Drone Navigation Using DRL

International Journal of Emerging Research in Science, Engineering, and Management

Vol. 2, Issue 1, pp. 280–287, January 2026

https://doi.org/10.58482/ijersem.v2i1.38

This work is licensed under a Creative Commons Attribution 4.0 International License .

Drone Navigation Using DRL

N Gowthami, R Likitha, K Bharath, P Kumari Padmaja, S Mahesh Reddy

Department of CSE, Siddharth Institute of Engineering & Technology, Puttur, India.

Abstract

This research explores the implementation of Deep Reinforcement Learning (DRL) to facilitate autonomous drone navigation within complex and unpredictable environments. Traditional navigation systems often rely on rigid, pre-programmed trajectories that struggle with real-time obstacles or environmental shifts. To overcome these limitations, the proposed framework utilizes a trial-and-error learning mechanism, allowing the unmanned aerial vehicle (UAV) to autonomously discover optimal flight paths and obstacle-avoidance strategies through continuous interaction with its surroundings. .By integrating high-frequency environmental sensing with adaptive learning algorithms, the system enhances its navigational precision and safety across diverse settings, including urban landscapes, rural terrains, and confined indoor spaces. A core component of the framework is the integration of proactive collision prediction and avoidance strategies, which significantly bolster operational reliability. The architecture is designed with scalability in mind, providing a foundation for multi-drone coordination and collaborative mission execution in high-density scenarios. This DRL-driven approach represents a shift toward truly intelligent, self-evolving aerial robotics capable of maintaining high mission success rates in dynamic, “in-the-wild” conditions.

Keywords: Deep Reinforcement Learning, Autonomous Navigation, Drone Technology, Obstacle Avoidance, Dynamic Environments.

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DOI: https://doi.org/10.58482/ijersem.v2i1.38

Open Access • Peer Reviewed Article

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This article is part of Volume 2, Issue 1 (January 2026)

2026-01-31