SBIR-STTR Award

Makel Engineering, Inc. proposes to develop a highly compact Environmental Microsensor Array (EMMA) as a payload for free flying Intra-Vehicular Activity (IVA) robots, such as NASA’s Astrobee. EMMA will include machine learning to translate and interpret onboard sensor data (e.g., chemicals, temperature, humidity, pressure, acoustic, etc.) within the context of planetary facilities. Planned human exploration beyond Earth orbit will rely on an orbiting facility near the Moon, called ‘Gateway’ with intermittent human occupation, requiring robust autonomous inspection and diagnostics tools. EMMA’s sensors and machine learning algorithms will establish nominal background conditions throughout the vehicle, to easily identify anomalies and trigger further action. For instance, fast leak of pressurized lines may be detected quickly via change in noise levels, which would trigger EMMA to look for small changes in pressure level and small changes in chemical signatures, which combined and compared to nominal background conditions using machine learning tools, provide early diagnostics data that would enable quick mitigation actions (e.g., isolation by valves, selectively shutting down systems, etc.). Phase I will identify Gateway use cases to define EMMA’s requirements and scenarios for occupied and vacant periods. EMMA onboard COTS autonomous robots (e.g., floor cleaning robots) will navigate staged occupied or vacant rooms, simulating the nominal and fault conditions of real scenarios. Phase I machine learning algorithms will be deployed on external computers. Phase II will mature the technology taking from lab demonstration to a series of flyable prototypes which will be delivered to NASA for testing in ongoing microgravity experiments and integration with Astrobee at NASA ground-based testbeds at ARC and JSC during the program.. The machine learning algorithms will be migrated to the embedded processors, resulting in a standalone prototype system. Potential NASA Applications (Limit 1500 characters, approximately 150 words) EMMA onboard Astrobee robotic flyer for deployment in NASA’s proposed Gateway during manned and unmanned periods for regular inspection and diagnostics. Example uses: -Early fire detection and outgassing from electrical insulators (e.g. CO, hydrocarbons, HCl, HCN, HF, non-contact temperature to monitor overheating) -Identification and tracking of fast and slow leaks (noise, pressure change, chemicals: H2, NH3, VOCs) -Air quality monitoring to ensure health of revitalization systems (CO2, O2, humidity, trace contaminants) Potential Non-NASA Applications (Limit 1500 characters, approximately 150 words) EMMA on flying/crawling drones used in large, high rise office buildings, surveying air quality to ensure workplace safety. EMMA could be used for robotic leak detection and air quality in chemical plants and mining operations. Comprehensive, low cost, and near real time inspection and diagnosis of systems (e.g., ventilation, utilities) in health care facilities to mitigate disease outbreaks.

Makel Engineering, Inc. proposes to develop a highly compact Environmental Microsensor Array (EMMA) as a payload for free flying Intra-Vehicular Activity (IVA) robots, such as NASA’s Astrobee, supporting the Integrated System for Autonomous and Adaptive Caretaking (ISAAC) project. EMMA will include machine learning to translate and interpret onboard sensor data (e.g., chemicals, temperature, humidity, pressure, etc.) within the context of planetary facilities. Planned human exploration beyond Earth orbit will rely on an orbiting facility near the Moon, called Gateway, with intermittent human occupation, requiring robust autonomous inspection and diagnostics tools. EMMA’s sensors and machine learning algorithms will establish nominal background conditions throughout the vehicle, to identify anomalies and trigger further action. The use of machine learning tools will enable EMMA to recognize changes in patterns and decide if additional investigation is granted, e.g., if hot spot detected indicating a potential fire, use chemical sensors to classify material type and further isolate the source of fire, enabling corrective action (e.g., selectively shutting down affected systems.). Phase I identified Gateway use cases and defined EMMA’s relevant requirements. EMMA was deployed onboard COTS autonomous floor cleaning robots navigating rooms for data collection and deployed towards simulated fault conditions. Phase I machine learning algorithm was deployed and demonstrated with the data collected by EMMA. Phase II will mature the technology taking from lab demonstration to a series of flyable prototypes which will be delivered to NASA for testing in ongoing microgravity experiments and integration with Astrobee at NASA ground-based testbeds at ARC and JSC during the program. The machine learning algorithms will be migrated to the embedded processors, resulting in a standalone prototype system. Potential NASA Applications (Limit 1500 characters, approximately 150 words): EMMA onboard Astrobee robotic flyer for deployment in NASA’s proposed Gateway during manned and unmanned periods for regular inspection and diagnostics, supporting ISAAC’s mission. Example uses: -Early fire detection and outgassing from electrical insulators (e.g. CO, CO2, VOCs, HCl, HCN, HF, non-contact temperature to monitor overheating) -Identification and tracking of slow leaks and fast leaks (pressure change, chemicals: H2, NH3, VOCs) -Air quality monitoring to ensure health of revitalization systems (CO2, O2, humidity, trace contaminants) Potential Non-NASA Applications (Limit 1500 characters, approximately 150 words): EMMA on flying/crawling drones used in large, high rise office buildings, surveying air quality to ensure workplace safety. EMMA could be used for robotic leak detection and air quality in chemical plants and mining operations. Comprehensive, low cost, and near real time inspection and diagnosis of systems (e.g., ventilation, utilities) in health care facilities to mitigate disease outbreaks. Duration: 24