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Steering herds away from dangers in dynamic environments.

Stef Van Havermaet1, Pieter Simoens1, Tim Landgraf2

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This study introduces a decentralized multi-robot system for herding. Robots successfully guide herds to safety by detecting and avoiding dangers, ensuring cohesive group movement.

Keywords:
collective motiondecentralized decision-makingmulti-agent systemshepherding

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Collective Behavior

Background:

  • Shepherding is crucial for animal herding, crowd control, and safety.
  • Existing single-robot and centralized multi-robot solutions have limitations in unconstrained environments and danger detection.

Purpose of the Study:

  • To develop and evaluate a decentralized control algorithm for multi-robot shepherding.
  • To enable robots to detect and respond to environmental dangers while guiding a herd.

Main Methods:

  • A decentralized control algorithm using a caging pattern for danger detection.
  • Simulations of robot swarms shepherding herds with different collective motion models.
  • Testing in dynamic scenarios involving avoidance of dangerous patches and containment within a safe enclosure.

Main Results:

  • The decentralized algorithm successfully shepherded herds to safety in dynamic scenarios.
  • Success was contingent on herd cohesion and sufficient robot deployment.
  • The caging pattern effectively detected and facilitated responses to nearby dangers.

Conclusions:

  • Decentralized multi-robot shepherding is feasible and effective.
  • The proposed algorithm provides a robust solution for complex herding tasks.
  • Herd cohesion and adequate robot numbers are critical for successful shepherding outcomes.