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This study introduces an intermittent search strategy for autonomous agents performing area-restricted target detection. The decentralized approach enhances underwater surveillance by coordinating drone swarms effectively.

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Area of Science:

  • Robotics
  • Autonomous Systems
  • Search Theory

Background:

  • Area-restricted search problems involve locating targets within defined boundaries.
  • Autonomous mobile sensing platforms (agents) are increasingly used for surveillance.
  • Underwater environments present unique challenges for sensing and communication.

Purpose of the Study:

  • To develop a decentralized, information-driven search strategy for a team of autonomous agents.
  • To address the challenges of range-dependent detection probability and false alarms in sensing.
  • To enable efficient underwater surveillance using amphibious drones.

Main Methods:

  • A novel intermittent search strategy alternating between ballistic (fast displacement) and diffusive (slow sensing) phases.
  • Decentralized computation for estimation and motion control, relying on local data.
  • Neighbor-only data exchange for agent coordination, eliminating the need for global network knowledge.

Main Results:

  • The proposed strategy enables coordinated multi-agent search in a decentralized manner.
  • Effective target detection is achieved despite range-dependent sensing probabilities and false alarms.
  • The approach facilitates efficient underwater surveillance with amphibious drone swarms.

Conclusions:

  • The intermittent information-driven search strategy is effective for decentralized multi-agent systems.
  • This method offers a robust solution for underwater surveillance and area-restricted search problems.
  • Local computations and neighbor-only communication ensure scalability and adaptability.