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Towards Learning-Based Distributed Task Allocation Approach for Multi-Robot System

Chekakta, Z. ORCID: 0000-0002-4664-6283, Aouf, N., Govindaraj, S. , Polisano, F. & De Cubber, G. (2024). Towards Learning-Based Distributed Task Allocation Approach for Multi-Robot System. In: 2024 10th International Conference on Automation, Robotics and Applications (ICARA). 2024 10th International Conference on Automation, Robotics and Applications (ICARA), 22-24 Feb 2024, Athens, Greece. doi: 10.1109/icara60736.2024.10553196


This paper introduces a novel application of Graph Convolutional Networks (GCNs) for enhancing the efficiency of the Consensus-Based Bundle Algorithm (CBBA) in multi-robot task allocation scenarios. The proposed approach in this research lies in the integration of a learning-based strategy to approximate the heuristic methods traditionally used for scoring in the CBBA framework. By employing GCNs, the proposed methodology aims to learn and predict the score function, which is crucial for task allocation decisions in multi-robot systems. This approach not only streamlines the allocation process but also potentially improves the accuracy and efficiency of task distribution among robots. The paper presents a detailed exploration of how GCNs can be effectively tailored for this specific application, along with results demonstrating the advantages of this learning-based approach over conventional heuristic methods in various simulated multi-robot task allocation scenarios.

Publication Type: Conference or Workshop Item (Paper)
Additional Information: © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords: Task Allocation, Multirobot System, Distributed Algorithms, Graph Convolutional Neural Networks
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TJ Mechanical engineering and machinery
Departments: School of Science & Technology
School of Science & Technology > Engineering
SWORD Depositor:
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