Hybrid Voting-Based Task Assignment in Modular Construction Scenarios

2025-05-19

AI Summary

To address the challenges of task allocation and spatiotemporal coordination—particularly collision-prone conflicts—in heterogeneous multi-robot teams for modular construction, this paper proposes a collaborative decision-making framework integrating large language models (LLMs) with multi-mechanism voting. The LLM is innovatively embedded within the voting process to generate fine-grained capability–task compatibility matrices; these are coupled with Conflict-Based Search (CBS) for decentralized, collision-free path planning, thereby bridging human-like delegation reasoning with robotic execution. Capability profiling and structured task representation significantly enhance task matching accuracy and execution robustness. Experimental evaluation in multi-robot simulated assembly scenarios demonstrates substantial reductions in assembly cycle time, improved pose estimation accuracy, and cross-platform generalizability for deployment.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Large language models for search

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.

Problem

• Optimizing task allocation in modular construction using multi-agent systems
• Integrating voting mechanisms with LLM for agent-task suitability assessment
• Ensuring collision-free path planning for robotic team coordination

Innovation

• Hybrid Voting-Based Task Assignment framework
• Integrates voting mechanisms with LLM
• Conflict-Based Search for path planning