Design of a Multi-Modal End-Effector and Grasping System: How Integrated Design helped win the Amazon Robotics Challenge
This work addresses the challenge of robust object manipulation in warehouse automation, offering an incremental improvement by proposing a new design aspect (precision vs. redundancy) based on competition experience.
The paper tackles the problem of designing a grasping system for diverse objects in robotics competitions by presenting Cartman, the winning robot in the 2017 Amazon Robotics Challenge, which achieved success through a multi-modal end-effector combining suction and parallel grippers with three grasp synthesis algorithms.
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/