Learning-Based Multi-UAV Flocking Control With Limited Visual Field and Instinctive Repulsion

Chengchao Bai, Peng Yan, Haiyin Piao, Wei Pan, Jifeng Guo

Research output: Contribution to journalArticlepeer-review

Abstract

This article explores deep reinforcement learning (DRL) for the flocking control of unmanned aerial vehicle (UAV) swarms. The flocking control policy is trained using a centralized-learning-decentralized-execution (CTDE) paradigm, where a centralized critic network augmented with additional information about the entire UAV swarm is utilized to improve learning efficiency. Instead of learning inter-UAV collision avoidance capabilities, a repulsion function is encoded as an inner-UAV “instinct.” In addition, the UAVs can obtain the states of other UAVs through onboard sensors in communication-denied environments, and the impact of varying visual fields on flocking control is analyzed. Through extensive simulations, it is shown that the proposed policy with the repulsion function and limited visual field has a success rate of $93.8\%$ in training environments, $85.6\%$ in environments with a high number of UAVs, $91.2\%$ in environments with a high number of obstacles, and $82.2\%$ in environments with dynamic obstacles. Furthermore, the results indicate that the proposed learning-based methods are more suitable than traditional methods in cluttered environments.
Original languageEnglish
Pages (from-to)462-475
Number of pages14
JournalIEEE Transactions on Cybernetics
Volume54
Issue number1
Early online date8 Mar 2023
DOIs
Publication statusPublished - 1 Jan 2024

Keywords

  • Autonomous aerial vehicles
  • Collision avoidance
  • Deep reinforcement learning (DRL)
  • flocking control
  • inter-unmanned aerial vehicle (UAV) collision avoidance
  • limited visual field
  • Optimization
  • Reinforcement learning
  • Sensors
  • Training
  • UAVs
  • Visualization

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