Coinbot: Intelligent Robotic Coin Bag Manipulation Using Artificial Brain

Abstract

Given the laborious difficulty of moving heavy bags of physical currency in the cash center of the bank, there is a large demand for training and deploying safe autonomous systems capable of conducting such tasks in a collaborative workspace. In this paper, we apply deep reinforcement learning and machine learning techniques to the task of controlling a collaborative robot to automate the unloading of coin bags from a trolley. To accomplish the task-specific process of gripping coin bags where the center of the mass changes during manipulation, a special gripper was designed in physical hardware. Leveraging a depth camera and deep learning, a bag detection and pose estimation has been done for choosing the optimal point of grasping. An intelligent approach based on deep reinforcement learning has been introduced to propose the best configuration of the robot end-effector to maximize successful grasping. A boosted motion planning is utilized to speed up the robot operation. Real-world trials with the proposed pipeline have demonstrated success rates over 96% in a real-world setting.