NRI: EAGER: Teaching Aerial Robots to Perch Like a Bat via AI-Guided Design and Control

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This research will develop the scientific and technological foundations for design of small unmanned aerial vehicles (UAVs) with bat shapes and flying abilities. These bat-like UAVs will be non-intrusive and safe to operate in shared spaces to provide situational awareness to humans. Our bat-inspired design will also be collision-tolerant to negotiate cluttered, hard-to-access environments in the physical world. The resulting technology can significantly improve public safety and vehicular dynamic traffic control in smart cities and cost-effectiveness associated with monitoring environmental disasters. Ultimately, the UAV can provide computing, communication and sensing capabilities in large-scale systems such as, residential buildings, streets, construction zones, and state parks. These capabilities should result in enormous societal impact and economic benefit. In addition, as the result of this project, a new generation of scientists and engineers will be trained in addressing multidisciplinary challenges at the intersection of theory and experiment. The project will create programs and tools to train workforce with new skills including bio-inspired robotics, machine learning and artificial intelligence, and nonlinear control theory. This research adopts an artificial intelligence-guided framework to study bat's flight maneuvers including perching (i.e. upside-down landing), zero-path flight, and hovering. Due to the fact that the salient aspects of the bat's wing motion can be represented in a low-dimensional subspace, we will apply an auto-encoding variational inference approach on the flying data from real animal in order to extract low dimensional, yet interpretable, embedding of the its underlying flight model. We will also use a high-fidelity virtual environment for 3D modeling, synthetic design and validation of the extracted low-dimensional latent variables. This will also lead to better understating of the bat sensory feedback mechanism through a data-driven procedure. Our research objective will simplify the engineering procedure to design bio-inspired aerial co-robots that closely mimic the flight behavior of a target animal, therefore is directly towards lowering the barriers for understanding fundamentals regarding closed-loop control and design of bio-inspired multimodal co-robots. In order to achieve the proposed research objectives, we will center our effort around conducting two main phases during the one year timeline of this project: first, AI-guided analysis and modeling of bat's various flight maneuvers, and second, development of a soft and collision-tolerant bat-inspired aerial agent capable of landing on structures. These two phases will be accomplished by team's cross-disciplinary collaborative, as components are highly interlinked and dependent.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.