CAHSOR: Competence-Aware High-Speed Off-Road Ground Navigation in $\mathbb {SE}(3)$

While the workspace of traditional ground vehicles is usually assumed to be in a 2D plane, i.e., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbb {SE}(2)$</tex-math></inline-formula>, such an assumption may not hold when they drive at high speeds on unstructured off-road terrain: High-speed sharp turns on high-friction surfaces may lead to vehicle rollover; Turning aggressively on loose gravel or grass may violate the non-holonomic constraint and cause significant lateral sliding; Driving quickly on rugged terrain will produce extensive vibration along the vertical axis. Therefore, most offroad vehicles are currently limited to driving only at low speeds to assure vehicle stability and safety. In this letter, we aim at empowering high-speed off-road vehicles with competence awareness in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbb {SE}(3)$</tex-math></inline-formula> so that they can reason about the consequences of taking aggressive maneuvers on different terrain with a 6-DoF forward kinodynamic model. The kinodynamic model is learned from visual, speed, and inertial Terrain Representation for Off-road Navigation (<sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tron</small>) using multimodal, self-supervised vehicle-terrain interactions. We demonstrate the efficacy of our Competence-Aware High-Speed Off-Road (<sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cahsor</small>) navigation approach on a physical ground robot in both autonomous navigation and a human shared-control setup and show that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cahsor</small> can efficiently reduce vehicle instability by 62% while only compromising 8.6% average speed with the help of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tron</small>.

Authors

Institutions

Publication Details

Journal
IEEE Robotics and Automation Letters
Published
2024-09-10
DOI
https://doi.org/10.1109/lra.2024.3457369
Citations
14
Primary Topic
Natural Language Processing Techniques
Type
article
Field-Weighted Citation Impact
4.86
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

CAHSOR: Competence-Aware High-Speed Off-Road Ground Navigation in $\mathbb {SE}(3)$

Aniket Datar, Xuesu Xiao, Mohammad Nazeri, Anuj Pokhrel
14 citations
IEEE Robotics and Automation Letters
Natural Language Processing Techniques
4.86
article

CAHSOR: Competence-Aware High-Speed Off-Road Ground Navigation in $\mathbb {SE}(3)$

Aniket Datar, Xuesu Xiao, Mohammad Nazeri, Anuj Pokhrel
article en
14 citations

Abstract

While the workspace of traditional ground vehicles is usually assumed to be in a 2D plane, i.e., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbb {SE}(2)$</tex-math></inline-formula>, such an assumption may not hold when they drive at high speeds on unstructured off-road terrain: High-speed sharp turns on high-friction surfaces may lead to vehicle rollover; Turning aggressively on loose gravel or grass may violate the non-holonomic constraint and cause significant lateral sliding; Driving quickly on rugged terrain will produce extensive vibration along the vertical axis. Therefore, most offroad vehicles are currently limited to driving only at low speeds to assure vehicle stability and safety. In this letter, we aim at empowering high-speed off-road vehicles with competence awareness in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbb {SE}(3)$</tex-math></inline-formula> so that they can reason about the consequences of taking aggressive maneuvers on different terrain with a 6-DoF forward kinodynamic model. The kinodynamic model is learned from visual, speed, and inertial Terrain Representation for Off-road Navigation (<sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tron</small>) using multimodal, self-supervised vehicle-terrain interactions. We demonstrate the efficacy of our Competence-Aware High-Speed Off-Road (<sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cahsor</small>) navigation approach on a physical ground robot in both autonomous navigation and a human shared-control setup and show that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cahsor</small> can efficiently reduce vehicle instability by 62% while only compromising 8.6% average speed with the help of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tron</small>.

IEEE Robotics and Automation LettersVol. 9(11)
George Mason University (US)
Openalex Percentile: Top 4%
Natural Language Processing Techniques
4.86
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.