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Multi UAV Coverage Path Planning in Urban Environments.

Javier Muñoz1, Blanca López1, Fernando Quevedo1

  • 1Robotics Lab, Universidad Carlos III de Madrid, Av. Madrid 30, 28911 Leganés, Spain.

Sensors (Basel, Switzerland)
|November 13, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces novel algorithms for Unmanned Aerial Vehicle (UAV) coverage path planning (CPP) in urban areas. The developed methods ensure efficient, collision-free flight paths for UAV formations, enhancing safety and mission success.

Keywords:
UAVscoverage path planningfast marchingpath planning

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Area of Science:

  • Robotics and Automation
  • Aerospace Engineering
  • Computer Science

Background:

  • Coverage Path Planning (CPP) is crucial for Unmanned Aerial Vehicle (UAV) applications like surveillance and mapping.
  • Urban environments present significant challenges for UAV navigation due to clutter and obstacles.
  • Existing CPP algorithms often lack robust collision avoidance for multi-UAV systems in complex settings.

Purpose of the Study:

  • To design efficient, collision-avoidance capable coverage path planning algorithms for single or multi-UAV systems.
  • To enable UAVs to safely and smoothly cover target zones or survey predefined viewpoints in cluttered urban environments.
  • To develop scalable algorithms adaptable to varying numbers of UAVs and formations.

Main Methods:

  • Development of two distinct CPP algorithms: one utilizing a boustrophedon pattern for area coverage and another for viewpoint-based path planning.
  • Implementation of a deformable triangular leader-follower formation for multi-UAV coordination, including a virtual leader strategy for even UAV numbers.
  • Integration of the Fast Marching Square algorithm for robust collision avoidance capabilities.
  • Simulation-based testing in diverse, cluttered urban environments to validate algorithm performance.

Main Results:

  • Both developed algorithms successfully generated safe and smooth paths for UAV formations in simulated urban environments.
  • The algorithms demonstrated scalability for a variable number of UAVs operating in coordinated formations.
  • Collision avoidance was effectively managed, ensuring operational safety within complex, cluttered settings.
  • The viewpoint-based algorithm ensured UAVs passed smoothly over designated objectives.

Conclusions:

  • The proposed CPP algorithms offer effective solutions for UAV navigation and coverage in challenging urban landscapes.
  • The integration of advanced collision avoidance and formation strategies enhances the reliability and safety of multi-UAV operations.
  • These algorithms represent a significant advancement in enabling proficient UAV deployment for various urban applications.