Reinforcement Learning for Autonomous Point-to-Point UAV Navigation

Salim Oyinlola; Nitesh Subedi; Soumik Sarkar

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arXiv · 2509.13943

Reinforcement Learning for Autonomous Point-to-Point UAV Navigation

Salim Oyinlola·Nitesh Subedi·Soumik Sarkar

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Abstract

Unmanned Aerial Vehicles (UAVs) are increasingly used in automated inspection, delivery, and navigation tasks that require reliable autonomy. This project develops a reinforcement learning (RL) approach to enable a single UAV to autonomously navigate between predefined points without manual intervention. The drone learns navigation policies through trial-and-error interaction, using a custom reward function that encourages goal-reaching efficiency while penalizing collisions and unsafe behavior. The control system integrates ROS with a Gym-compatible training environment, enabling flexible deployment and testing. After training, the learned policy is deployed on a real UAV platform and evaluated under practical conditions. Results show that the UAV can successfully perform autonomous navigation with minimal human oversight, demonstrating the viability of RL-based control for point-to-point drone operations in real-world scenarios.

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BibTeX RIS

Salim Oyinlola, Nitesh Subedi, Soumik Sarkar. 2025-09-17. Reinforcement Learning for Autonomous Point-to-Point UAV Navigation. https://arxiv.org/abs/2509.13943

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