Design and Experimental Validation of an Autonomous Ground Robot with Adaptive Camouflage and AI-Based Drone Detection for Border Surveillance

Abstract

Border surveillance in complex and dynamic environments demands intelligent systems capable of autonomous operation, real-time threat detection, and adaptive concealment. Conventional monitoring methods often suffer from limited coverage, delayed response, and high human dependency. To address these challenges, this study presents the design and experimental validation of an autonomous ground robot integrated with adaptive camouflage and artificial intelligence-based drone detection for border surveillance applications. The proposed system combines a mechanically optimized ground vehicle, an adaptive camouflage mechanism for environmental blending, and a deep learning-based vision module for detecting aerial drones and human intrusions. Internet of Things (IoT) architecture enables real-time monitoring, remote control, and data transmission under field conditions. Mechanical integrity and aerodynamic stability of the platform are validated through structural, modal, and computational fluid dynamics analyses. Experimental results demonstrate high detection accuracy for both aerial and ground targets, with low communication latency and reliable system responsiveness. The adaptive camouflage mechanism improves concealment effectiveness across varying terrain conditions. The integrated framework shows robust performance during real-time field trials, confirming its suitability for autonomous border surveillance tasks. The proposed system offers a scalable and cost-effective solution for intelligent surveillance applications, with potential extensions toward multi-robot coordination and advanced threat response strategies.