The rapid advancement of autonomous systems has led to their increasing deployment in critical domains such as intelligent transportation, industrial automation, and smart cities. These systems rely on the cooperation of multiple autonomous agents to achieve complex tasks more efficiently and adaptively than traditional automated systems. Such systems are often referred to as Cooperative Autonomous Systems (CASs). The integration of CASs with wireless communication networks enables a new level of flexibility, allowing distributed systems to collaborate dynamically without rigid, pre-programmed interactions.
However, the adoption of CASs in real-world applications faces fundamental scientific and technical challenges, particularly in environments with stringent time-sensitive constraints, dynamic uncertainties, and communication limitations. Current design approaches rely on modular methodologies, where control, estimation, and communication are treated as separate layers. This often results in suboptimal, inefficient, and fragile systems that struggle to adapt to real-world conditions. Additionally, the growing complexity of interconnected systems necessitates a paradigm shift—one that moves beyond incremental improvements toward a holistic co-design framework that integrates these critical components seamlessly.
This project aims to bridge the gap between theoretical advances and practical implementations by developing a fundamental yet realistic framework for real-time control, estimation, and localization in CASs. By rethinking the way these systems interact and operate, the project will establish the foundations for future high-performance, adaptive, and robust cooperative autonomous networks.
