A Capability-Aware Role Allocation Approach to Industrial Assembly Tasks

• Published in: IEEE robotics and automation letters Vol. 4; no. 4; pp. 3378 - 3385
• Main Authors: Lamon, Edoardo, De Franco, Alessandro, Peternel, Luka, Ajoudani, Arash
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-10-2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; assembly; Assembly lines; Collaboration; intelligent and flexible manufacturing; Measurement; Performance indices; Physical human-robot interaction; Reconfigurable control systems; Resource management; Robot kinematics; Service robots; Task analysis; Task complexity; task planning; Workload
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2019.2926963

The deployment of industrial robotic cells based on lean manufacturing principles enables the development of fast-reconfigurable assembly lines in which human and robotic agents collaborate to achieve a shared task. To ensure the effective coordination of the shared effort, each task must be decomposed into a sequence of atomic actions that can be assigned either to a single agent or to the combination of more agents, according to a defined metric. While task allocation is a general problem and has been discussed intensively in other fields, less effort has been devoted in industrial scenarios, involving mixed human-robot teams and in particular, to the factors that should be considered in allocating tasks among a heterogeneous set of agents in collaborative manufacturing scenarios. In this letter, we investigate the agent characteristics that should be considered in the task allocation problem of fast-reconfigurable systems in industrial assembly processes. First, we introduce a set of indices, namely task complexity, agent dexterity, and agent effort, to evaluate agent performance with respect to a task. Second, we propose an offline allocation algorithm that combines the performance indices to assign optimally the task to the team agents. Finally, we validate the framework in a proof-of-concept collaborative assembly of a metallic structure. The results show that the workload is shared through the agents according to their particular physical capabilities and skill levels. A subjective analysis of the proposed collaborative framework on 12 healthy participants also validated the intuitiveness-of-use and improved performance.