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Seminar Name/Date/Time Location Description



Grace Xingxin Gao, PhD

University of Illinois at Urbana-Champaign

Thursday 3/16/2017 4:00-5:00pm 


FXB 1109 Boeing Lecture Hall


The ever-growing applications of Unmanned Aerial Vehicles (UAVs) require UAVs to navigate at low altitude below 2000 feet. Traditionally, a UAV is equipped with a single GPS receiver. When flying at low altitude, a single GPS receiver may receive signals from less than four GPS satellites in the partially visible sky, not sufficient to conduct trilateration. In such a situation, GPS coordinates become unavailable and the partial GPS information is discarded. A GPS receiver may also suffer from multipath errors, causing the navigation solution to be inaccurate and unreliable.

In this talk, we present our recent work on UAV navigation using not one, but multiple GPS receivers, either on the same UAV or across different UAVs fused with other navigational sensors, such as IMUs and vision. We integrate and take use of the partial GPS information from peer GPS receivers and are able to dramatically improve GPS availability. We apply advanced filtering algorithms to multiple GPS measurements on the same UAV to mitigate multipath errors. Furthermore, multiple

UAVs equipped with on-board communication capabilities can cooperate by forming a UAV network to further improve navigation accuracy, reliability and security.

Brendan Englot, PhD

Stevens Institute of Technology

Friday 3/17/2017 12:00-1:00pm 


EECS 1008


 I will discuss a three-tiered research effort to develop algorithms that will enable autonomous underwater robots to operate reliably in complex, cluttered 3D environments. The first provides a foundation for navigating in the absence of prior knowledge of the environment – 3D occupancy mapping with an underwater robot equipped with a scanning sonar. We apply Gaussian processes and other supervised learning techniques to build real-time predictive occupancy maps over sparse and noisy data. The middle tier of our effort addresses exploring an unknown environment while a map is being constructed – and the application of supervised learning to efficiently predict the information gain of candidate sensing actions. This is achieved with the aid of Bayesian optimization. Finally, when an accurate model of the environment is available, I will discuss approaches for motion planning under uncertainty that will allow a robot to curb the growth of localization error under limited sensing resources. A carefully chosen metric to represent localization uncertainty will allow the efficient propagation of uncertainty along a graph, and the search of the graph for paths that optimally curb goal-state uncertainty. 


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