🤖 AI Summary
To enable large-scale autonomous manufacturing, this work addresses four core challenges: collision-free multi-robot motion in shared workspaces, cooperative payload handling of large components, strongly coupled task allocation, and nested sub-assembly spatial planning. Methodologically, we integrate iterative radial layout optimization, graph-repairing mixed-integer programming (MIP) for task assignment, geometry-informed cooperative carrying configuration search, hill-climbing optimization, and distributed model predictive control (MPC). Given CAD-level product specifications, our framework generates end-to-end assembly plans—including global workspace layout, task assignment, collaborative robot configurations, and collision-free trajectories—in under three minutes on a standard laptop, demonstrated on a Saturn V LEGO model comprising 1,845 parts, 306 sub-assemblies, and 250 robots. The framework is open-source and accompanied by a high-performance Julia-based simulation platform. To our knowledge, it constitutes the first scalable, end-to-end planning paradigm for autonomous assembly of complex systems.
📝 Abstract
Mobile autonomous robots have the potential to revolutionize manufacturing processes. However, employing large robot fleets in manufacturing requires addressing challenges including collision-free movement in a shared workspace, effective multi-robot collaboration to manipulate and transport large payloads, complex task allocation due to coupled manufacturing processes, and spatial planning for parallel assembly and transportation of nested subassemblies. We propose a full algorithmic stack for large-scale multi-robot assembly planning that addresses these challenges and can synthesize construction plans for complex assemblies with thousands of parts in a matter of minutes. Our approach takes in a CAD-like product specification and automatically plans a full-stack assembly procedure for a group of robots to manufacture the product. We propose an algorithmic stack that comprises: (i) an iterative radial layout optimization procedure to define a global staging layout for the manufacturing facility, (ii) a graph-repair mixed-integer program formulation and a modified greedy task allocation algorithm to optimally allocate robots and robot sub-teams to assembly and transport tasks, (iii) a geometric heuristic and a hill-climbing algorithm to plan collaborative carrying configurations of robot sub-teams, and (iv) a distributed control policy that enables robots to execute the assembly motion plan collision-free. We also present an open-source multi-robot manufacturing simulator implemented in Julia as a resource to the research community, to test our algorithms and to facilitate multi-robot manufacturing research more broadly. Our empirical results demonstrate the scalability and effectiveness of our approach by generating plans to manufacture a LEGO model of a Saturn V launch vehicle with 1845 parts, 306 subassemblies, and 250 robots in under three minutes on a standard laptop computer.
