AI-Based Dynamic Task Allocation in Multi-Robot Systems

  • Authors

    • Alexey Lyapunov Professor, Moscow State University, Russia Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-2025PI8N3Z

    Published 07-21-2026

  • Artificial Intelligence, Multi-Robot Systems, Dynamic Task Allocation, Reinforcement Learning, Swarm Intelligence, Machine Learning, Autonomous Robots, Cooperative Robotics, Intelligent Scheduling, Optimization Algorithms, Distributed Robotics, Industry 5.0

    Issue

    Vol. 8 No. 1 (2025)

    Section

    Articles

    How to Cite

    [1]
    A. Lyapunov, “AI-Based Dynamic Task Allocation in Multi-Robot Systems”, IJIARE, vol. 8, no. 1, pp. 01–16, Jul. 2026, doi: 10.67228/30715725/IJIARE-2025PI8N3Z.

  • Abstract

    The rapid advancement of artificial intelligence (AI), autonomous robotics, and distributed computing has significantly improved the capabilities of multi-robot systems (MRS) across applications such as warehouse automation, disaster response, healthcare, precision agriculture, intelligent transportation, and smart manufacturing. A key challenge in these systems is dynamic task allocation, where robots must efficiently assign and reassign tasks in response to changing environments, communication constraints, resource limitations, and robot failures. Conventional approaches often face limitations in scalability, computational efficiency, and adaptability.This paper proposes an AI-based dynamic task allocation framework that integrates machine learning, reinforcement learning, swarm intelligence, and optimization techniques to enable intelligent and adaptive decision-making in heterogeneous multi-robot systems. The framework considers robot capabilities, task priorities, battery levels, communication quality, travel distance, and workload balancing to optimize real-time task allocation. Reinforcement learning supports adaptive policy learning, while swarm intelligence enables decentralized cooperation. Graph-based task modeling and utility-based optimization further improve resource utilization and minimize execution time, energy consumption, and task conflicts.Experimental evaluation using metrics such as task completion rate, response time, energy efficiency, workload distribution, and scalability demonstrates that the proposed framework outperforms conventional scheduling methods by providing improved adaptability, fault tolerance, and operational efficiency. The proposed approach offers a scalable and intelligent solution for next-generation multi-robot collaboration in Industry 5.0 and cyber-physical systems.

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