Dom, cars don’t fly!—Or do they? In-Air Vehicle Maneuver for High-Speed Off-Road Navigation

When pushing the speed limit for aggressive off-road navigation on uneven terrain, it is inevitable that vehicles may become airborne from time to time. During time-sensitive tasks, being able to fly over challenging terrain can also save time, instead of cautiously circumventing or slowly negotiating through. However, most off-road autonomy systems operate under the assumption that the vehicles are always on the ground and therefore limit operational speed. In this paper, we present a novel approach for in-air vehicle maneuver during high-speed off-road navigation. Based on a hybrid forward kinodynamic model using both physics principles and machine learning, our fixed-horizon, sampling-based motion planner ensures accurate vehicle landing poses and their derivatives within a short airborne time window using vehicle throttle and steering commands. We test our approach in extensive in-air experiments both indoors and outdoors, compare it against an error-driven control method, and demonstrate that precise and timely in-air vehicle maneuver is possible through existing ground vehicle controls.

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Publication Details

Published
2025-10-19
DOI
https://doi.org/10.1109/iros60139.2025.11246862
Citations
1
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
1.28
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article

Dom, cars don’t fly!—Or do they? In-Air Vehicle Maneuver for High-Speed Off-Road Navigation

Aniket Datar, Xuesu Xiao, Anuj Pokhrel
1 citations
Robotic Path Planning Algorithms
1.28
article

Dom, cars don’t fly!—Or do they? In-Air Vehicle Maneuver for High-Speed Off-Road Navigation

Aniket Datar, Xuesu Xiao, Anuj Pokhrel
article en
1 citations

Abstract

When pushing the speed limit for aggressive off-road navigation on uneven terrain, it is inevitable that vehicles may become airborne from time to time. During time-sensitive tasks, being able to fly over challenging terrain can also save time, instead of cautiously circumventing or slowly negotiating through. However, most off-road autonomy systems operate under the assumption that the vehicles are always on the ground and therefore limit operational speed. In this paper, we present a novel approach for in-air vehicle maneuver during high-speed off-road navigation. Based on a hybrid forward kinodynamic model using both physics principles and machine learning, our fixed-horizon, sampling-based motion planner ensures accurate vehicle landing poses and their derivatives within a short airborne time window using vehicle throttle and steering commands. We test our approach in extensive in-air experiments both indoors and outdoors, compare it against an error-driven control method, and demonstrate that precise and timely in-air vehicle maneuver is possible through existing ground vehicle controls.

George Mason University (US)
Openalex Percentile: Top 14%
Robotic Path Planning Algorithms
1.28
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Dom, cars don’t fly!—Or do they? In-Air Vehicle Maneuver for High-Speed Off-Road Navigation — Aniket Datar, Xuesu Xiao, et al. · (2025) | TGRS Research Map | TGRS