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Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
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Hybrid Diagnostic Framework for Interpretable Bearing Fault Classification Using CNN and Dual-Stage Feature Selection.

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Related Experiment Video

Updated: Jan 13, 2026

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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Autonomous Vision-Based Object Detection and Tracking System for Quadrotor Unmanned Aerial Vehicles.

Oumaima Gharsa1, Mostefa Mohamed Touba1, Mohamed Boumehraz2

  • 1Laboratory of Identification, Command, Control and Communication (LI3CUB), Department of Electrical Engineering, University of Biskra, BP 145, Biskra 07000, Algeria.

Sensors (Basel, Switzerland)
|October 29, 2025
PubMed
Summary
This summary is machine-generated.

This study presents an autonomous vision-based tracking system for quadrotor drones to follow moving targets without GPS. The system uses visual detection and Kalman filters for reliable, low-cost aerial pursuit.

Keywords:
computer visionmoving object trackingobject detectionstate estimationunmanned aerial vehicles

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Area of Science:

  • Robotics and Control Systems
  • Computer Vision
  • Aerospace Engineering

Background:

  • Accurate tracking of dynamic aerial targets is crucial for unmanned aerial vehicle (UAV) applications.
  • Existing systems often rely on GPS or external sensors, limiting their operational flexibility and increasing costs.

Purpose of the Study:

  • To develop an autonomous, vision-based tracking system for a quadrotor UAV.
  • To enable real-time tracking of maneuvering targets without external localization or GPS.

Main Methods:

  • Implemented a vision-based system combining HSV filter detection and shape detection algorithms.
  • Utilized an enhanced extended Kalman filter (EKF) for precise target state estimation.
  • Integrated a closed-loop Proportional-Integral-Derivative (PID) controller for autonomous target following.

Main Results:

  • The system demonstrated efficient and reliable tracking of dynamic targets in simulations and experiments.
  • Achieved robustness against environmental factors like noise, light reflections, and illumination interference.
  • Ensured stable and rapid tracking performance using low-cost hardware components.

Conclusions:

  • The proposed autonomous vision-based tracking system is effective for quadrotor UAVs.
  • The system offers a robust, low-cost solution for real-time aerial target tracking.
  • Validated the potential for autonomous pursuit of maneuvering targets in complex environments.