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Counter a Drone in a Complex Neighborhood Area by Deep Reinforcement Learning.

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Countering a Drone in a 3D Space: Analyzing Deep Reinforcement Learning Methods.

Ender Çetin1, Cristina Barrado1, Enric Pastor1

  • 1Computer Architecture Department, UPC BarcelonaTECH, Esteve Terrades 7, 08860 Castelldefels, Spain.

Sensors (Basel, Switzerland)
|November 26, 2022
PubMed
Summary

Artificial intelligence (AI) using deep reinforcement learning (DRL) effectively counters drones in 3D space. Transfer learning and deep Q-learning from demonstrations (DQfD) significantly accelerate drone interception training.

Keywords:
DQNDQfDUAVYolocounter dronesdeep reinforcement learningdueling DDQNobject detectionprioritized experience replaytransfer learning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Aerospace Engineering

Background:

  • The commercial drone market is rapidly expanding, increasing the risk of airspace conflicts and security threats.
  • Existing counter-drone methods often lack the efficiency and adaptability required for complex aerial environments.
  • Artificial intelligence offers a promising solution for developing autonomous counter-drone systems.

Purpose of the Study:

  • To propose and evaluate a deep reinforcement learning (DRL) method for an unmanned aerial vehicle (UAV) to intercept another drone in a 3D environment.
  • To address the challenges of 3D drone interception, including real-time obstacle avoidance and training efficiency.
  • To investigate the effectiveness of transfer learning and a hybrid imitation-reinforcement learning approach for enhancing counter-drone capabilities.

Main Methods:

  • A Deep Q-Network (DQN) algorithm with dueling network architecture and prioritized experience replay was implemented in the Airsim simulator.
  • Transfer learning was utilized by replaying and filtering past training experiences to accelerate new model training.
  • A deep Q-learning from demonstrations (DQfD) algorithm combining expert and self-generated data was developed to further enhance learning speed.

Main Results:

  • Transfer learning significantly improved model performance and dramatically increased the learning progress of the interceptor drone.
  • The DQfD algorithm demonstrated accelerated learning, particularly effective even with limited expert demonstration data.
  • The proposed DRL methods proved capable of effectively countering drones in complex 3D scenarios.

Conclusions:

  • Deep reinforcement learning, enhanced by transfer learning and DQfD, provides an efficient and robust solution for 3D drone interception.
  • AI-powered counter-drone systems can be trained more effectively and rapidly using advanced machine learning techniques.
  • These advancements are crucial for the safe integration of unmanned aerial vehicles into shared airspace.