Dynamic Sensor-Tasking and Routing System for Subsurface Hazard Detection

SBIR · Phase: BOTH · Topic DTR27BZ02-NV003 · Solicitation 27.BZ

Current field-deployable capabilities for WMD device and component defeat, route reconnaissance, and sensitive site exploitation have made significant strides in rapid surface-level mapping and visual threat triage using unmanned aerial and ground platforms. However, these operational systems encounter a critical bottleneck when confronting subsurface hazards. Visual, optical, and topographic sensors are fundamentally blind to threats that are partially buried, completely subsurface, or obscured beneath soil, blast debris, or disturbed earth. When subsurface geophysical instruments are integrated onto these unmanned platforms to address this blind spot, current field architecture suffers from a severe "collect everything" bottleneck. Lacking localized intelligence, platforms typically execute static, pre-programmed survey scripts which force sensors to operate continuously and indiscriminately across an entire area. This floods constrained tactical communication networks with massive volumes of raw sensor data, overwhelms field computing devices, drastically slows operational tempo, and results in sensor operational blindness when local soil physics degrade or invalidate specific sensor modalities. When conducting standoff reconnaissance of suspected hazard areas—which may contain buried chemical or biological munitions, radiological dispersal device (RDD) components, bulk precursor storage vessels, or associated explosive triggers and conventional ordnance—these bottlenecks force operators into dangerous, prolonged exposure on target. Furthermore, relying on rigid, static classical logic to manage these complex sensor suites in real time introduces a critical limitation, as traditional rule-based programs cannot adapt to highly dynamic, uncharacterized environments where the boundary conditions are not known beforehand. A proactive system is required to answer: "Given this specific environment and mission profile, which sensor should be deployed first, what detection threshold triggers the next modality, and which sensors may be inhibited due to the environment or other physical boundary conditions?" This topic seeks an adaptive, machine-learning-driven sensor-tasking and routing decision engine capable of scheduling sensor deployment and routing platform payloads in real time using probabilistic, data-driven optimization methods. The primary need is an intelligent edge capability that continuously monitors changing site conditions and dynamically queues, activates, or inhibits physical sensor payloads on an autonomous platform. Rather than streaming raw data or running all sensors simultaneously, the system must govern sensor execution through learned decision policies (utilizing techniques such as reinforcement learning, contextual bandits, or partially observable Markov decision processes) that optimize performance based on sensor-inferred environmental physics (such as electrical conductivity and dielectric permittivity), immediate mission constraints (such as required advance speed, standoff distance requirements, and target priority), and real-time sensor performance signatures. When an initial screening modality detects an anomaly, the engine must evaluate the environmental context to dynamically determine if a secondary, higher-fidelity sensor is required to verify and classify the threat, or if the platform should continue its route. By automating this sensor-queuing and routing logic at the edge through explainable and efficient AI, the system will overcome the subsurface bottlenecks of current field suites, eliminate tactical network saturation, minimize time on target, and ensure reliable detection and defeat of subsurface WMD devices and associated hazards across diverse, uncharacterized operational environments. State of Technology: Currently deployed military and commercial technologies have reached a high level of maturity in distinct, stove-piped domains, but they do not integrate these capabilities into a s…

SBIR/STTR 은 미국 중소기업만 지원할 수 있습니다(Small Business Act 법정 요건). • 계열사를 포함해 상시 종업원 500명 이하 • 미국 시민 또는 영주권자 1인 이상이 50%를 초과해 직접 소유·지배 • 미국 내 사업장을 두고 주로 미국 내에서 사업을 영위할 것 • 수행책임자(PI)의 주된 근무처가 신청 기업일 것 출처: https://www.sbir.gov/faq/eligibility-requirements

Primary country

🇺🇸 United States

Also eligible

None

Foreign companies

No

Local entity

Required

Location condition

미국 내 사업장을 두고 미국 시민·영주권자가 50%를 초과해 소유한 중소기업만 신청할 수 있습니다(법정 요건).

Document requirements were not captured (needs verification). Check the official announcement.

Application process not captured.

Evaluation process not captured.

Process steps were not captured.

No risk assessment yet. Re-analyse from Admin.

No IP assessment yet.

Official URL

https://www.dodsbirsttr.mil/topics-app/

Application URL

https://www.dodsbirsttr.mil/topics-app/

Last fetched

2026.11.06

Human review

Not reviewed — check the official announcement

First seen

2026.11.06

Data status

Auto-collected, not reviewed

AI enrichment

Needs verification