10-15 Year Prediction: U.S. Military & Strategic UAS

Introduction:

This post presents the rationale to support a forecast that the U.S. military will demonstrate the successful operational use of Unmanned Aircraft Systems (UAS) for inter-theater strategic missions by the 2018-2023 timeframe. The sustained growth in the electronic support and tactical/theater combat mission areas (OSD, 2005) should continue and eventually lead to concept development, testing, and limited deployment in the high-value, limited-resource roles currently reserved for larger airframes and multi-person crews (e.g. bombers, airlift, command & control, aerial refueling).

UAS Concept:

The current UAS concept is an evolutionary product representing the technical, operational, and doctrinal characteristics of its Unmanned Aerial Vehicle (UAV) ancestry. Whereas the UAV was traditionally a more tactically-focused system, the UAS reflects consideration of the more complex architectures and increasing operational capabilities of the newer systems (Wikipedia, 2008). This diagram depicts the basic operational concept of a UAS and provides some insight into the technological sophistication and operational complexity underpinning this capability:



Major Factors:

There are a multitude of social, political, and technical factors that have shaped the research, development, and implementation of the UAS to date. Over the next 10-15 years, this complex system of influence will undoubtedly continue to guide and constrain the evolution of UAS capabilities. The author’s basic model reflects an approach based on the Analysis phase of the TechCase method (TechCast, 2007) and accounts for the most influential variables that will affect the outcome of this prediction:



Technology Risk:

Given the fact that the UAS is such a complex, technology-dependent system, arguably the most critical factor in the model is Research & Development (R&D). Within the overall R&D effort, Autonomy technology offers the most reward but also presents the greatest risk due to relative immaturity. This diagram shows the most significant technologies for enabling Autonomy (Wikipedia, 2008) and reducing UAS dependency on human interaction:



For the UAS to widely replace the traditional human-piloted airframe, it must be capable of acting as a rational agent. In this, the UAS would be have the means to deal with some uncertainty and act to achieve the best expected outcome. The overall field of Artificial Intelligence (AI) has had multiple cycles of progress and setbacks beginning in the 1950s. However, the last decade has provided renewed upward momentum with some successful applications of AI that reflect the trend needed to support this prediction (Russell & Norvig, 2003):

• IBM’s Deep Blue beat the world champion chess master Gary Kasparov.

• NASA’s Remote Agent Program implemented on-board autonomous planning and scheduling for spacecraft operations.

• The ALVINN system steered the NAVLAB computer-controlled minivan across the United States while maintaining control 98% of the time.

Supporting Trends:

Undoubtedly, the U.S. military has demonstrated an increasing commitment to the continuous improvement and employment of UAS over the last couple of decades for both tactical and theater-level purposes. The U.S. Department of Defense (DoD) began its journey with the UAV in the mid-1960s, but the concept was ahead of the enabling technologies. As the growth of many technologies exploded, U.S. military and political leadership have demonstrated a long-term, sustained commitment to UAS capability growth:



Some recent statistics on DoD funding and operational use of UAS provide additional support for this claim (OMB, 2005):





Conclusion:

Looking into the future is a tricky endeavor. While some trends hold over time, others will not due to randomness, complexity, and unanticipated factors. Therefore, accurate technology predictions can’t be accomplished by merely extrapolating a single trend (Sherden, 1998). Hopefully, this work has provided meaningful support for the author’s prediction by taking a basic approach that considers multiple, influencing factors and their associated trends. In this particular case, just merely extrapolating the growth trend in UAS capability could warrant a more optimistic assessment. However, weighing in both the criticality and relativity immaturity of Autonomy technology necessitates a more conservative assessment of the likely outcome. In summary, recent significant achievements in AI implementations have demonstrated advances in key support technologies vital for UAS Autonomy and help to mitigate the technology risk. Mitigation of this key risk factor should enable our military to employ UAS in strategic air missions - those currently reserved for only manned platforms.

References:

Federation of American Scientists (FAS), 2008. Unmanned Aerial Vehicles (UAVs). Retrieved from http://www.fas.org/irp/program/collect/uav.htm

Office of Management & Budget (OMB), 2005. Detailed Information on the DoD Unmanned Aircraft Systems (UAS) Assessment. Retrieved from http://www.whitehouse.gov/omb/expectmore/detail/10003201.2005.html

Office of the Secretary of Defense (OSD), 2005. Unmanned Aircraft Systems Roadmap. Retrieved from http://www.fas.org/irp/program/collect/uav_roadmap2005.pdf
Russell, S. J., & Norvig, P. (2003). Artificial Intelligence: A Modern Approach. New Jersey: Pearson Education, Inc.

Sherden, W. (1998). The Fortune Sellers: The Big Business of Buying and Selling Predictions. New York: John Wiley & Sons, Inc.

TechCast (2007). The TechCast Technology Forecasting Research Method. Retrieved from http://www.techcast.org/Methodology.aspx

Wikipedia (2008). Unmanned Aircraft System. Retrieved from http://en.wikipedia.org/wiki/Unmanned_Aircraft_System