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In GNSS-denied environments such as indoor facilities, urban canyons, or forested terrains, Unmanned Aerial Vehicles (UAVs) must autonomously navigate and track human targets without relying on global positioning. This paper presents a decentralized reinforcement learning (DRL) framework for real-time UAV person tracking and obstacle avoidance using multi-modal sensing. The proposed system integrates RGB, thermal, and depth data through a YOLOv8-nano perception module, enabling robust object detection under low visibility and occlusion. A rule-based geometric model estimates the target’s relative position, which, along with sensor fusion inputs, forms the state space for a Proximal Policy Optimization (PPO) control policy. The agent operates over a discrete action set, allowing stable and sample-efficient learning while maintaining reactive and goal-directed behavior. The framework is validated in simulated scenarios demonstrating superior performance in navigation accuracy, tracking robustness, and energy efficiency. Our approach highlights the potential of decentralized DRL for scalable and resilient UAV autonomy in challenging operational conditions.

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Learning to Navigate and Track: A Decentralized RL Framework for UAV Operations in GNSS-Denied Terrain

  • 会议论文

  • 发表日期：

    2026年

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  • Nikolaos D. Almalis,
  • George A. Tsihrintzis,
  • Michail Mitsios,
  • Tsiapras Tilemachos

摘要

In GNSS-denied environments such as indoor facilities, urban canyons, or forested terrains, Unmanned Aerial Vehicles (UAVs) must autonomously navigate and track human targets without relying on global positioning. This paper presents a decentralized reinforcement learning (DRL) framework for real-time UAV person tracking and obstacle avoidance using multi-modal sensing. The proposed system integrates RGB, thermal, and depth data through a YOLOv8-nano perception module, enabling robust object detection under low visibility and occlusion. A rule-based geometric model estimates the target’s relative position, which, along with sensor fusion inputs, forms the state space for a Proximal Policy Optimization (PPO) control policy. The agent operates over a discrete action set, allowing stable and sample-efficient learning while maintaining reactive and goal-directed behavior. The framework is validated in simulated scenarios demonstrating superior performance in navigation accuracy, tracking robustness, and energy efficiency. Our approach highlights the potential of decentralized DRL for scalable and resilient UAV autonomy in challenging operational conditions.