Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly

TL;DR

This paper introduces a scalable multi-robot planning system for construction assembly that handles large teams and complex tasks over long horizons, integrating optimization and sampling-based planning.

Contribution

It extends prior work by enabling long-horizon, multi-robot planning with heterogeneous teams through an iterative, subproblem-solving approach combining optimization and sampling-based methods.

Findings

Demonstrates robustness over long planning horizons

Shows scalability to many objects and robots

Validates real-world execution on robot arms

Abstract

Robotic assembly planning enables architects to explicitly account for the assembly process during the design phase, and enables efficient building methods that profit from the robots' different capabilities. Previous work has addressed planning of robot assembly sequences and identifying the feasibility of architectural designs. This paper extends previous work by enabling planning with large, heterogeneous teams of robots. We present a planning system which enables parallelization of complex task and motion planning problems by iteratively solving smaller subproblems. Combining optimization methods to solve for manipulation constraints with a sampling-based bi-directional space-time path planner enables us to plan cooperative multi-robot manipulation with unknown arrival-times. Thus, our solver allows for completing subproblems and tasks with differing timescales and synchronizes them effectively. We demonstrate the approach on multiple case-studies to show the robustness over long planning horizons and scalability to many objects and agents of our algorithm. Finally, we also demonstrate the execution of the computed plans on two robot arms to showcase the feasibility in the real world.