Team Delft’s robot winner of the Amazon Picking Challenge 2016
Carlos Hernández (TU Delft - Robust Robot Systems)
M. Bharatheesha (TU Delft - Robust Robot Systems)
Wilson Ko (Delft Aerial Robotics (DAR))
J.C. Gaiser (Delft Aerial Robotics (DAR))
J.E.T. Tan (TU Delft - Robust Robot Systems)
Kanter van Deurzen (Delft Aerial Robotics (DAR), TU Delft - OLD Computer Aided Design Engineering)
M. de Vries (TU Delft - Biomechatronics & Human-Machine Control)
B.J. van Mil (TU Delft - RoboValley, Delft Aerial Robotics (DAR))
J.A. van Egmond (TU Delft - Robust Robot Systems)
R.B. Burger (TU Delft - Robust Robot Systems)
Mihai Morariu (TU Delft - Biomechatronics & Human-Machine Control)
R.M. Ensing (TU Delft - Intelligent Vehicles)
J. van Frankenhuyzen (Delft Aerial Robotics (DAR), TU Delft - Biomechatronics & Human-Machine Control)
M. Wisse (Delft Aerial Robotics (DAR), TU Delft - Robust Robot Systems)
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Abstract
This paper describes Team Delft’s robot, which won the Amazon Picking Challenge 2016, including both the Picking and the Stowing competitions. The goal of the challenge is to automate pick and place operations in unstructured environments, specifically the shelves in an Amazon warehouse. Team Delft’s robot is based on an industrial robot arm, 3D cameras and a customized gripper. The robot’s software uses ROS to integrate off-the-shelf components and modules developed specifically for the competition, implementing Deep Learning and other AI techniques for object recognition and pose estimation, grasp planning and motion planning. This paper describes the main components in the system, and discusses its performance and results at the Amazon Picking Challenge 2016 finals.
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