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Robot manipulation and grasping mechanisms have
received considerable attention in the recent past, leading to
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 on 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
will be chosen to execute on a real robot. A motion planner
will generate 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.
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