Scalable PSO-Enhanced Fuzzy Decision-Making for Decentralized Task Allocation in Multi-Robot Systems

Abstract

This paper presents a novel fuzzy decision-making framework for decentralized task allocation in heterogeneous multi-robot systems, enhanced through particle swarm optimization. The proposed model, called Particle Swarm Optimization of Fuzzy Decision-Making for Task Selection (PSO-FDM-TS), automates the tuning of fuzzy membership function parameters to improve adaptability and robustness in uncertain environments with limited communication. Through extensive simulations in a warehouse transportation scenario, PSO-FDM-TS achieves a significant reduction in task completion error compared to both manually-tuned fuzzy systems and observation-based allocation approaches. Moreover, the results show that PSO-FDM-TS maintains superior performance across diverse operational conditions, including varying task heterogeneity, different robot team sizes, and large-scale deployments without the need for inter-robot communication. This work represents a key step toward scalable and efficient coordination of heterogeneous robot teams in real-world applications such as automated logistics and mission adaptation in hostile environments.

DOI: 10.61416/ceai.v28i2.9804