International Journal of Robotics and Autonomous Systems ISSN: 3107-7382 (Online)

Author: Preeti Reddy

Abstract: Autonomous drones are increasingly being utilized in various applications, from delivery services to surveillance and environmental monitoring. A critical challenge in the deployment of autonomous drones is the development of efficient path planning algorithms that can navigate dynamic and unpredictable environments. This paper explores the use of machine learning techniques, specifically reinforcement learning and neural networks, to develop advanced path planning strategies for autonomous drones. Our proposed system dynamically adjusts to changes in the environment, learning optimal paths through continuous interaction and feedback. Experimental results, obtained from both simulated environments and real-world tests, indicate that our machine learning-based approach significantly outperforms traditional path planning methods in terms of efficiency, adaptability, and robustness. The findings suggest that machine learning has a transformative potential in enhancing the capabilities of autonomous drones.

Keywords: Path Planning, Autonomous Drones,Reinforcement Learning, Neural Networks, Dynamic Environments