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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Haddad, Maryam Allawi
M.Sc. Student, Department of Computer Engineering, University of Basrah, Iraq
Computer Science & IT Electrical & Electronics Engineering

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Enhancing GRU-Based DRL with Delta-LiDAR for Robust UAV Navigation in Partially Observable Dynamic Environments Haddad, Maryam Allawi; Khudher, Dhayaa Raissan
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 3: September 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i3.7067

Abstract

Partial observability and sensor limitations are challenging for the navigation of autonomous Unmanned Aerial Vehicles (UAVs). Deep Reinforcement Learning (DRL) algorithms have emerged as potential tools in advancing this field. However, their effectiveness degrades in challenging environments, particularly in the presence of dynamic obstacles. Recent research trends emphasize the need for new DRL variants that guarantee robustness, real-time adaptability, and improved generalization under uncertainty. This paper proposes a lightweight DRL architecture that combines Proximal Policy Optimization (PPO) with a Gated Recurrent Unit (GRU), extended with a temporal LiDAR differencing feature called Delta-LiDAR. The difference between consecutive LiDAR scans is computed to provide the velocity and directional cues without the computational burden of Long Short-Term Memory (LSTM) networks. We evaluate three models, PPO-LSTM, PPO-GRU, and Delta-LiDAR augmented PPO-GRU in a 3D simulated UAV navigation environment characterized by noise, clutter, and dynamic obstacles. We considered several metrics, including success rate, collision frequency, trajectory smoothness, and computational efficiency, to determine the effectiveness of each architecture. The experimental results demonstrate that Delta-LiDAR improves GRU-based temporal reasoning. The deployment complexity is reduced compared with the LSTM-based architecture, which makes it ideal for real-time UAV operation in partially observable environments.
Co-Authors Khudher, Dhayaa Raissan