Author(s) :

Vir Sanghvi.

Volume/Issue :

Abstract :

While substantial research has explored individual components of autonomous weapons systems (AWS), this study provides a comprehensive analysis comparing their capabilities, implementation methods, and effectiveness factors across different systems and operational contexts. This systematic literature review examines 49 studies from 2,847 initial records to investigate technological capabilities, operational challenges, and mitigation strategies that define contemporary AWS programs. The analysis shows that AWS capabilities have evolved from early automated systems to advanced platforms incorporating artificial intelligence, cutting-edge materials, and network-centric integration. Three categories of challenges primarily constrain the development of AWS: computational optimization difficulties in weapon-target assignment systems, complexities in mechanical systems integration, and limitations in materials science. The key findings suggest the effectiveness of virtual reality training systems, which achieve over 80% accuracy in military applications; advanced multi-objective evolutionary algorithms (MOEA/D-iAM2M) demonstrating superior convergence and diversity in weapon-target assignment optimization; and aluminium-carbon nanotube composite materials with enhanced ballistic protection capabilities. The findings also offer important insights for military planners and defense contractors, highlighting the importance of focusing on systems that show dependable integration and investing in communication capabilities to support network-centric warfare. Moreover, the rapid advancements in artificial intelligence (AI), materials science, and manufacturing technologies suggest an ongoing expansion of AWS capabilities, necessitating continuous research into reliability, effectiveness, and responsible deployment.