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。

大规模多机器人组装规划的自主制造

Large-Scale Multi-Robot Assembly Planning for Autonomous Manufacturing

The rise of simulation environments has enabled learning-based approaches for
assembly planning, which is otherwise a labor-intensive and daunting task.
Assembling furniture is especially interesting since furniture are intricate
and pose challenges for learning-based approaches. Surprisingly, humans can
solve furniture assembly mostly given a 2D snapshot of the assembled product.
Although recent years have witnessed promising learning-based approaches for
furniture assembly, they assume the availability of correct connection labels
for each assembly step, which are expensive to obtain in practice. In this
paper, we alleviate this assumption and aim to solve furniture assembly with as
little human expertise and supervision as possible. To be specific, we assume
the availability of the assembled point cloud, and comparing the point cloud of
the current assembly and the point cloud of the target product, obtain a novel
reward signal based on two measures: Incorrectness and incompleteness. We show
that our novel reward signal can train a deep network to successfully assemble
different types of furniture. Code and networks available here:
this https URL

本文介绍了一种基于学习的方法，使用模拟环境实现家具组装规划，无需连接标签，仅依靠原始点云图像来训练神经网络以成功组装不同类型的家具。

从物体点云中学习家具装配

AssembleRL: Learning to Assemble Furniture from Their Point Clouds

For planning an assembly of a product from a given set of parts, robots
necessitate certain cognitive skills: high-level planning is needed to decide
the order of actuation actions, while geometric reasoning is needed to check
the feasibility of these actions. For collaborative assembly tasks with humans,
robots require further cognitive capabilities, such as commonsense reasoning,
sensing, and communication skills, not only to cope with the uncertainty caused
by incomplete knowledge about the humans' behaviors but also to ensure safer
collaborations. We propose a novel method for collaborative assembly planning
under uncertainty, that utilizes hybrid conditional planning extended with
commonsense reasoning and a rich set of communication actions for collaborative
tasks. Our method is based on answer set programming. We show the applicability
of our approach in a real-world assembly domain, where a bi-manual Baxter robot
collaborates with a human teammate to assemble furniture. This manuscript is
under consideration for acceptance in TPLP.

提出一种基于 answer set programming 的协作装配方法，结合常识推理和丰富的通信行为，用于应对组装过程中的不确定性，适用于现实世界中的协作任务，如家具组装等。