A Stochastic Approach to Generate Emergent Behaviors in Robotic Swarms

• Paulo Rezeck UFMG
• Luiz Chaimowicz UFMG

DOI: https://doi.org/10.5753/ctd.2024.2302

Resumo

This work proposes a stochastic methodology utilizing Gibbs Random Fields (GRFs) to induce diverse emergent behaviors in robotic swarms, including flocking, segregation, cooperative object transportation, and pattern formation. Relying solely on local information and decentralized control mechanisms, our approach enables robust and scalable swarm behaviors. Through numerical simulations and real-world experiments with HeRo 2.0, a low-cost swarm robotic platform developed as part of this work, we demonstrate the benefits of the proposed methodology, including adaptability, robustness, and resilience. Overall, our stochastic approach holds promise for advancing swarm robotics by enabling the generation of versatile behaviors, contributing to the field’s evolution and showing potential for new applications.

Palavras-chave: Robotics, Multi-agent system, Swarm robotics, Probabilistic robotics

Referências

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