SBIR/STTR Research Award

TRUST: Testing Robustness of UAS Safety Technology

Award identifier db5b50736c389e21782a0cda38a59a4e0d0f7b4da684b3090743d47a6f9fbd18Awarding agency Department of Defense · Office of the Secretary of Defense

Award amount

$981,324

Program and phase

SBIR · Phase II

Award year

2015

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Award details

Awarding agency
Department of Defense
Branch
Office of the Secretary of Defense
Recipient company
SOAR TECHNOLOGY, LLC
Proposal award date
2015-08-13
Contract end date
2017-11-14
UEI
L9Q4Y83HTUM3
Solicitation number
2013.3
Topic code
OSD13-HS2

Research abstract

It is understood that an autonomous unmanned air, ground, or sea vehicles can incur a near infinite decision space that is difficult to capture completely in extensive simulation. The response of these vehicles to untrained environments can potentially have unintended consequences to adversely affect safety. This is of particular concern when these vehicles are considered for collaborative manned/unmanned teaming missions. For such systems, a run time verification engine may be developed to ensure the safety of human life by constraining the output of the autonomous algorithm to guarantee actions are correct, interpretable, and recoverable. The autonomous algorithm combined with the failsafe mechanism is intended to improve the robustness of autonomous systems to unknown environments and unexpected events. However, if such a failsafe/recovery mechanism existed, what evaluation systems are available to test their viability and robustness The intent of this solicitation is to develop a verification method to examine the robustness of a run time safety algorithm. The first objective is to examine the techniques presented in [1][2] and apply them to an autonomous unmanned vehicle model that includes a learning trajectory generation algorithm. The techniques in [1] present a method to protect the behavior of an adaptive / learning function. The techniques in [2][3] present methods to analyze the robustness of the implemented safety algorithm. Due to the dependence of autonomous systems on historical state data, current simulation environments require the need for extensive run times to reach a potential unintended operating region. Additionally, as a greater quantity of information is fused and utilized by the autonomous algorithm to make decisions, gradual, unintended data streams may induce state conditions that may cause an unsafe or unpredictable response. A key capability must be to rapidly re-stimulate the system to an untrained, unintended, or erroneous operating state in order to assess the robustness of the run time safety algorithm. The verification algorithm must implement: A method to introduce specific logical or run time operating states that induce an algorithm failure. A mechanism for recording and initializing systems to specific states. An interface control description that emulates real world sensor outputs to be provided to the system under test. The generation of a robustness measure around an operating region [2][3].

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

240 SBIR/STTR records share this UEI (1998–2023). Reported award amounts total $124,845,066. Reported amounts are not federal obligations.

Phases: Phase I, Phase II, Phase II – Direct to Phase II

Agencies: Department of Defense, National Aeronautics and Space Administration, Department of Education, Department of Transportation

Agency intelligence

Department of Defense

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