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.

移动自主机器人在制造过程中具有改革性潜力。我们提出了一个完整的算法堆栈，用于解决制造中的挑战，并能够在几分钟内为成千上万个零部件设计复杂的装配计划。