Hybrid Voting-Based Task Assignment in Modular Construction Scenarios

TL;DR

This paper presents HVBTA, a novel hybrid voting-based framework that enhances task assignment and coordination among heterogeneous robotic teams in modular construction, leveraging voting mechanisms and large language models for improved efficiency and safety.

Contribution

The paper introduces HVBTA, integrating multiple voting methods and LLMs for nuanced task-agent suitability assessment in multi-agent construction scenarios.

Findings

HVBTA effectively assigns tasks with high accuracy in simulated environments.

The framework ensures collision-free path planning during assembly.

Preliminary evaluations show improved coordination efficiency.

Abstract

Modular construction, involving off-site prefabrication and on-site assembly, offers significant advantages but presents complex coordination challenges for robotic automation. Effective task allocation is critical for leveraging multi-agent systems (MAS) in these structured environments. This paper introduces the Hybrid Voting-Based Task Assignment (HVBTA) framework, a novel approach to optimizing collaboration between heterogeneous multi-agent construction teams. Inspired by human reasoning in task delegation, HVBTA uniquely integrates multiple voting mechanisms with the capabilities of a Large Language Model (LLM) for nuanced suitability assessment between agent capabilities and task requirements. The framework operates by assigning Capability Profiles to agents and detailed requirement lists called Task Descriptions to construction tasks, subsequently generating a quantitative Suitability Matrix. Six distinct voting methods, augmented by a pre-trained LLM, analyze this matrix to robustly identify the optimal agent for each task. Conflict-Based Search (CBS) is integrated for decentralized, collision-free path planning, ensuring efficient and safe spatio-temporal coordination of the robotic team during assembly operations. HVBTA enables efficient, conflict-free assignment and coordination, facilitating potentially faster and more accurate modular assembly. Current work is evaluating HVBTA's performance across various simulated construction scenarios involving diverse robotic platforms and task complexities. While designed as a generalizable framework for any domain with clearly definable tasks and capabilities, HVBTA will be particularly effective for addressing the demanding coordination requirements of multi-agent collaborative robotics in modular construction due to the predetermined construction planning involved.