Team Delft’s Robot Winner of the Amazon Picking Challenge 2016
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.
KeywordsRobotic system Warehouse automation Motion planning Grasping Deep learning
All authors gratefully acknowledge the financial support by the European Union’s Seventh Framework Programme project Factory-in-a-day (FP7-609206) We would like to thank RoboValley (http://www.robovalley.com), the ROS-Industrial consortium, our sponsors Yaskawa, IDS, Phaer, Ikbenstil and Induvac, the people at the Delft Center for Systems and Control and TU Delft Logistics for their support, also Lacquey B.V. for helping us handle our heavy rail, and finally special thanks to Gijs vd. Hoorn for his help during the development of the robotic system.
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