Dynamic Routing of Unmanned Aerial Vehicles: Current State and Development Prospects
The paper presents an integrated framework for dynamic routing of unmanned aerial vehicles (UAVs) operating in, uncertain, and rapidly changing environments. After reviewing classical deterministic, sampling complex -based, bio-inspired, machine learning, and hybrid path-planning methods, their limitations with respect to real-time replanning, energy efficiency, and scalability to multi-UAV missions are critically analysed.