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Multi-Target Coordinated Search Algorithm for Swarm Robotics Considering Practical Constraints.

You Zhou1,2, Anhua Chen1, Xinjie He3

  • 1Department of Mechanical and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, China.

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Summary
This summary is machine-generated.

This study introduces a new coordinated search algorithm for swarm robots navigating complex environments. The algorithm enhances target detection and obstacle avoidance, improving efficiency and practicality.

Keywords:
coordinated searchdistributed neighborhood communicationmulti-target searchroaming searchsimplified virtual force modelswarm robots

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Swarm Intelligence

Background:

  • Multi-target search in unknown complex environments poses significant challenges for swarm robots.
  • Existing algorithms often struggle with real-time obstacle avoidance and distributed communication.

Purpose of the Study:

  • To propose a novel multi-target coordinated search algorithm for swarm robots.
  • To address practical constraints including robot kinematics, obstacle avoidance, and communication.

Main Methods:

  • Developed an ideal search algorithm framework combining roaming and coordinated search strategies.
  • Integrated a simplified virtual force model for real-time obstacle avoidance.
  • Proposed a distributed neighborhood communication mechanism with restricted random line of sight.

Main Results:

  • The proposed algorithm demonstrates enhanced stability, efficiency, and practicality compared to previous methods.
  • Numerical simulations verified the effectiveness of the coordinated search strategy under various constraints.
  • The algorithm successfully manages swarm robot kinematics, obstacle avoidance, and communication.

Conclusions:

  • The novel algorithm offers a robust solution for multi-target search problems in complex environments.
  • This approach improves the operational capabilities of swarm robots in real-world applications.
  • The study validates the effectiveness of the integrated search, obstacle avoidance, and communication strategies.