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A collective intelligence model for swarm robotics applications.

Alessandro Nitti1, Marco D de Tullio2, Ivan Federico3

  • 1Department of Mechanics Mathematics and Management, Polytechnic University of Bari, Via Edoardo Orabona 4, Bari, Italy. alessandro.nitti@poliba.it.

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This study introduces a novel swarm cooperation model for multi-agent systems, enhancing reliability and performance in practical applications. The model effectively optimizes tasks and controls vehicles, demonstrating superior results in complex environments.

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Area of Science:

  • Robotics and Artificial Intelligence
  • Multi-agent Systems
  • Optimization Algorithms

Background:

  • Swarm intelligence models offer potential for complex tasks but lack practical decentralized cooperation logic.
  • Current models face challenges in reliability with small swarms and parameter efficiency.
  • Existing techniques often prioritize computational optimization over real-world applicability.

Purpose of the Study:

  • To develop a robust swarm cooperation model applicable to both virtual optimization and real-world vehicle control.
  • To improve model reliability with limited agents and minimal parameters.
  • To bridge the gap between theoretical swarm intelligence and practical decentralized applications.

Main Methods:

  • Integration of meta-heuristic methods and consensus theory to create a novel swarm cooperation model.
  • Evaluation against benchmark methods on diverse optimization landscapes.
  • Computational proof of concept for autonomous underwater vehicle (AUV) control in marine environments.

Main Results:

  • The proposed model achieved higher or equal success rates compared to benchmarks on 22 out of 33 landscapes with ≤16 agents and low-dimensional problems.
  • Demonstrated effectiveness in multimodal optimization tasks.
  • Successfully controlled AUVs for contaminant localization in a complex marine environment.

Conclusions:

  • The developed swarm cooperation model offers a practical and effective solution for decentralized multi-agent systems.
  • The model shows significant potential for improving reliability and performance in real-world applications, including environmental monitoring.
  • This approach advances the application of swarm intelligence beyond theoretical optimization to tangible control tasks.