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

• Main Authors: Jian Zhou, Hee-Jun Kang
• Format: Article
• Language: English
• Published: SAGE Publishing 2015-06-01
• Series: Advances in Mechanical Engineering
• Online Access: https://doi.org/10.1177/1687814015590289

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.