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.
Highlights
Robot manipulators play an important role in the industrial field
The results show that the position accuracy of the robot is significantly improved at a rate of 98% after compensating both geometric and compliance errors based on the identified parameters
We model the robot joint as a piecewise linear system to approximate its nonlinear behavior, which allows us to obtain a linear form of the identification equations that can be solved using a standard least-squares technique
Summary
Robot manipulators play an important role in the industrial field. Industrial applications such as assembly, welding, and machining operations require highly accurate robot manipulators. Based on the comprehensive error model with the given transition joint torques, an iterative least-squares algorithm is used to identify the geometric parameters and joint stiffness values. Joint compliance parameters Ci are obtained from equation (19) Based on these identified joint compliance parameters, another iterative process to find the equilibrium position (uie), torque, and the resulting joint deflections (Duic) is applied as follows. Load data set, which in our case includes the nominal kinematic parameters, the masses of the load and links, the mass centers of links, and the measurement data consisting of the position vectors PM of the robot end-effector and the joint encoder readings uE related to m measure- Figure 4.
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