Efficient strategies for visually guided flight: from insects to drones. Flying in real environments, that are densely cluttered with obstacles, is a major challenge limiting the proliferation of aerial robotic technology yet flying insects such as honeybees accomplish this task with ease. This project will seek to uncover the salient vision-based flight-control strategies implemented by insects to deal with clutter. These will be used to develop sensory and information processing frameworks for ....Efficient strategies for visually guided flight: from insects to drones. Flying in real environments, that are densely cluttered with obstacles, is a major challenge limiting the proliferation of aerial robotic technology yet flying insects such as honeybees accomplish this task with ease. This project will seek to uncover the salient vision-based flight-control strategies implemented by insects to deal with clutter. These will be used to develop sensory and information processing frameworks for implementation in miniature robotic systems which will allow them to navigate autonomously in complex environments even when GPS positioning is denied. Such capabilities will expand the operational domain and potential applications for small autonomous vehicles while improving our knowledge of insect locomotion.Read moreRead less
When stabilization and optimization meet: a codesign approach. The next generation of engineered systems need to perform complex tasks with precision, and be robust, resilient and adaptive to their environment enabled by the confluence of control, optimization, learning and computation Understanding the interplay between robust stability and optimization is key to this endeavor. Many techniques, such as model predictive control and reinforcement learning, rely on an intricate interplay between ....When stabilization and optimization meet: a codesign approach. The next generation of engineered systems need to perform complex tasks with precision, and be robust, resilient and adaptive to their environment enabled by the confluence of control, optimization, learning and computation Understanding the interplay between robust stability and optimization is key to this endeavor. Many techniques, such as model predictive control and reinforcement learning, rely on an intricate interplay between an optimization-based control algorithm and an optimization routine used to calculate the control law. This project aims to develop a general design framework for stability, suboptimality and robustness of such algorithms, that can be used in range of novel applications, such as driverless cars and drones. Read moreRead less