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Multiple-camera system that ensures safer operations using ML

Army taps Charles River Analytics to develop personnel safety system for autonomous ground systems

Charles River Analytics was awarded a $1.7 million contract from the Army Small Business Innovation Research (SBIR) program to develop a personnel safety system for autonomous ground systems.

Operational safety is paramount to successfully deploying driverless systems, as they will operate alongside soldiers, bystanders, and hostile actors. Therefore, these systems must be equipped with a sensing solution to avoid collisions and enemy threats, among other safety concerns.

The Advanced Safety and Intelligent Mobility through Onboard Vision (ASIMOV) system provides day-or-night, 360-degree awareness of personnel. ASIMOV can continuously track stationary and moving pedestrians while anticipating the movements and intentions of friendly versus hostile combatants. Offering constant surveillance, ASIMOV enables maximum mobility while maintaining the vehicle’s safety radius. The system avoids active sensors such as lidar or radar, which can compromise covertness. Instead, it uses cameras and machine learning algorithms to distinguish between friendly and hostile pedestrians.

“We are developing fast, robust technologies for reliably detecting and tracking people in challenging environments and situations,” said Camille Monnier, Principal Scientist at Charles River Analytics and Principal Investigator on the ASIMOV effort.

By improving the safety of automated vehicles, Charles River Analytics expects that the full-scope ASIMOV system will contribute to deploying and adopting robotic combat vehicles throughout the armed forces. As trust in robotic vehicles among personnel grows thanks to tools like ASIMOV, overall confidence in the Army’s autonomous ground systems is expected to increase.

Contact us to learn more about ASIMOV and our other robotics and autonomy capabilities.

This material is based upon work supported by the ASA(ALT) SBIR CCoE under Contract No. W51701-22-C-0062. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the ASA(ALT) SBIR CcoE.

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