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UAVs Maneuver Decision-Making Method Based on Transfer Reinforcement Learning.

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This study introduces a novel UAV obstacle avoidance method using deep deterministic policy gradient and lidar detection for complex environments. Transfer learning significantly accelerates training and enhances performance in 1vs1 confrontations.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Aerospace Engineering

Background:

  • Traditional Unmanned Aerial Vehicle (UAV) navigation often relies on complete global obstacle knowledge, limiting effectiveness in dynamic, complex environments.
  • Addressing 1vs1 confrontation scenarios with randomly distributed, unknown obstacles presents a significant challenge for autonomous UAVs.

Purpose of the Study:

  • To develop an intelligent maneuver decision-making method for UAVs in complex, obstacle-rich environments.
  • To enhance UAV obstacle avoidance capabilities using airborne lidar detection and deep deterministic policy gradient (DDPG).
  • To improve training efficiency and performance through the application of transfer learning.

Main Methods:

  • Designed a UAV airborne lidar detection model for identifying unknown obstacles in real-time.
  • Implemented the deep deterministic policy gradient (DDPG) algorithm for maneuver decision-making.
  • Utilized transfer learning to share and adapt trained strategies between UAVs for accelerated learning.

Main Results:

  • The proposed lidar detection model effectively handles numerous unknown obstacles.
  • Transfer learning demonstrated a significant speed-up in the UAV training process.
  • The overall training effect and performance of UAVs in complex tasks were substantially improved.

Conclusions:

  • The integration of DDPG, lidar detection, and transfer learning offers a robust solution for UAV autonomous navigation in challenging environments.
  • Transfer learning enhances the progressive intelligence and adaptability of UAVs in cooperative and competitive scenarios.
  • This approach advances the capabilities of UAVs for complex 1vs1 confrontations and similar autonomous tasks.