Adaptive Robotics For Military Defense: Material Optimization And Camouflage Intelligence

Abstract

Advancements in robotics and AI have empowered defense strategies with innovative technologies that merge adaptive camouflage, autonomous threat detection, and structural material optimization. This chapter presents a comprehensive study combining material science and computer vision-based autonomy for military robotic systems. Two primary developments are explored: a drone and threat detection system using deep learning, and a structurally optimized, camouflage-enabled robot capable of stealth and counterattack operations. Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), and deep learning models are integrated to enhance structural durability and real- time responsiveness. Results demonstrate improvements in drag reduction, load-bearing capacity, and autonomous decision-making. These findings provide a foundation for next-gen military robotics.