A hierarchical, tree-structured Bayesian intention tracker with a verification level reduces false cooperation detections and human effort in a collaborative assembly task, at the cost of longer completion times.
Insights from an Industrial Collaborative Assembly Project: Lessons in Research and Collaboration
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abstract
Significant progress in robotics reveals new opportunities to advance manufacturing. Next-generation industrial automation will require both integration of distinct robotic technologies and their application to challenging industrial environments. This paper presents lessons from a collaborative assembly project between three academic research groups and an industry partner. The goal of the project is to develop a flexible, safe, and productive manufacturing cell for sub-centimeter precision assembly. Solving this problem in a high-mix, low-volume production line motivates multiple research thrusts in robotics. This work identifies new directions in collaborative robotics for industrial applications and offers insight toward strengthening collaborations between institutions in academia and industry on the development of new technologies.
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Hierarchical Intention Tracking with Switching Trees for Real-Time Adaptation to Dynamic Human Intentions during Collaboration
A hierarchical, tree-structured Bayesian intention tracker with a verification level reduces false cooperation detections and human effort in a collaborative assembly task, at the cost of longer completion times.