基于遗传算法的机器人负载重力补偿优化算法研究

航空航天装配领域,利用安装在机器人末端的六维力传感器来感知外力是实现工业机器人柔顺装配的关键技术之一,而负载的存在会干扰对外力的感知。针对工业机器人末端负载的重力补偿,提出一种基于遗传算法的重力补偿优化算法。此方法以误差平方和最小为目标建立了最优解模型,利用遗传算法求解,最终可以在不使用测量仪器的情况下,估计力传感器安装偏角,提高重力补偿精度。同时设计了特定的机器人测量姿态来减少系统误差。试验表明,优化算法可以补偿任意角度的力传感器安装偏角,与补偿前相比,各方向最大重力补偿误差及平均重力补偿误差都有所减小。

Research on Optimization Algorithm of Robot Load Gravity Compensation Based on Genetic Algorithm

In the field of aeronautical and astronautical assembly,the six–dimensional force sensors installed on the end of robot is commonly used to derive external forces,which is one of the key technologies to realize the compliance control of industrial robots,but the presence of loads will interfere the perception of external forces.To compensate the gravity of end load of industrial robots,a genetic algorithm-based gravity compensation optimization algorithm is proposed.Based on the least squared method,this method considers the influence of force sensor installation deflection angle on compensation effect,and an optimal solution model is established with the objective of minimizing the sum of squared error,by using genetic algorithm.As a result,the installation deflection angle of force sensor can be estimated without use of measuring instruments,and the accuracy of gravity compensation can be improved.In the meantime,the system errors are reduced effectively by designing specific robot measuring attitude.The experiments show that arbitrary angles of the installation deflection angle can be compensated by optimization algorithm.Compared with before compensation,maximum gravity compensation errors in each direction and average gravity compensation errors are both generally reduced.