A pack hunting strategy for heterogeneous robots in rescue operations

  • 0Department of Biomedical Engineering, Center for Biomedical and Robotics Technology (BART LAB), Faculty of Engineering, Nakhon Pathom 73170, Thailand.

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Summary

This summary is machine-generated.

This study enhances robot team coordination for rescue operations using pack-hunting strategies. Robots dynamically adapt roles, improving mission effectiveness and resource management in complex environments.

Area Of Science

  • Robotics
  • Artificial Intelligence
  • Swarm Intelligence

Background

  • Coordinating heterogeneous robots (UAVs, UGVs) is crucial for complex tasks like rescue operations.
  • Existing methods often rely on static roles, limiting adaptability in dynamic environments.

Purpose Of The Study

  • To develop a novel framework for improved coordination in heterogeneous robot teams.
  • To enhance the effectiveness of rescue operations through adaptive robot behavior.

Main Methods

  • Inspired by natural pack-hunting strategies, a framework combining hierarchical decision-making with decentralized control was developed.
  • Dynamic target assignment and real-time task allocation using a multi-factor scoring function (distance, energy, communication).
  • Implementation of adaptive 'Chaser' and 'Flanker' roles allowing robots to switch based on real-time data.

Main Results

  • The proposed framework demonstrated superior coordination and decision-making capabilities.
  • Robots autonomously adjusted their roles to optimize mission outcomes.
  • Improved responsiveness and efficient resource utilization in simulated rescue scenarios.

Conclusions

  • Combining hierarchical structures with decentralized control significantly enhances robot team performance.
  • The adaptive role-switching mechanism improves adaptability in complex, changing environments.
  • The framework shows strong potential for real-world applications, particularly in autonomous rescue operations.