Jonathan Gammell
Assistant Professor at Queen's University
Kingston, Ontario, Canada
Summary
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Rockstar🎓
Top SchoolJonathan Gammell is an Assistant Professor at Queen's University with 11 years of experience building and researching robotic systems, particularly in motion planning and autonomous space robotics. He holds a PhD from the University of Toronto and has held research and teaching positions at the University of Oxford and affiliations with JPL and Creative Destruction Lab, bridging academic research and applied aerospace work. Jonathan contributes to the widely used Open Motion Planning Library (OMPL), adding deterministic random-seed control and richer path cost and comparison operations that improve reproducibility and optimization in planning algorithms. His background blends mechanical engineering fundamentals with advanced robotics and probabilistic planning, enabling both theoretical advances and practical software improvements. Colleagues would note his uncommon mix of deep algorithmic focus and hands-on open-source engineering that surfaces in production-grade research tools.
11 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Autonomous Space Robotics Lab, Doctor of Philosophy (Ph.D.), Autonomous Space Robotics Lab at University of TorontoBachelor's Degree, Mechanical Engineering with an option in Physics, Bachelor's Degree, Mechanical Engineering with an option in Physics at University of Waterloo
French, English
Github Skills (9)
robotics10
c-language10
cprogramming-language10
motion-planning10
data-structure9
algorithm9
data-structures9
algorithms9
software-design8
Programming languages (6)
Github contributions (5)
ompl/ompl
Nov 2014 - May 2020
The Open Motion Planning Library (OMPL)
Role in this project:
Back-end Developer
Back-end DeveloperContributions:9 reviews, 86 commits, 32 PRs in 5 years 7 months
Contributions summary:Jonathan primarily focused on enhancing the Open Motion Planning Library (OMPL) with features related to random number generation and optimization. They added functionality to set and get random number generator seeds, crucial for deterministic behavior in motion planning algorithms. Further contributions included the ability to calculate the cost of a path within an optimization objective, allowing for more complex path evaluation, and adding a full suite of comparison operators for the cost.
motion-planningomplrobotics
utiasASRL/batch-informed-trees
Nov 2014 - Feb 2018
A fork of The Open Motion Planning Library (OMPL) to include Batch Informed Trees (BIT*)
Contributions:84 commits, 167 PRs, 285 pushes in 3 years 3 months
motion-planningompl
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