A hybrid least-squares genetic algorithm–based algorithm for simultaneous identification of geometric and compliance errors in industrial robots

Abstract

Due to the flexibility of robot joints and links, industrial robots can hardly achieve the accuracy required to perform tasks when a payload is attached at their end-effectors. This article presents a new technique for identifying and compensating compliance errors in industrial robots. Within this technique, a comprehensive error model consisting of both geometric and compliance errors is established, where joint compliance is modeled as a piecewise linear function of joint torque to approximate the nonlinear relation between joint torque and torsional angle. A hybrid least-squares genetic algorithm–based algorithm is then developed to simultaneously identify the geometric parameters, joint compliance values, and the transition joint torques. These identified geometric and non-geometric parameters are then used to compensate geometric and joint compliance errors. Finally, the developed technique is applied to a 6 degree-of-freedom industrial serial robot (Hyundai HA006). Experimental results are presented that demonstrate the effectiveness of the identification and compensation techniques.