,用遗传算法优化的神经网络对重力项进行预测。实验结果表明,采用该算法得到的重力项预测值和理论值基本一致,减少了运算量,提高了效率,为进一步实时控制提供了可能。By adopting a genetic algorithm for optimization of neural network,the gravity compensation for robot arm is researched.Firstly,through the basic knowledge of mechanics and D-H parameter to set up robot kinematics model,the theoretical computation formula of the gravity item of each joint torque in robot arm is got.Secondly,in the Solid Works simulation software,the simulation value of the gravity item in some certain pose got,thus the correctness of the theoretical computation formula is verified.Finally,the predicted value of the gravity item by genetic algorithm for optimization of neural network is obtained.Experimental results show that the predicted value of gravity items of robot arm learnt with this method is basically conforming with the theoretical value.Consequently,the work load of calculation for the gravity items of robot arm is effectively reduced.Furthermore,this method provide a possible way for the real-time control.

关 键 词：机器人臂 D-H参数 重力补偿 神经网络 遗传算法 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]