Electrical and Computer Engineering Faculty Publications

Document Type

Conference Proceeding

Publication Date

12-2025

Abstract

This paper presents an Auction-Consensus Algorithm with a Loss Mechanism (ACALM), a decentralized task allocation method for multi-robot systems that enhances the existing Consensus-Based Auction Algorithm (CBAA) by incorporating a novel loss propagation mechanism. In contrast to purely greedy bidding strategies, it enables agents to dynamically update task priorities based on the accumulated loss from previously unsuccessful bids. This extended work reduces globally inefficient allocations caused by early suboptimal decisions. The proposed approach is evaluated through large-scale simulations in thousands of randomized scenarios and swarm sizes ranging from 5 to 120 robots. Compared to existing CBAA and GCAA algorithms, ACALM yields task assignments with higher global efficiency on average. The results also show that ACALM maintains effectiveness with larger swarms, suggesting strong robustness in large decentralized contexts. However, this improvement comes with an increase in communication overhead due to additional consensus rounds. Potential extensions include methods to reduce communication costs, support for multiple assignments per robot, and verification of functionality under asynchronous communication.

Comments

©2025 IEEE

DOI

10.1109/MRS66243.2025.11357252

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