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Jonathan Gammell Joint CS/ECE Faculty Candidate Seminar

Event Type
Electrical and Computer Engineering and Computer Science
2405 Siebel Center and Zoom
Mar 2, 2023   10:00 - 11:00 am  
Dr. Jonathan Gammell, Adjunct Fellow, University of Oxford
Angie Ellis
Originating Calendar
Illinois ECE Calendar

Dr. Jonathan Gammell

Adjunct Fellow, University of Oxford

Joint CS/ECE Faculty Candidate Seminar

Thursday, March 2, 2023, 10:00-11:00am

2405 Siebel Center and Via Zoom

Title: Measuring and Planning Motion in Robotics

Abstract: Autonomous robots are being used in increasingly difficult real-world situations. These exciting new applications highlight the current limitations of autonomy. Advancing the state of the art will require significant research on core problems in robotics, including motion estimation and motion planning.
This talk will present work on understanding these problems and using this knowledge to design better solutions. It will present work on multimotion estimation and motion planning in continuous search spaces. The first extends the success of Visual Odometry (VO) to measuring other motions in the scene. The second unifies and extends informed, graph-based search (e.g., A*) and anytime, sampling-based planning (e.g., RRT*) to create informed, anytime sampling-based planning algorithms for a variety of problems in continuous spaces. These planning algorithms exploit universal properties of the problem to perform better on many real-world robotic planning problems, especially in the presence of complex constraints or high state dimensions.

Jonathan Gammell is an Adjunct Fellow of the Oxford Robotics Institute at the University of Oxford, where he founded and leads the Estimation, Search, and Planning (ESP) research group. He holds a B.A.Sc. in Mechanical Engineering with a physics option from the University of Waterloo and a M.A.Sc. and Ph.D. in Aerospace Science & Engineering from the University of Toronto.
 His research at ESP is focused on understanding the fundamental problems of robotics and autonomy and using this knowledge to develop theoretically well-founded algorithms. This work is tested on robots operating in complex environments, either independently or in collaboration with external partners, including NASA JPL, and is used widely in real-world robotic systems, from steerable catheters to full-sized autonomous helicopters.




link for robots only