Category: Real-Time Scheduling

This post was co-authored by Sagar Chaki

In 2011, the U.S. Government maintained a fleet of approximately 8,000 unmanned aerial systems (UAS), commonly referred to as "drones," a number that continues to grow. "No weapon system has had a more profound impact on the United States' ability to provide persistence on the battlefield than the UAVs," according to a report from the 2012 Defense Science Board. Making sure government and privately owned drones share international air space safely and effectively is a top priority for government officials. Distributed Adaptive Real-Time (DART) systems are key to many areas of Department of Defense (DoD) capability, including the safe execution of autonomous, multi-UAS missions having civilian benefits. DART systems promise to revolutionize several such areas of mutual civilian-DoD interest, such as robotics, transportation, energy, and health care. To fully realize the potential of DART systems, however, the software controlling them must be engineered for high-assurance and certified to operate safely and effectively. In short, these systems must satisfy guaranteed and highly-critical safety requirements (e.g., collision avoidance) while adapting smartly to achieve application requirements, such as protection coverage, while operating in dynamic and uncertain environments. This blog post describes our architecture and approach to engineering high-assurance software for DART systems.

Many DoD computing systems--particularly cyber-physical systems--are subject to stringent size, weight, and power requirements. The quantity of sensor readings and functionalities is also increasing, and their associated processing must fulfill real-time requirements. This situation motivates the need for computers with greater processing capacity. For example, to fulfill the requirements of nano-sized unmanned aerial vehicles (UAVs), developers must choose a computer platform that offers significant processing capacity and use its processing resources to meet its needs for autonomous surveillance missions. This blog post discusses these issues and highlights our research that addresses them.