Visual-based joint calibration method for multirobot systems

A vision based joint calibration method for multi robot systems is proposed to address various parameter calibration issues (hand eye, positioner, dual robot base coordinate system calibration). Firstly, the chessboard calibration board data is collected by an industrial camera, and the calibration parameters of the hand eye, displacement machine, and dual robots are calculated using linear algorithms as initial values. Then, a joint calibration objective function for the multi robot system is established, and the calibration parameters of the hand eye, displacement machine, and dual robots of the multi robot system are optimized using gradient descent algorithm. The final experiment showed that the average maximum distance between the center points optimized by this calibration method was 4.07mm, significantly better than the average maximum distance before optimization of 5.34mm. The average standard deviation of the overall data after optimization was 2.37mm, significantly better than the average standard deviation before optimization of 3.08mm, proving that this calibration optimization method improves the overall calibration accuracy of the multi robot system.