Learning Methods And The Future of Aerial Robots Challenges & Opportunities

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Unmanned aerial vehicles (UAVs) have been explored for a wide variety of applications ranging from search and rescue, package delivery, traffic monitoring, and exploration tasks in an unknown environment.

In this talk will focus both model-based and model-free learning methods to handle various real-time aerial robot control problems.

this talk will also introduce deep learning methods for perception and planning of aerial robots as applied to the autonomous drone racing problem.

Last but not least, the talk will address the anomaly detection problem using drones. A deep neural network-based method will be introduced to find point anomalies (i.e., single instance anomalous data) and contextual anomalies (i.e., context-specific abnormality) in an environment using a UAV.

Agenda:

11:45 am - 11:50 am Arrival and socializing

11:50 am - 11:55 am Opening

12:00 pm - 1:50 pm Erdal Kayacan, "Learning Methods And The Future of Aerial Robots Challenges & Opportunities"

1:50 pm - 2:00 pm Q&A

About Erdal Kayacan:

Erdal Kayacan received a Ph.D. degree in electrical and electronic engineering at Bogazici University, Istanbul, Turkey. He is pursuing his research at Aarhus University at the Department of Electrical and Computer Engineering as an associate professor and he is the Director of Artificial Intelligence in Robotics Laboratory (AiR Lab). He has since published more than 140 peer-refereed book chapters, journal and conference papers in modelbased and model-free control, parameter and state estimation, computer vision, motion and path planning for robots. He has completed a number of research projects which have focused on the design and development of ground and aerial robotic systems, vision-based control techniques and artificial intelligence.

Please register using the zoom link to get a reminder:

https://us02web.zoom.us/webinar/register/WN_qxO3ZI3jTZu4NjOybmrT7Q

Webinar ID: 861 8175 2812