Biography
Jonathan Gammell is a Departmental Lecturer in Robotics at the Oxford Robotics Institute (ORI). He leads the Estimation, Search, and Planning (ESP) research group which seeks to develop and exploit better understandings of fundamental robotic problems. He holds a Ph.D. and M.A.Sc. in Aerospace Science & Engineering from the University of Toronto (UTIAS) and a B.A.Sc. in Mechanical Engineering (Co-op) with a Physics Option from the University of Waterloo.
Jonathan is a dedicated 'full-stack' roboticist with extensive experience solving real-world problems with robotic hardware and software. He has deployed autonomous systems around the world on a variety of projects. Please see his biography on ESP's webpage for more information.
Most Recent Publications
Task and Motion Informed Trees (TMIT*): Almost-Surely Asymptotically Optimal Integrated Task and Motion Planning
Thomason W, Strub MP & Gammell JD (2022), IEEE Robotics and Automation Letters, 7(4), 11370-11377
Task and Motion Informed Trees (TMIT*): Almost-Surely Asymptotically Optimal Integrated Task and Motion Planning
Thomason W, Strub MP & Gammell JD (2022)
The Surface Edge Explorer (SEE): A measurement-direct approach to next best view planning
Border R & Gammell JD (2022)
Effort Informed Roadmaps (EIRM*): Efficient Asymptotically Optimal Multiquery Planning by Actively Reusing Validation Effort
Hartmann VN, Strub MP, Toussaint M & Gammell JD (2022)
Application of a robotics path planning algorithm to assess the risk of mobile bearing dislocation in lateral unicompartmental knee replacement.
Yang I, Gammell JD, Murray DW & Mellon SJ (2022), Scientific reports, 12(1), 2068
Research Groups
Most Recent Publications
Task and Motion Informed Trees (TMIT*): Almost-Surely Asymptotically Optimal Integrated Task and Motion Planning
Thomason W, Strub MP & Gammell JD (2022), IEEE Robotics and Automation Letters, 7(4), 11370-11377
Task and Motion Informed Trees (TMIT*): Almost-Surely Asymptotically Optimal Integrated Task and Motion Planning
Thomason W, Strub MP & Gammell JD (2022)
The Surface Edge Explorer (SEE): A measurement-direct approach to next best view planning
Border R & Gammell JD (2022)
Effort Informed Roadmaps (EIRM*): Efficient Asymptotically Optimal Multiquery Planning by Actively Reusing Validation Effort
Hartmann VN, Strub MP, Toussaint M & Gammell JD (2022)
Application of a robotics path planning algorithm to assess the risk of mobile bearing dislocation in lateral unicompartmental knee replacement.
Yang I, Gammell JD, Murray DW & Mellon SJ (2022), Scientific reports, 12(1), 2068
