Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Hybrid Voting-Based Task Assignment in Modular Construction Scenarios

Daniel Weiner, Raj Korpan

TL;DR

This paper addresses the complex coordination and task allocation challenges in modular construction with heterogeneous multi-agent systems. It introduces HVBTA, a hybrid framework that fuses structured Task Descriptions and Capability Profiles with a multi-rule voting scheme, LLM-based semantic reasoning, and Conflict-Based Search for collision-free path planning. Key contributions include the Suitability Matrix concept, six voting methods with LLM tie-breaking, and the integration of CBS to derive safe execution plans. The approach promises more efficient, scalable, and safer modular assembly by front-loading capability-aware allocation and dynamic path planning, with ongoing simulations evaluating performance across varied robotic platforms and task complexities.

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.

Hybrid Voting-Based Task Assignment in Modular Construction Scenarios

TL;DR

This paper addresses the complex coordination and task allocation challenges in modular construction with heterogeneous multi-agent systems. It introduces HVBTA, a hybrid framework that fuses structured Task Descriptions and Capability Profiles with a multi-rule voting scheme, LLM-based semantic reasoning, and Conflict-Based Search for collision-free path planning. Key contributions include the Suitability Matrix concept, six voting methods with LLM tie-breaking, and the integration of CBS to derive safe execution plans. The approach promises more efficient, scalable, and safer modular assembly by front-loading capability-aware allocation and dynamic path planning, with ongoing simulations evaluating performance across varied robotic platforms and task complexities.

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.
Paper Structure (18 sections, 1 figure, 1 table)

This paper contains 18 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Optimization-Based Approaches
  4. Market and Auction‑Based Methods
  5. LLMs for Semantic Matching
  6. Multi-Agent Path Finding
  7. The HVBTA Framework
  8. Task Descriptions and Agent Capability Profiles
  9. Suitability Matrix Generation
  10. LLM Integration for Semantic Reasoning
  11. Voting and Allocation Mechanism
  12. CBS for Path Planning
  13. Applying HVBTA to Multi-agent Construction
  14. Handling Heterogeneity
  15. Efficient Task Assignment
  16. ...and 3 more sections

Figures (1)

  • Figure 1: Diagram of the HVBTA system. The Suitability Matrix is created from the Task Descriptions and Capability Profiles when all task requirements are well defined, otherwise, LLM integration is utilized to score suitability, then all scores are passed to voting methods for task allocation. Finally CBS plans paths to the tasks for each agent.