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Pigeon interaction mode switch-based UAV distributed flocking control under obstacle environments.

Huaxin Qiu1, Haibin Duan1

  • 1Key Laboratory of Virtual Reality Technology and Systems, School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing 100083, PR China.

ISA Transactions
|August 2, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel UAV flocking control algorithm inspired by pigeon behavior. It enables heterogeneous drone swarms to navigate obstacles effectively using distributed control with few informed agents.

Keywords:
Coordinated obstacle avoidanceFlocking controlLocal minimumPigeon flockUnmanned aerial vehicle

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Area of Science:

  • Robotics and Control Systems
  • Artificial Intelligence
  • Aerospace Engineering

Background:

  • Coordinating Unmanned Aerial Vehicle (UAV) swarms presents significant challenges, particularly in dynamic environments.
  • Existing flocking control algorithms often struggle with heterogeneous swarms and complex obstacle avoidance.
  • Understanding biological flocking behavior offers insights for developing robust artificial swarm systems.

Purpose of the Study:

  • To develop a distributed flocking control algorithm for heterogeneous UAV swarms.
  • To enable UAV swarms to navigate complex obstacle environments autonomously.
  • To leverage pigeon flocking behavior for enhanced swarm coordination.

Main Methods:

  • Proposed a pigeon flocking model incorporating hierarchical and egalitarian interaction modes.
  • Developed a coordinated obstacle-avoiding model based on pigeon behavior.
  • Designed a distributed flocking control algorithm for heterogeneous UAV swarms.
  • Conducted comparative simulations to validate the algorithm's performance.

Main Results:

  • The proposed algorithm successfully coordinated a heterogeneous UAV swarm through obstacle environments.
  • Simulations demonstrated the feasibility and validity of the developed flocking and obstacle-avoiding models.
  • The algorithm showed superiority compared to existing methods in complex scenarios.

Conclusions:

  • The novel algorithm effectively addresses UAV flocking control challenges in dynamic environments.
  • The pigeon-inspired models provide a robust framework for distributed swarm coordination.
  • This research contributes to the advancement of autonomous heterogeneous UAV swarm navigation.