Artificial Intelligence and Machine Learning for Unmanned Vehicles

Military & Aerospace Electronics Magazine Article Link

Key snippets from the article:

Small Form Factor

Aneesh Kothari, vice president of marketing at Systel Inc. in Sugar Land, Texas, says that unmanned systems can take advantage of on-board AI and machine learning to reduce liabilities brought on by the realities of operating in a contested environment.

“High-performance embedded edge computing is critical to deployed AI mission success. Operating in a contested environment with restricted bandwidth and degraded communications make the tactical use of cloud-based computing and AI a liability. Computational processing capability must reside on-premise to ensure the low latency and near real-time speed demanded of AI-based applications. Advances in COTS (commercial off-the-shelf) technologies in recent years have allowed for practical embedded edge computing use in unmanned vehicles,” Kothari says. “Systel’s rugged embedded systems such as Kite-Strike and Raven-Strike integrate commercial hardware components such as video capture cards and encoders, and the latest NVIDIA Ampere-based and Jetson Xavier GPUs in small-form factor (SFF) rugged embedded computers, making them ideal for use in unmanned vehicles.”

Trustworthy Autonomy

“As AI technologies become increasingly sophisticated and prevalent it can be tempting to think of AI as a ‘silver bullet,’ says Systel’s Kothari. “While AI can offload a majority of the burden of the operator, achieving sensor fusion and automating analysis tasks that are essential when dealing with enormous amounts of raw data, and even as we move from ‘kill chains’ to more complex ‘kill webs,’ humans still very much need to remain in the loop.”