NASA SBIR 2017 Solicitation


PROPOSAL NUMBER: 17-2 A1.09-8639
SUBTOPIC TITLE: Vehicle Safety- Internal Situational Awareness and Response
PROPOSAL TITLE: Improved UAS Robustness Through Augmented Onboard Intelligence

SMALL BUSINESS CONCERN (Firm Name, Mail Address, City/State/Zip, Phone)
Black Swift Technologies, LLC
3200 Valmont Road, Suite 7
Boulder, CO 80301 - 2887
(720) 638-9656

PRINCIPAL INVESTIGATOR/PROJECT MANAGER (Name, E-mail, Mail Address, City/State/Zip, Phone)
Dr Jack Elston
2100 Central Avenue, Suite 102
Boulder, CO 80301 - 2887
(720) 933-4503

CORPORATE/BUSINESS OFFICIAL (Name, E-mail, Mail Address, City/State/Zip, Phone)
Dr Jack Elston
2100 Central Avenue, Suite 102
Boulder, CO 80301 - 2887
(720) 933-4503

Estimated Technology Readiness Level (TRL) at beginning and end of contract:
Begin: 4
End: 7

Technology Available (TAV) Subtopics
Vehicle Safety- Internal Situational Awareness and Response is a Technology Available (TAV) subtopic that includes NASA Intellectual Property (IP). Do you plan to use the NASA IP under the award?

TECHNICAL ABSTRACT (Limit 2000 characters, approximately 200 words)

The overall goal of the technology developed in this SBIR is to aid in enabling ubiquitous operations of UAS in the national airspace. This includes beyond-line-of-sight operations, flights over populated areas, and fully autonomous operations without direct human oversight. This overarching vision will require many new advancements such as collision avoidance capabilities, GPS denied navigation, and improvements in overall system reliability and robustness. The specific technology gap addressed in this SBIR is focused on improving reliability, subsystem failure tolerance, and automated diagnostics. The specific technical objectives of this Phase II include:

  1. Finalize and validate the design of the new subsystems including the actuator and battery monitoring nodes, the vision node, and the flight management node.

  2. Continued iterative design and testing of machine learning techniques for identifying failures and required maintenance as well as machine learning algorithms for safe landing detection. These will be built and improved using the new hardware and additional flight experiments.

  3. Bench-top testing and hardware-in-the-loop simulation of monitoring systems to gather training data, validate sensor selection, processing bandwidth, and algorithm implementation

  4. Implementation of new features in the current user interface to alert the operator in an intuitive manner of subsystem failures or required maintenance. This will be based on standards and concepts that have been proven in manned aircraft.

  5. Deployment of a customer-facing online portal to iteratively test and deploy algorithms in a commercial space using flight data.

  6. Validation of the full system through flight testing on the BST S2 aircraft with analysis on the achieved reliability metrics. This tasks will include early-on flight experiments to gather training data with certain features and failures along with testing of the full system to validate the overall performance towards the end of the Phase II project.

POTENTIAL NASA COMMERCIAL APPLICATIONS (Limit 1500 characters, approximately 150 words)
It is the goal of this technology to greatly reduce the amount of training and expertise needed to fly UAS, and enable operators such as NASA scientists to directly conduct field campaigns without sacrificing safety. Within NASA, this capability will allow wider adoption where UAS can be operated with less training, less focus on maintenance, and more focus on the data and information gathered by the aircraft. This will be enabled by automatically communicating the need for specific routine maintenance to the user. Furthermore, automated warnings and actions in the form of popup checklists on the user interface during flight will reduce the need for expert operators to be able to deal with these contingencies.

NASA also has a history of conducting new and difficult missions with UAS in challenging environments, such as the deployment of the Sierra UAS in the Arctic environment and the DragonEye to perform volcanic plume characterization. The proposed system will be designed to extend monitoring capabilities to new types of missions and reduce flight risks, such as the detection of aircraft icing using machine learning approaches. The small size of the proposed system will ensure this type of capability can then be employed in small UAS, enabling operations in areas that would historically be considered too risky. This will enable more frequent and capable flight campaigns for NASA Earth Science missions.

POTENTIAL NON-NASA COMMERCIAL APPLICATIONS (Limit 1500 characters, approximately 150 words)
BST aims to utilize the proposed monitoring technology to further lower the barrier of entry and reduce the risk of mission failure due to maintenance mistakes or user error during off nominal flight conditions. Increasing industry confidence in UAS technology is required to continue to grow the market. Furthermore increasing reliability will allow customers to operate increasingly expensive payloads. This will enable more advanced capabilities for UAS, growing the size of the potential market and leading to wider adoption by commercial operators and higher demand for the new capabilities by their customers. As an example, survey and GIS companies can regularly begin using sensors such as GPS RTK and scanning LIDAR without the fear of the prohibitively high cost associated with an accident. These specific sensors will create more demand; there are many areas that need 3D mapping where photogrammetry does not work well due to trees and other vegetation. Those areas are currently rarely serviced by UAS due to the high cost of the sensors. More reliable UAS are also essential for making a safety case with the FAA to allow new types of missions. Reducing failure likelihood due to consistent maintenance and improving flight anomaly detection and mitigation will be important factors in enabling beyond visual line of sight operations and eventually fully autonomous flights without direct human oversight. Many markets and missions will take advantage of these sorts of capabilities.

TECHNOLOGY TAXONOMY MAPPING (NASA's technology taxonomy has been developed by the SBIR-STTR program to disseminate awareness of proposed and awarded R/R&D in the agency. It is a listing of over 100 technologies, sorted into broad categories, of interest to NASA.)
Air Transportation & Safety
Algorithms/Control Software & Systems (see also Autonomous Systems)
Autonomous Control (see also Control & Monitoring)
Avionics (see also Control and Monitoring)
Data Acquisition (see also Sensors)
Data Fusion
Hardware-in-the-Loop Testing
Recovery (see also Autonomous Systems)

Form Generated on 03-05-18 17:24