A Deep Learning-Based Autonomous RobotManipulator for Sorting Application
This work addresses the problem of automating object sorting in industrial settings, but it is incremental as it builds on existing deep learning and robotic manipulation techniques.
The paper tackles autonomous robotic grasping for object sorting by developing a system that uses RGB-D data, convolutional neural networks for detection, and grasp generation algorithms, achieving robust and fast sorting performance on an AUBO robotic manipulator.
Robot manipulation and grasping mechanisms have received considerable attention in the recent past, leading to the development of wide range of industrial applications. This paper proposes the development of an autonomous robotic grasping system for object sorting application. RGB-D data is used by the robot for performing object detection, pose estimation, trajectory generation, and object sorting tasks. The proposed approach can also handle grasping certain objects chosen by users. Trained convolutional neural networks are used to perform object detection and determine the corresponding point cloud cluster of the object to be grasped. From the selected point cloud data, a grasp generator algorithm outputs potential grasps. A grasp filter then scores these potential grasps, and the highest-scored grasp is chosen to execute on a real robot. A motion planner generates collision-free trajectories to execute the chosen grasp. The experiments on AUBO robotic manipulator show the potentials of the proposed approach in the context of autonomous object sorting with robust and fast sorting performance.