Neural inspired sparse sensing and control for agile flight
Abstract
Flying animals are superbly adapted to acquire and process information about themselves and the environment to control their movement in an exceedingly complex and dynamic world. They do so over a vast range of temporal and spatial scales. Importantly, the micro circuits in insect neural systems operate under stringent constraints of size, weight, and power. In stark contrast with many modern engineered systems, these remarkable motor behaviors are achieved not by brute force computation and learning, but rather with specialized hardware and relatively sparse neuronal com putations. Therefore, sparsity is a central concept in understanding neural control of agile flight, where it serves as a mathematical framework to promote hyper efficient solutions and to achieve robust sensing and control. In this proposal, we seek understanding of and inspiration from liv ing systems, which provide proof by existence that sparse sensing, processing, and computation can achieve remarkably agile and rapid control in complex, nonlinear, and uncertainty environ ments. We will combine expertise in sensory biology, systems neuroscience, machine learning, sparse optimization, control theory, sensor design, and robotics. Our three main technical thrusts are centered around biological principles underlying agile insect flight at different timescales. We will integrate modern biological experiments with the associated development of enabling compu tations and physical models. The proposed research program will have broad impact for societal and defense interests, leading to significant innovations in our ability to design efficient sensor networks, to perform adaptive, nonlinear control of multiscale systems, and to achieve agile flight sensing and control. Our integrated approach can contribute foundational biological knowledge as well as lead to transformative engineering capabilities, with impact for the next generation of hyper efficient, autonomous engineered systems.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 14, 2022
- Source ID
- FA95501910386
Entities
People
- Bing W. Brunton
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of Washington