Fruit Picking Robot Arm Training Solution Based on Reinforcement Learning in Digital Twin

Abstract

In the era of Industry 4.0, digital agriculture is developing very rapidly and has achieved considerable results. Nowadays, digital agriculture-based research is more focused on the use of robotic fruit picking technology, and the main research direction of such topics is algorithms for computer vision. However, when computer vision algorithms successfully locate the target object, it is still necessary to use robotic arm movement to reach the object at the physical level, but such path planning has received minimal attention. Based on this research deficiency, we propose to use Unity software as a digital twin platform to plan the robotic arm path and use ML-Agent plug-in as a reinforcement learning means to train the robotic arm path, to improve the accuracy of the robotic arm to reach the fruit, and happily the effect of this method is much improved than the traditional method.