Artificial Intelligence Based Path Planning for Autonomously Piloted UAVs in Remote Sensing

Abstract

Autonomously piloted Unmanned Aerial Vehicles (UAVs) are revolutionizing remote sensing by enhancing mission efficiency, data quality, operational safety and location precision. This paper explores the role of artificial intelligence (AI) in optimizing UAV path planning for dynamic and complex sensing environments. AI algorithms, particularly reinforcement learning and deep learning models, allow UAVs to adaptively respond to environmental uncertainties, detect and avoid obstacles, and navigate efficiently across vast terrains without human intervention. The integration of convolutional neural networks(CNNs) improves visual perception and semantic interpretation, while policy-driven learning frameworks enhance autonomous decisionmaking. This study examines several path planning strategies, including movement primitives, sensor fusion, and semantic mapping techniques. In addition, practical challenges, such as energy constraints, on-board computational limitations, and sim-to-real transfer issues are discussed alongside solutions like hardware-in-the-loop simulation and edge-based inference systems. The work aims to establish AI path planning as a foundational element for scalable and intelligent remote sensing missions.