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Tuesday, February 6, 2018
12:00pm to 1:00pm
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Annenberg 105
Nonlinear control and estimation theory for multi-agent autonomy and autonomous flying ambulances
Soon-Jo Chung, Associate Professor of Aerospace and Bren Scholar; Jet Propulsion Laboratory Research Scientist, Engineering and Applied Science, CALTECH,

Recent advances in self-driving car and drone technologies are turning a century-old dream of vertical-take-off-landing personal transportation vehicles into a reality with many existing projects in development. Caltech's Center for Autonomous Systems and Technologies (CAST)'s engineers and scientists have developed a 1/5 working scale model of their Autonomously Flying Ambulance (AFA) with innovative design ideas, including flight by a hybrid of distributed fans and deployable wings, bio-inspired flight and control, and vision-based navigation. The model has been flight-tested successfully in CAST's unique drone arena using an open-air distributed fan-array wind tunnel. CAST's AFA rotorcraft and autonomy technologies can provide solutions for a range of short-distance travel challenges: point-to-point delivery of packages on Earth or scientific samples on Mars. I will review some of the control theoretical results derived for control and coordination of novel aerial robotic platforms.  First, I will present distributed, motion planning and multi-point routing algorithms for optimally reconfiguring swarms of vehicles with limited communication and computation capabilities from various pick-up locations to target locations. The real-time guidance algorithm solves both the optimal assignment and collision-free trajectory generation in an integrated manner. Three related approaches have been derived for optimal assignment problem for real-time routing: (1) distributed auction assignment, (2) novel probabilistic swarm guidance that employs time-inhomogeneous Markov chains; and (3) potential games solved by binary log-linear learning. Second, nonlinear tracking control and estimation is utilized to track optimal reconfiguration trajectories with a property of robustness (finite-gain Lp incremental stability). I will also show such nonlinear incremental stability analysis can be extended to a set of Itô stochastic nonlinear systems for synchronization control and nonlinear estimation, including exponential stability of a distributed Bayesian filtering algorithm, robust nonlinear estimation for visual SLAM, and consensus stability of distributed reinforcement learning for flying ambulances or taxis.

For more information, please contact Diane Goodfellow by email at [email protected].