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Related Experiment Videos

Coastal Areas Division and Coverage with Multiple UAVs for Remote Sensing.

Fotios Balampanis1, Iván Maza2, Aníbal Ollero3

  • 1Robotics, Vision and Control Group, Universidad de Sevilla, Avda. de los Descubrimientos s/n, 41092 Seville, Spain. fbalampanis@us.es.

Sensors (Basel, Switzerland)
|April 12, 2017
PubMed
Summary

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This study presents a novel method for dividing coastal areas for multiple unmanned aerial vehicles (UAVs), ensuring efficient coverage and resource allocation. The approach effectively handles complex scenarios and optimizes UAV deployment for surveillance missions.

Area of Science:

  • Robotics and Autonomous Systems
  • Geospatial Analysis
  • Operations Research

Background:

  • Coordinating heterogeneous unmanned aerial vehicles (UAVs) for area surveillance presents challenges in task allocation and coverage.
  • Existing methods often struggle with sensor limitations and dynamic environmental factors in complex terrains like coastal regions.

Purpose of the Study:

  • To develop and evaluate an algorithm for exact cell decomposition and partitioning of coastal regions for heterogeneous UAV teams.
  • To account for onboard sensor capabilities (field of view/sensing radius) in the partitioning process.
  • To address and resolve deadlock situations and resource allocation issues in UAV mission planning.

Main Methods:

  • An initial sensor-based exact cell decomposition is performed.
Keywords:
Unmanned Aerial Vehiclesarea partitioncell decompositionremote sensors

Related Experiment Videos

  • A growing regions algorithm is used for isotropic partitioning based on UAV locations and capabilities.
  • Two novel algorithms are applied to adjust partitioning, resolving deadlocks and reallocating sub-areas.

    • Main Results:

    • The proposed approach successfully partitions complex coastal regions for heterogeneous UAVs.
    • The algorithms effectively resolve non-allocated regions and balance workload among UAVs.
    • Simulations demonstrate valid and sound solutions under various UAV configurations.

    Conclusions:

    • The developed method provides an effective solution for partitioning coastal areas for multi-UAV surveillance.
    • The approach enhances operational efficiency by optimizing resource allocation and mitigating deadlocks.
    • This work contributes to the advancement of autonomous systems in complex environmental monitoring.