Varying Bundle Size Reactive Multi-Task Assignment using Selective Cost Estimation for Multi-Agent Systems

2026-06-23

AI Summary

This work addresses the challenge of real-time multi-agent task allocation, which is often hindered by the high computational cost of high-fidelity path planning. To overcome this limitation, the authors propose a distributed two-stage, multi-fidelity bundle generation framework. In the first stage, low-fidelity heuristics rapidly explore the space of possible task bundles; in the second stage, only high-potential candidates undergo computationally expensive, high-fidelity path planning. A central coordinator then solves a set-packing problem to ensure global feasibility and utility maximization. The approach supports dynamic bundle sizes and enables reactive, real-time assignment while preserving agents’ internal states and proprietary cost models. Experimental results across diverse simulation scenarios demonstrate that the framework significantly enhances the performance of reactive auction-based task allocation, achieving both efficiency and scalability in multi-robot coordination.

Abstract

This paper presents a scalable framework for multi-robot task allocation in complex environments where estimating task execution costs is computationally expensive. While combinatorial auction-based approaches offer reliable solutions, the exponential complexity of bundle generation typically renders them intractable for real-time reactive applications, particularly when accurate path planning is required for cost validation. We address this through a distributed, two-stage multi-fidelity bundle generation approach. Agents utilize a local search tree guided by a low-fidelity heuristic (such as euclidean distance) to rapidly explore the bundle space, applying high-fidelity path planning only to the most promising candidates in a best-first manner. These refined bids are then submitted to a central coordinator that solves a set packing problem to ensure global feasibility and maximize the overall utility. Simulation results in multiple environments demonstrate that the framework is able to improve the performance of reactive auction-based task allocation. Overall, the presented framework is shown to enable reactive task allocation with dynamic bundle sizes in multiple settings without exposing the agents' state and internal cost estimation models.

Problem

• multi-agent systems
• task allocation
• combinatorial auctions
• bundle generation
• cost estimation

Innovation

• multi-fidelity bundle generation
• reactive multi-task assignment
• combinatorial auction
• distributed task allocation
• high-fidelity path planning