CHARLES RIVER ANALYTICS, INC. — Department of Homeland Security SBIR Phase II: H-SB019.1-010

CHARLES RIVER ANALYTICS, INC. — SBIR Phase II award from Department of Homeland Security.

Amount
$999,978
Agency
Department of Homeland Security · Countering Weapons of Mass Destruction
Program / Phase
SBIR · Phase II
Topic
H-SB019.1-010
Solicitation
FY19.1
NAICS
Place of performance
MA
Period
2020-09-10 → 2022-09-09

Description

A successful WMD terrorist attack against the United States would have profound and potentially catastrophic impact on our nation. Quick, efficient, and effective localization of radiological threats in unstructured environments is imperative to mitigate and deny such an event. Man-portable devices are used to localize radiological materials in unstructured environments, but manned detection is both costly and dangerous due to exposure and contamination risk requiring immediate decontamination to mitigate adverse and long-term health effects.<br/> Real-time, detection, localization and mapping of radiological materials has applications ranging from nuclear decommissioning, waste management, and environmental remediation to emergency response, international safeguards, and homeland security. Man-portable radiation detectors have become smaller and more capable, and commercial unmanned aircraft systems (UAS) have become much cheaper and more sophisticated. Charles River Analytics has matured drone technologies with increased levels of autonomy using commercial off-the-shelf (COTS) sensors and drones enabling us to keep costs down while ensuring operators are out of harm's way.<br/> Charles River Analytics proposes to develop a system for identifying radiological or nuclear (RAD/NUC) threats using COTS radiation detectors in combination with perceptual sensors on small drone(s) flying autonomously or semi-autonomously. The 3-D radiation localization and mapping payload (RADLAMP) is platform-agnostic and can be deployed on unmanned ground, or aerial vehicles, without the need for external power or offline processing. Using advanced computer vision/machine learning techniques, radiation detection, and autonomous navigation algorithms, the envisioned product extends our existing product lines and furthers the capabilities of relevant stakeholders in homeland security.