An AI-Based UAV Decision Algorithm for CBRNE Threat Identification, Route Planning and Display
Date/Time: Wednesday, November 10 at 3:00 – 4:00 pm ET
Webinar Description:
Kongsberg Geospatial and SFL Scientific show how Artificial Intelligence is being used to develop autonomous unmanned systems for complex tasks.
Rapid identification of chemical and visual threats is crucial in a variety of civilian and federal missions. Next-generation devices will be integrated with the capability to autonomously identify, locate, and help prioritize decisions in the detection of threats, anomalous activity, and other key indicators to support the safety and effectiveness of individuals.
SFL Scientific has partnered with Kongsberg Geospatial to demonstrate the capability for detecting chemical threats, identifying and displaying invisible hazardous plumes, onboard a drone in real-time. This implementation fuses visual, thermal, multichannel chemical sensor (FLIR MUVE C360), environmental, and location data using a dedicated AI processor (NVIDIA Jetson), on a commercial drone. Critically, it leverages custom bleeding-edge Generative Adversarial Networks (GANs) and Graph Deep Learning models to identify novel threats through multi-feature graph representations at the molecular level.
Kongsberg's IRIS UxS Ground Control Station is utilized to provide mission planning, fleet control, and unparalleled situational awareness to safely operate the Unmanned Systems Beyond Visual Line-of-Sight (BVLOS).
Speakers:
- Chris Gagnon – Solutions Engineer, Kongsberg Geospatial
- Rex Hayes – Director of Unmanned Systems, Kongsberg Geospatial
- Michael Segala, Phd. – CEO, SFL Scientific
Registration:
Registration is free for all attendees