Design of a Multi-Modal End-Effector and Grasping System: How Integrated Design helped win the Amazon Robotics Challenge
October 04, 2017 Β· Declared Dead Β· π IEEE International Conference on Robotics and Automation
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Authors
S. Wade-McCue, N. Kelly-Boxall, M. McTaggart, D. Morrison, A. W. Tow, J. Erskine, R. Grinover, A. Gurman, T. Hunn, D. Lee, A. Milan, T. Pham, G. Rallos, A. Razjigaev, T. Rowntree, R. Smith, K. Vijay, Z. Zhuang, C. Lehnert, I. Reid, P. Corke, J. Leitner
arXiv ID
1710.01439
Category
cs.RO: Robotics
Citations
18
Venue
IEEE International Conference on Robotics and Automation
Last Checked
4 months ago
Abstract
We present the grasping system and design approach behind Cartman, the winning entrant in the 2017 Amazon Robotics Challenge. We investigate the design processes leading up to the final iteration of the system and describe the emergent solution by comparing it with key robotics design aspects. Following our experience, we propose a new design aspect, precision vs. redundancy, that should be considered alongside the previously proposed design aspects of modularity vs. integration, generality vs. assumptions, computation vs. embodiment and planning vs. feedback. We present the grasping system behind Cartman, the winning robot in the 2017 Amazon Robotics Challenge. The system makes strong use of redundancy in design by implementing complimentary tools, a suction gripper and a parallel gripper. This multi-modal end-effector is combined with three grasp synthesis algorithms to accommodate the range of objects provided by Amazon during the challenge. We provide a detailed system description and an evaluation of its performance before discussing the broader nature of the system with respect to the key aspects of robotic design as initially proposed by the winners of the first Amazon Picking Challenge. To address the principal nature of our grasping system and the reason for its success, we propose an additional robotic design aspect `precision vs. redundancy'. The full design of our robotic system, including the end-effector, is open sourced and available at http://juxi.net/projects/AmazonRoboticsChallenge/
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