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Adaptive behaviors in multi-agent source localization using passive sensing.

Mansoor Shaukat1, Mandar Chitre1

  • 1Acoustics Research Laboratory, Tropical Marine Sciences Institute, Singapore.

Adaptive Behavior
|December 27, 2016
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Summary
This summary is machine-generated.

Adaptive group cohesion enables cooperative multi-agent systems to achieve source localization. This emergent behavior, driven by agent interactions, ensures team connectivity for efficient and robust localization, even with environmental challenges.

Keywords:
Swarm intelligenceautonomous robotscollective behaviorcooperative source localizationmulti-agent systems

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Distributed Systems

Background:

  • Cooperative multi-agent systems require robust source localization strategies.
  • Individual agents often lack sufficient sensing or processing capabilities for autonomous localization.
  • Maintaining group cohesion is critical for decentralized task achievement.

Purpose of the Study:

  • Investigate the role of adaptive group cohesion in cooperative multi-agent source localization.
  • Develop a distributed source localization algorithm for homogeneous agents.
  • Optimize agent behaviors for efficient and connected localization.

Main Methods:

  • A distributed algorithm combining individualistic and social behaviors was developed.
  • Agents use gradient and neighbor sensing for localization.
  • Two temporal sampling behaviors (intensity-based and connectivity-based adaptation) were implemented.
  • A two-phase evolutionary optimization process was used to tune agent behaviors.

Main Results:

  • Source localization emerged as a collective property from adaptive agent interactions.
  • Connectivity-based adaptation minimized agent breakaways, ensuring team cohesion.
  • The optimized behaviors demonstrated robustness against sensor noise, multi-path interference, and signal loss.

Conclusions:

  • Adaptive group cohesion is a key factor for successful cooperative source localization.
  • The proposed distributed algorithm achieves emergent localization through simple agent interactions.
  • The strategy is effective even in challenging environments with noise and interference.