Papers
21
Total Citations
823
H-Index
9
About
Jonathan D. Gammell is a leading researcher in robotics and autonomous systems, whose work has fundamentally advanced the field of motion planning. His primary research areas include sampling-based optimal path planning, integrated task and motion planning (TMP), and multimodal perception. Gammell is best known for developing the Batch Informed Trees (BIT*) algorithm (2015, 417 citations), a groundbreaking approach that unifies graph-based and sampling-based planning by recognizing that samples describe an implicit random geometric graph. This work, along with its successors Advanced BIT* (ABIT*) and Adaptively Informed Trees (AIT*), has established new standards for efficiency in optimal path planning, enabling robots to navigate complex environments with unprecedented speed. Beyond planning, Gammell has made significant contributions to dynamic scene understanding through Multimotion Visual Odometry (MVO) and the Oxford Multimotion Dataset, which provide tools for estimating multiple independent motions from visual data. His work on the Surface Edge Explorer (SEE) for next-best-view planning has also advanced autonomous 3D scene surveying. With over 800 total citations and a portfolio of highly influential algorithms, Gammell’s research continues to shape how robots reason about and interact with the world.
Research Focus
Key Achievements
Top Papers
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- 5The Oxford Multimotion Dataset: Multiple SE(3) Motions With Ground Truth42 citations · 2019
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- 7Into Darkness: Visual Navigation Based on a Lidar-Intensity-Image Pipeline34 citations · 2016
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