A Robot Calibration Method Using a Neural Network Based on a Butterfly and Flower Pollination Algorithm

Abstract

This article proposes a robot calibration method using an extended Kalman filter (EKF) and an artificial neural network (ANN) based on a butterfly and flower pollination algorithm (ANN-BFPA) to improve the robot’s absolute pose (position and orientation) accuracy. After establishing a geometric error model, the EKF, a robust optimization algorithm for a nonlinear system with Gaussian noise, was used to estimate geometric parameter errors and compensate for geometric errors. However, nongeometric errors caused by joint clearance, gear backlash, and link deflection could still affect the pose accuracy and interfere with the correctness of the model. Therefore, the ANN-BFPA was proposed to compensate for these errors. The ANN model was used to establish the complex relationship between joint lengths and pose error. In addition, BFPA was used to optimize weights and bias of the neural network. The efficiency of the proposed calibration method was evaluated using a Stewart platform. Experimental results demonstrated that the proposed method significantly improved the robot’s pose accuracy and showed better performance than previous techniques.