HAC-FRL: A learning-driven distributed task allocation framework for large-scale warehouse automation

Abstract

Hybrid Auction-Consensus with Fine-tuned Recurrent Learning (HAC-FRL) is a framework that is presented in this research for the purpose of distributed task allocation in large-scale warehouse automation. For the purpose of enhancing conflict resolution, accelerating recovery, and optimizing energy use, HAC-FRL incorporates proximal optimization with a mine blast algorithm for training data execution. Unlike previous approaches, which are plagued by agent conflicts, inefficient learning, and poor deadlock recovery, HAC-FRL gives robots the ability to dynamically alter their strategy prior to the assignment of tasks. When compared to baseline approaches, simulation trials using 1000 robots and 5000 tasks indicate considerable gains. These advantages include a 26.2 % increase in task success rate, a 1.24 % reduction in deadlocks, an 84 % faster recovery, a 38 % higher energy efficiency, and a 62 % lower message loss. In light of these findings, it is clear that HAC-FRL offers a solution that is both fault-tolerant and scalable, enabling multi-agent task allocation that is both reliable and efficient in terms of energy consumption for mission-critical systems. The system that has been suggested improves the dependability and scalability of warehouse automation by guaranteeing that learning is efficient and distributed coordination is resilient.