Optimization of Multi-Agent Workflow for Human-Robot Collaboration in Assembly Manufacturing

Abstract

Human-robot collaboration presents an opportunity to improve the efficiency of manufacturing and assem-bly processes, particularly for aerospace manufacturing where tight integration and variability in the build process make physical isolation of robotic-only work challenging. In this paper, we develop a robotic schedul-ing and control capability that adapts to the changing preferences of a human co-worker or supervisor while providing strong guarantees for synchronization and timing of activities. This formulation is then expanded to optimize the workflow of a team of robots according to a set of qualitative and quantitative spatial and tempo-ral constraints and performance objectives. We describe the Adaptive Preferences Algorithm that computes the optimal flexible scheduling policy for task completion that meets hard temporal constraints. We use APA within a mixed integer multi-agent optimization algorithm that assigns a flexible schedule of agents to tasks. We show that execution of the Advanced Preferences Algorithm is fast, robust, and adaptable to changing pref-erences for workflow and that the multi-agent optimization, while slower, is practically useful for important applications in multi-robot assembly of large structures for aerospace manufacturing. We specifically demon-strate the capability for quick reoptimization of a plan in response to temporal disturbances in the schedule and changing high-level guidance from a human supervisor. I.