Attention-Guided Reinforcement Learning for Visual Servoing Control of Multirotor UAVs

To address the challenges of dynamic perception, real-time decision-making, and control stability in UAV visual tracking tasks, this study proposes an Attention-guided Visual Servoing Reinforcement Learning (AVSRL) framework. Unlike conventional Image-Based Visual Servoing (IBVS) methods that rely on analytical Jacobian control, AVSRL employs a virtual camera mechanism to normalize image observations and extract geometry-aware visual features as state inputs to a deep reinforcement learning (DRL) agent. The proposed framework integrates model identification, attention-enhanced actor-critic learning, and multi-source visual-environment encoding to enable robust and adaptive UAV control in dynamic and complex…

• NN
  National Natural Science Foundation of China

  Awards: U2433208, 52502398
• CP
  China Postdoctoral Science Foundation

  Award: 2025M781582
• AS
  Aeronautical Science Foundation of China
• FR
  Fundamental Research Funds for the Central Universities

  Award: CHD300102244102