Advancements & Challenges in Unmanned Ground Vehicles

Abstract

This paper reviews the advancements and challenges in Unmanned Ground Vehicles (UGVs), tracing their evolution from early research to modern applications. We synthesize recent progress in mobility platforms, including wheeled, tracked, and legged systems, and their use in military, agricultural, and disaster response missions. The review highlights key technological enablers for UGV autonomy, such as advanced perception systems that fuse data from LiDAR, cameras, and radar. We also examine the role of artificial intelligence, particularly deep learning for perception and reinforcement learning for navigation. Furthermore, the paper addresses the increasing importance of modularity, interoperability standards like JAUS, and the use of UGV swarms. Despite this progress, significant challenges persist, including reliable off-road autonomy, localization in GPS-denied environments, and ensuring cybersecurity. The paper concludes by outlining critical areas for future research to achieve more resilient, intelligent, and collaborative UGV systems.