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<p>This paper presents a multi-layered framework for fully distributed and decentralized task allocation in heterogeneous swarms, derived from interaction-based dynamics. This approach is built upon a baseline of homogeneous swarms composed of simple, autonomous, identical, locally sensing agents. Agents operate under the “flexible swarm” paradigm, which promotes global cohesion through local interactions that prevent swarm fragmentation. We demonstrate how a desired swarm behavior of exploration, task detection, and requirement fulfillment can emerge from local motion rules based solely on self-sensing without explicit inter-agent communication. Starting from a homogeneous baseline of identical agents, we extend the framework to heterogeneous swarms composed of agents with varying sensing capabilities and payloads. We introduce a novel interaction scheme in which two homogeneous groups, scouts and fire-extinguishers, are functionally coupled through local sensing, forming an emergent heterogeneous system. This coupled coordination enables distributed fire detection and suppression, enhancing robustness and scalability. The framework is validated through firefighting simulations, utilizing a heterogeneous swarm composed of scout agents with long-range sensing and fire-extinguishing agents with limited perception and actuation abilities. Through simple local motion rules, agents self-organize to detect and extinguish fires in a cooperative manner, driven solely by local sensing and interaction forces. Various fire suppression simulations demonstrate clear trade-offs between scouting and fire-extinguishing capacities. Our simulation results show that scouting dominates overall performance, whereas adding more fire-extinguishers provides only marginal improvement beyond a critical ratio. The results demonstrate that decentralized control of heterogeneous swarms enhances scalability, robustness, and efficiency even under direct communication denial. A simulation video demonstrating the proposed framework, illustrating the emergent coordination, task allocation dynamics, and analysis of a specific scenario, is available at <a href="https://youtu.be/dZqVxlhhzYc">https://youtu.be/dZqVxlhhzYc</a>.</p>

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Distributed and decentralized task allocation for heterogeneous swarms

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月28日

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  • Yigal Koifman,
  • Ariel Barel,
  • Alfred M. Bruckstein

摘要

This paper presents a multi-layered framework for fully distributed and decentralized task allocation in heterogeneous swarms, derived from interaction-based dynamics. This approach is built upon a baseline of homogeneous swarms composed of simple, autonomous, identical, locally sensing agents. Agents operate under the “flexible swarm” paradigm, which promotes global cohesion through local interactions that prevent swarm fragmentation. We demonstrate how a desired swarm behavior of exploration, task detection, and requirement fulfillment can emerge from local motion rules based solely on self-sensing without explicit inter-agent communication. Starting from a homogeneous baseline of identical agents, we extend the framework to heterogeneous swarms composed of agents with varying sensing capabilities and payloads. We introduce a novel interaction scheme in which two homogeneous groups, scouts and fire-extinguishers, are functionally coupled through local sensing, forming an emergent heterogeneous system. This coupled coordination enables distributed fire detection and suppression, enhancing robustness and scalability. The framework is validated through firefighting simulations, utilizing a heterogeneous swarm composed of scout agents with long-range sensing and fire-extinguishing agents with limited perception and actuation abilities. Through simple local motion rules, agents self-organize to detect and extinguish fires in a cooperative manner, driven solely by local sensing and interaction forces. Various fire suppression simulations demonstrate clear trade-offs between scouting and fire-extinguishing capacities. Our simulation results show that scouting dominates overall performance, whereas adding more fire-extinguishers provides only marginal improvement beyond a critical ratio. The results demonstrate that decentralized control of heterogeneous swarms enhances scalability, robustness, and efficiency even under direct communication denial. A simulation video demonstrating the proposed framework, illustrating the emergent coordination, task allocation dynamics, and analysis of a specific scenario, is available at https://youtu.be/dZqVxlhhzYc.