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Updated: Sep 29, 2025

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Fast Multi-UAV Path Planning for Optimal Area Coverage in Aerial Sensing Applications.

Marco Andrés Luna1,2, Mohammad Sadeq Ale Isaac1,2, Ahmed Refaat Ragab2,3,4

  • 1Computer Vision and Aerial Robotics Group, Centre for Automation and Robotics, Universidad Politécnica de Madrid, 28040 Madrid, Spain.

Sensors (Basel, Switzerland)
|March 26, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces optimized path planning for multiple drones (UAVs) for rapid area coverage. The Powell optimized bin packing trajectory planner (POWELL-BINPAT) method achieves the fastest mission completion times.

Keywords:
aerial sensingbin-packing problemcoverage path planningmulti-UAV

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Science

Background:

  • Coverage Path Planning (CPP) is crucial for Unmanned Aerial Vehicle (UAV) missions.
  • Efficient multi-UAV coordination for large-scale coverage remains a challenge.
  • Existing methods often lack real-time optimization for dynamic environments.

Purpose of the Study:

  • To develop and analyze fast coverage path planning solutions for multiple UAVs.
  • To implement a software framework and algorithms for efficient UAV trajectory generation.
  • To provide a practical tool for first responders like police and firefighters.

Main Methods:

  • A back-and-forth path generation algorithm based on sensor footprint.
  • Three path assignment methods: SIMPLE-BINPAT, BINPAT, and POWELL-BINPAT.
  • Utilized heuristic algorithms, linear sum assignment, and minimization techniques for optimization.

Main Results:

  • POWELL-BINPAT demonstrated superior performance in generating optimal UAV paths for minimum mission time.
  • The proposed approach significantly reduced computation time for trajectory generation compared to existing literature.
  • Simulations and real-world experiments validated the effectiveness of the POWELL-BINPAT method with RGB and thermal cameras.

Conclusions:

  • The POWELL-BINPAT method offers an effective solution for fast and optimal multi-UAV coverage path planning.
  • The developed software framework is practical for deployment by first responders.
  • This research contributes to advancing efficient autonomous systems for critical applications.