Swarm Intelligence-Based Trajectory Planning and Collision Avoidance for Flying Cars in Aerial Transportation
M. B., P Santhiya, Stewart Kirubakaran S, Getzi Jeba Leelipuushpam · 2025
Flying cars represent a potential alternative to city air transport, aiming to reduce traffic and provide more mobility. However, real-time trajectory planning and collision avoidance are problematic due to their larger size, higher speed, and more advanced maneuvering requirements. The conventional application of swarm intelligence models that were originally intended for drones overlooks these unique characteristics, leading to poor path planning and increased collision risks. The present work explores drone swarm algorithms for aerial vehicles to identify significant constraints and adaptation problems. A comparison study finds differences in maneuverability, route efficiency, and survivability. To address these limitations, a swarm intelligence system with adaptive features, multi-agent coordination, and real-time rerouting is suggested. Performance is evaluated considering response efficiency, collision avoidance, and flight stability across varying conditions. The aim is to develop an optimum trajectory planning model that upholds safe and congestion-free aerial travel, maintaining strong and consistent flight operations.