Design and Test of a Flapping Wing Micro-Aerial Vehicle

The objective of this project is to design, fabricate and test a Flapping Wing Micro Aerial Vehicle (MAV) with hovering capability. The MAV is designed to bio-mimic hummingbird flight, in which the wings flap in a plane perpendicular to the horizontal. Aerodynamic lift is created from the interaction between the wings and the vortex generated by the previous stroke (wake-capture motion). The team designed, manufactured and assembled an initial static prototype. The final design is characterized by a wing span of 36 cm and a weight of 41 g. The wings have been manufactured using monokote reinforced with carbon fiber, while the chassis and drive train have been 3-D printed based on thermoplastics

EMS Communication Systems Data Analysis

The exponential growth of the human population requires an increasingly efficient EMS infrastructure and service. The purpose of this project was to examine and analyze various aspects of the EMS communication system within the United States of America, and identify areas that could be improved. Current operation and maintenance of EMS communication system were investigated, including input and retrieval of information, data storage, infrastructure and modes of communication. Areas for improvement were recognized, and feasible solutions that could improve time response, were researched. A medical database was developed to store and retrieve patient information and delay differential equations were formulated to reduce system dynamic delays

Artificial Intelligence for the Advancement of Lunar and Planetary Science and Exploration

Over the past decades of NASA’s inner solar system exploration, data obtained from the Moon alone accounts for ~76%. Most of the lunar orbital spacecraft of the past and present carried imaging cameras and spectrometers (including multispectral and hyperspectral payloads), as well as a large variety of other passive and active instruments. For example, NASA’s Lunar Reconnaissance Orbiter (LRO) has been operating for more than 10 years, providing us with ~1206 TB of lunar data which amounts to ~99.5% of the total data contributed by NASA built instruments. Given recent advances in instrument and communication capabilities, the amount of data returned from spacecraft is expected to keep rising quickly. The white paper focus on potential components of AI and ML that could help to accelerate the future exploration of the Moon and other planetary bodies. The white paper highlights on selected AI/ML-based approaches for lunar and planetary surface science and exploration, the need for open-source availability of training, validation, and testing datasets for AI-ML based approaches, and need for opportunities to further bridge the gap between industry and academia for advancing AI-ML based research in lunar and planetary science and exploration

Artificial Intelligence for the Advancement of Lunar and Planetary Science and Exploration

Over the past decades of NASA’s inner solar system exploration, data obtained from the Moon alone accounts for ~76%. Most of the lunar orbital spacecraft of the past and present carried imaging cameras and spectrometers (including multispectral and hyperspectral payloads), as well as a large variety of other passive and active instruments. For example, NASA’s Lunar Reconnaissance Orbiter (LRO) has been operating for more than 10 years, providing us with ~1206 TB of lunar data which amounts to ~99.5% of the total data contributed by NASA built instruments. Given recent advances in instrument and communication capabilities, the amount of data returned from spacecraft is expected to keep rising quickly. The white paper focus on potential components of AI and ML that could help to accelerate the future exploration of the Moon and other planetary bodies. The white paper highlights on selected AI/ML-based approaches for lunar and planetary surface science and exploration, the need for open-source availability of training, validation, and testing datasets for AI-ML based approaches, and need for opportunities to further bridge the gap between industry and academia for advancing AI-ML based research in lunar and planetary science and exploration