Toward the Development of a Low-Altitude Air Traffic Control Paradigm for Networks of Small, Autonomous Unmanned Aerial Vehicles

Abstract

To enable safe and efficient operation of a large number of Unmanned Aerial Vehicles (UAVs) as envisioned in future commercial operations, a novel networked-based air traffic control paradigm is needed to provide for autonomous route optimization and cooperation to enable deconfliction. This system would need to ensure deconfliction from nearby manned and unmanned operations and would need the capability to respond to unexpected close-proximity aircraft, known as interlopers. This network-based control paradigm will require that operators be able to oversee a network of cooperative and autonomous UAVs capable of maneuvering at low altitudes. Such a network would need new algorithms and increased autonomy to best support cooperative deconfliction and high level supervisory control, and one that also allows operators the ability to both understand local and global behavior of a network of UAVs. To this end, we propose a paradigm-shifting multi-layer distributed air traffic control concept focused on low altitude, high density UAV operations. This new architecture would include a group of analysts that perform a global analysis of the UAV network at a strategic level and more tactical, local controllers manipulating the UAV network, who are advised by the analysts. The proposed operational support system would include methods for the automatic identification of issues within the network, as well as a method for the automatic distribution of effective tasks to the most suitable personnel. This architecture would be a combination of current air traffic control techniques as well as those troubleshooting techniques found in other domains like network and cloud computing management. Nomenclature E= error matrix M n = n th metric T = task P n = n th Air Traffic Operative (ATO)

Companion Unmanned Aerial Vehicles: A Survey

“…Fourth, we also encourage HDI researchers to share development code among companion UAV studies to facilitate comparison study under a shared metrics. In addition to our recommendation, one could also draw inspiration from the practices and know-how in the aerospace field [44] and social robotics field [20,37].…”

A multiagent framework for learning dynamic traffic management strategies

“…A centralized controller tells every robot in the system when it can travel, and where it can travel. Aubert et al (2015) proposed a similar framework specifically for UAVs to communicate potential flight plans to a centralized air traffic management system that determines whether to accept or reject the plan. In order to compute a solution, these methods require full state information and are often slow and computationally complex.…”