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Development of Multiple UAV Collaborative Driving Systems for Improving Field Phenotyping.

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  • 1Department of Biosystems Engineering, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon 24341, Korea.

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Summary
This summary is machine-generated.

This study introduces a collaborative multi-drone system for precise crop phenotyping. The system uses a leader-follower algorithm and collision avoidance to improve data accuracy in smart agriculture.

Keywords:
collaborative drivingfield phenotypingmultiple UAVsremote sensingsynchronized motion

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Area of Science:

  • Agricultural Engineering
  • Robotics
  • Remote Sensing

Background:

  • Unmanned aerial vehicles (UAVs) with RGB and multispectral cameras are crucial for crop monitoring in smart agriculture.
  • Simultaneous multi-perspective image acquisition is vital for accurate field phenotyping, but drone and plant movement can cause errors.
  • Existing methods struggle with measurement errors due to dynamic environmental factors and platform movement.

Purpose of the Study:

  • To develop a collaborative multi-UAV system for simultaneous image acquisition from diverse viewpoints, minimizing phenotyping errors.
  • To enhance digital surface model accuracy through coordinated UAV flight patterns.
  • To improve the precision and reliability of UAV-based field phenotyping.

Main Methods:

  • A collaborative driving system with an integrated MAVSDK-based navigation system for attitude and position control.
  • Implementation of a leader-follower swarm driving algorithm coupled with a long-range wireless network for synchronized flight.
  • Development of a collision avoidance algorithm to ensure safe group operation under external disturbances.
  • Validation and optimization of the flight algorithm within a GAZEBO-based simulation environment prior to field deployment.

Main Results:

  • The collaborative system successfully enabled multiple UAVs to maintain synchronized flight, speed, direction, and image overlap.
  • The developed collision avoidance algorithm ensured safe operation during group flights.
  • Simulation results showed a Root Mean Square Error (RMSE) of 0.36 m for flight accuracy.
  • Real-world field tests achieved a flight accuracy of 0.46 m (RMSE), comparable to commercial systems.

Conclusions:

  • The proposed collaborative multi-UAV system effectively minimizes measurement errors in field phenotyping.
  • The leader-follower algorithm and collision avoidance enhance the safety and accuracy of drone swarms in agriculture.
  • This technology offers a robust solution for precision agriculture, improving data quality for smart farming applications.