Artificial Intelligence (AI) has been gaining traction for military, aerospace, and defense purposes in recent deep learning fundamentals, computers can recognize sophisticated patterns and characteristics of tasks, simulating human cognitive intelligence. Incorporating this with network edge computing enables the possibility of implementing the necessary computational tools and capabilities where they’re needed the most: at the farthest reaches of a network on the battlefield’s edge.
Figure 1: The modern battlefield will soon be full of AI-enabled systems
What is Edge Computing?
The concept of edge computing describes how data can be processed by distributed local computational resources that are physically close to the sensors collecting the data. In contrast to cloud computing, edge computers remove the dependency on the processing power away from data centers and the need for connectivity to the Internet. This enables immediate computational power to devices and platforms that require it, such as drones, manned vehicles, or smart weapon systems.
With increased computational capabilities at the edge of the network, artificial intelligence, traditionally consigned to data centers, can be enabled for warfighters and mission-critical equipment and systems. What’s more, leveraging operations through the network edge offers advantages when compared to cloud computing. With the right tools in place, edge computing provides increased bandwidth and security for data transfers. It also reduces latency as there is no need for data transitioning back and forth from remote servers.
Applications of AI at the Edge
When it comes to military uses of artificial intelligence on the battlefield, applications such as unmanned vehicles, monitoring systems, and manned vehicle systems can benefit greatly from AI.
Smart Unmanned Vehicles
Unmanned vehicles are increasingly becoming an indispensable tool on the battlefield, and with the emergence of AI, the trend is bound to continue. Independent of their class – unmanned aerial vehicles (UAV), unmanned ground vehicles (UGV), unmanned underwater vehicles (UUV), or unmanned surface vehicles (USV) – AI can enable these platforms to do more with less human involvement.
Tasks such as mine detection, border and coastal patrol and protection, rapid environment assessments (REAs), and intelligence, surveillance, and reconnaissance (ISR) activities are already efficiently done by UAVs, UUVs, and monitoring technology along borders, with little to no human intervention. Today, autonomous weapon systems on unmanned vehicles are already capable of detecting threats and activating defense protocols. However, as AI matures, this technology could evolve so UAVs or UGVs, for example, can discover and mitigate threats more independently in an urban or warfare scenario.
Additionally, “swarm intelligence” is an application likely to be seen in the future powered by AI capabilities. A group of drones, usually UAVs, self-organize into a coherent swarm, flying in synchrony without colliding. This application can be used, for example, to survey an area of the battlefield and identify any enemy threats within the perimeter. Rather than flying a pre-programmed route, each drone tracks its own position and velocity, sharing that information with the rest of the swarm. This way, they can self-navigate around obstacles, avoid enemy fire, and explore areas where they might notice a large contingent of enemy warfighters.
Aircraft System Applications
Artificial intelligence’s contribution to manned vehicle systems can range from radar-like functions to monitoring and maintenance systems. AI can be leveraged by multiple sensors on the aircraft to identify and mitigate potential threats, providing the pilot and operators with much larger data input.
Furthermore, data on available aircraft and their current condition can be gathered and analyzed to gauge the vehicle’s overall health in monitoring systems. This data leads to higher efficiency in ensuring proper maintenance of different parts ensuring higher useful life and decreasing potential repair time. The advancement of AI in this aspect benefits mainly health and usage monitoring systems (HUMS) for aircraft, for example.
Artificial Intelligence and Edge Computing on the Battlefield
The modern battlefield will soon be full of AI-enabled systems. The computing power required for such systems is substantial, and this is what edge computing brings. It takes the necessary processing power away from data centers to the network’s edge.
In our white paper, Enabling AI at the Network Edge of the Battlefield, we explore the intrinsic values that operating at the network edge provides, how this permits AI to be leveraged by warfighters, and the available solutions that enable these possibilities.