MURI: Neural-inspired Sparse Sensing and Control for Agile Flight

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 for flight are achieved not by brute-force computation and learning, but rather with specialized hardware and relatively sparse neuronal computations. 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 MURI, we seek understanding of and inspiration from living systems, which provide proof by existence that sparse sensing, processing, and computation can achieve remarkably agile and rapid flight control in complex, nonlinear, and uncertainty environments. Our multi-institute team of researchers combines expertise in sensory biology, systems neuroscience, machine learning, sparse optimization, control theory, sensor design, and robotics.