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Shahbazi Khojasteh, Mahdi, Salimi Badr, Armin. (1404). Vision-based Autonomous UAV Navigation Through GPS-Denied Narrow Passages using Deep Reinforcement Learning. فناوری آموزش, (), -. doi: 10.22061/jecei.2025.11563.815
Mahdi Shahbazi Khojasteh; Armin Salimi Badr. "Vision-based Autonomous UAV Navigation Through GPS-Denied Narrow Passages using Deep Reinforcement Learning". فناوری آموزش, , , 1404, -. doi: 10.22061/jecei.2025.11563.815
Shahbazi Khojasteh, Mahdi, Salimi Badr, Armin. (1404). 'Vision-based Autonomous UAV Navigation Through GPS-Denied Narrow Passages using Deep Reinforcement Learning', فناوری آموزش, (), pp. -. doi: 10.22061/jecei.2025.11563.815
Shahbazi Khojasteh, Mahdi, Salimi Badr, Armin. Vision-based Autonomous UAV Navigation Through GPS-Denied Narrow Passages using Deep Reinforcement Learning. فناوری آموزش, 1404; (): -. doi: 10.22061/jecei.2025.11563.815

Vision-based Autonomous UAV Navigation Through GPS-Denied Narrow Passages using Deep Reinforcement Learning

Journal of Electrical and Computer Engineering Innovations (JECEI)
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 20 خرداد 1404
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2025.11563.815
نویسندگان
Mahdi Shahbazi Khojasteh؛ Armin Salimi Badr*
Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran.
تاریخ دریافت: 29 بهمن 1402،  تاریخ بازنگری: 26 اردیبهشت 1404،  تاریخ پذیرش: 16 خرداد 1404 
چکیده
Background and Objectives: Unmanned Aerial Vehicles (UAVs) face significant challenges in navigating narrow passages within GPS-denied environments due to sensor and computational limitations. While deep reinforcement learning (DRL) has improved navigation, many methods rely on costly sensors like depth cameras or LiDAR. This study addresses these issues using a vision-based DRL framework with a monocular camera for autonomous UAV navigation.
Methods: We propose a DRL-based navigation system utilizing Proximal Policy Optimization (PPO). The system processes a stack of grayscale monocular images to capture short-term temporal dependencies, approximating the partially observable environment. A custom reward function encourages trajectory optimization by assigning higher rewards for staying near the passage center while penalizing further distances. The navigation system is evaluated in a 3D simulation environment under a GPS-denied scenario.
Results: The proposed method achieves a high success rate, surpassing 97% in challenging narrow passages. The system demonstrates superior learning efficiency and robust generalization to new configurations compared to baseline methods. Notably, using stacked frames mitigates computational overhead while maintaining policy effectiveness.
Conclusion: Our vision-based DRL approach enables autonomous UAV navigation in GPS-denied environments with reduced sensor requirements, offering a cost-effective and efficient solution. The findings highlight the potential of monocular cameras paired with DRL for real-world UAV applications such as search and rescue and infrastructure inspection. Future work will extend the framework to obstacle avoidance and general trajectory planning in dynamic environments.
کلیدواژه‌ها
Deep Reinforcement Learning؛ Obstacle Avoidance؛ Trajectory Planning؛ Autonomous Navigation؛ Unmanned Aerial Vehicle
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