Autonomous Pick and Place for Mechanical Assembly WRS 2020 Assembly Challenge

Abstract

The World Robot Summit (WRS) 2020 Assembly Challenge aims to realize the future of industrial robotics by building agile and lean production systems that can (1) respond to ever-changing manufacturing requirements for high-mix low-volume production, (2) be configured easily without paying a high cost for system integration, (3) be reconfigured in an agile and lean manner, and (4) be implemented as agile one-off manufacturing for the industry. One of the technical challenges of assembly is the detection, identification, and classification of a small object in an unstructured environment to grasp, manipulate, and insert it into the mating part to assemble the mechanical system. We presented an end-to-end system solution based on flexibility, modularity, and robustness with integrated machine vision and robust planning and execution. To achieve this, we utilized the behavior tree paradigm and a hierarchy of recovery strategies to ensure effective operation. We selected the behavior tree approach because of its design efficiency in creating complex systems that are modular, hierarchical, and reactive, and offer graphical representations with semantic meaning. These properties are essential for any part assembly task. Our system was selected for the WRS 2020 Assembly Challenge finals based on its successful performance in qualifying rounds.