# Large-Scale Multi-Robot Assembly Planning for Autonomous Manufacturing

arXiv: https://arxiv.org/abs/2311.00192 · HTML: https://mlsanity.com/paper/2311.00192/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 38 references that are in the map.
- First posted on arXiv 31 Oct 2023.

## Builds on (most cited first, 5 of 5)

- [Integrated Task and Motion Planning](https://mlsanity.com/paper/2010.01083/) (2020)
- [Optimal Sequential Task Assignment and Path Finding for Multi-Agent Robotic Assembly Planning](https://mlsanity.com/paper/2006.08845/) (2020)
- [Robust Task and Motion Planning for Long-Horizon Architectural Construction Planning](https://mlsanity.com/paper/2003.07754/) (2020)
- [Heterogeneous Coalition Formation and Scheduling with Multi-Skilled Robots](https://mlsanity.com/paper/2306.11936/) (2023)
- [Efficient Trajectory Planning for Multiple Non-holonomic Mobile Robots via Prioritized Trajectory Optimization](https://mlsanity.com/paper/2012.08135/) (2020)

## Abstract (arXiv)

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.