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<p>This paper proposes a self-organizing decentralized control framework for modular robotic manipulation surfaces composed of locally interacting actuators. These surfaces aim to induce object translation and rotation using only local sensing and actuation, without centralized coordination or global object tracking. We introduce and systematically compare four behavior rule variants–Discrete, Logistic, Gaussian, and Fourier–under the umbrella of Swarm-Inspired Controllers. Through simulation experiments on various 2D object shapes, we evaluate positioning accuracy, orientation alignment, operation time, and robustness to actuator failure. Results show that decentralized behavior achieves positioning, orientation, and fault tolerance comparable to a centralized baseline, despite relying solely on local information. This demonstrates that fully decentralized heuristics can match centralized control in effectiveness, while offering scalability and resilience.</p>

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Swarm-inspired controllers: a comparative study of decentralized behaviors for distributed manipulation surfaces

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月7日

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  • Nicolas Bessone,
  • Kasper Stoy,
  • Payam Zahadat

摘要

This paper proposes a self-organizing decentralized control framework for modular robotic manipulation surfaces composed of locally interacting actuators. These surfaces aim to induce object translation and rotation using only local sensing and actuation, without centralized coordination or global object tracking. We introduce and systematically compare four behavior rule variants–Discrete, Logistic, Gaussian, and Fourier–under the umbrella of Swarm-Inspired Controllers. Through simulation experiments on various 2D object shapes, we evaluate positioning accuracy, orientation alignment, operation time, and robustness to actuator failure. Results show that decentralized behavior achieves positioning, orientation, and fault tolerance comparable to a centralized baseline, despite relying solely on local information. This demonstrates that fully decentralized heuristics can match centralized control in effectiveness, while offering scalability and resilience.