This project will result in the commercialization of an Intelligent Electronic Speed Controller (IESC) for use on Unmanned Aerial Vehicles (UAVs). The IESC will advance the state-of-the-art of health-state awareness. This will be achieved through the integration of propulsion system health monitoring sensors that - in unison with an Intelligent Rule Set - will be able to monitor system and component performance trends and predict propulsion system faults. The system is designed to provide the analytic capability necessary to predict propulsion system degradation, maintenance or repair needs. An Artificial Neural Network (ANN) will be trained on data from IESC sensors from nominal flights and those with known faults leading to failure. After training, an initial Intelligent Rule Set will be extracted to represent the knowledge of the ANN and used in the system to predict failures. This set of rules will be periodically updated as more flight data is collected. Potential NASA Commercial Applications:
(Limit 1500 characters, approximately 150 words) This effort supports the objectives of the NASA Unmanned Aerial System Traffic Management (UTM) system concept and also the activities of NASA's Small Unmanned Aerial Vehicle Laboratory (SUAVE Lab). Successful implementation of the UTM concept will require that UAVs operate without failure or fault to the greatest extent possible. UTM Technical Capability Level Four will involve higher-density urban areas for autonomous vehicles used for news gathering and package delivery (with a demonstration target of 2019); flight incidents in urban areas could result in injury to humans or damage to property of loss of control incidents occur. The SUAVE Lab designs, develops, builds and tests small UAVs and provides expertise to national level organizations on small UAV designs, operations and airspace integration. The technology serves to ensure the reliability of small UAV systems advances as needed to support expansion of their use in the future.
Potential NON-NASA Commercial Applications:
(Limit 1500 characters, approximately 150 words) Non-NASA use will target manufacturers of Unmanned Aerial Vehicles (UAVs) in the commercial market sectors that provide their end-users with highly reliable UAVs. Currently, there are 35,000 FAA-certified UAV pilots. Reliability will become increasingly important in these market sectors as the cost and complexity of payloads increases and as proximity to humans and property decreases. The technology developed will be low-cost and will integrate seamlessly with existing designs. UAVs are well-suited to performing many types of missions including those that are inherently dangerous to humans, those that require precision flight for data collection, and those that need to be performed within a limited budget. Applications for UAVs include aerial photography, remote sensing, disaster response, agricultural monitoring, forestry service support (including forest fires), infrastructure inspection, mining and quarrying, and environmental surveys to name a few.As submitted: Manufacturers of Unmanned Aerial Vehicles (UAVs) that sell systems for valuable payloads are the primary customers. The technology will integrate seamlessly with existing UAV designs. Commercial applications include aerial photography, remote sensing, disaster response, agricultural monitoring, forestry service support, infrastructure inspection, mining and quarrying, and environmental surveys. 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.) Acoustic/Vibration Algorithms/Control Software & Systems (see also Autonomous Systems) Autonomous Control (see also Control & Monitoring) Circuits (including ICs; for specific applications, see e.g., Communications, Networking & Signal Transport; Control & Monitoring, Sensors) Condition Monitoring (see also Sensors) Diagnostics/Prognostics Inertial Intelligence Recovery (see also Autonomous Systems) Thermal