Jonathan Gammell

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University of Oxford

United Kingdom

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Recent Grants

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ACE-OPS: From Autonomy to Cognitive assistance in Emergency OPerationS

Open Date: 2019-12-01

Close Date: 2022-11-30

Articles (9)

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2025

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2024

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2024

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2024

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2023

Year:

2022

Year:

2022

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2021

Year:

2020

Collaborators (1)

Zachary Kingston

Assistant Professor

Purdue University

UNITED STATES

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Jonathan Gammell

Research Interests

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Recent Grants

Articles (9)

AORRTC: Almost-Surely Asymptotically Optimal Planning with RRT-Connect

The surface edge explorer (SEE): A measurement-direct approach to next best view planning

Multimotion visual odometry

Osprey: Multisession Autonomous Aerial Mapping With LiDAR-Based SLAM and Next Best View Planning

Event-Based Stereo Visual Odometry With Native Temporal Resolution via Continuous-Time Gaussian Process Regression

Task and Motion Informed Trees (TMIT*): Almost-Surely Asymptotically Optimal Integrated Task and Motion Planning

Adaptively Informed Trees (AIT) and Effort Informed Trees (EIT): Asymmetric bidirectional sampling-based path planning

Asymptotically Optimal Sampling-Based Motion Planning Methods

Batch Informed Trees (BIT*): Informed asymptotically optimal anytime search

Collaborators (1)

Zachary Kingston

Make your next move.

Jonathan Gammell

Research Interests

Explore related searches

Contact this professor

Recent Grants

Articles (9)

AORRTC: Almost-Surely Asymptotically Optimal Planning with RRT-Connect

The surface edge explorer (SEE): A measurement-direct approach to next best view planning

Multimotion visual odometry

Osprey: Multisession Autonomous Aerial Mapping With LiDAR-Based SLAM and Next Best View Planning

Event-Based Stereo Visual Odometry With Native Temporal Resolution via Continuous-Time Gaussian Process Regression

Task and Motion Informed Trees (TMIT*): Almost-Surely Asymptotically Optimal Integrated Task and Motion Planning

Adaptively Informed Trees (AIT*) and Effort Informed Trees (EIT*): Asymmetric bidirectional sampling-based path planning

Asymptotically Optimal Sampling-Based Motion Planning Methods

Batch Informed Trees (BIT*): Informed asymptotically optimal anytime search

Collaborators (1)

Zachary Kingston

Make your next move.

Adaptively Informed Trees (AIT) and Effort Informed Trees (EIT): Asymmetric bidirectional sampling-based path planning