  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.
