Firing Methods for Firing Robots Based on Supervised Learning and Model Predictive Control

In order to improve the firing accuracy of firing robot against moving targets, hit probability estimation and image servo control are investigated in this paper. Firstly, a method to predict the impact points based on supervised learning is proposed. An corrected ballistic trajectory model is applied to generate training samples, and the mapping relationship that expresses the state of the impact points is formed. In addition, an image servo control method based on model predictive control and human key point detection is proposed, which effectively improves the tracking accuracy of moving targets. Finally, numerical simulation and firing experiment validation are carried out, and the results show that the precision firing method of firing robot proposed in this paper can effectively improve the firing accuracy of firing robot.