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This research introduces Vehicle-Conditioned Terrain Planning (VCTP), a novel algorithm that integrates vehicle heading, body geometry, and wheel contact dynamics to optimize off-road navigation paths. By sampling surface costs at wheel contacts rather than the vehicle center, VCTP reduces modeled surface costs by 39.3% in simulations while maintaining computational efficiency through selective updates. The work represents a significant advancement in robotics pathfinding logic, specifically addressing how physical vehicle states interact with terrain data to improve route selection.
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VCTP: Vehicle-Conditioned Terrain Planning for Off-Road Navigation

arXiv2 min read

arXiv:2609.17834v1 Announce Type: new Abstract: A vehicle's heading affects both the surfaces beneath its tires and its pitch and roll. We present Vehicle-Conditioned Terrain Planning (VCTP), which retains these relationships by evaluating shared elevation and surface-ID layers at eight headings. Body geometry constrains admissibility, while loaded wheel contacts determine modeled surface cost and predicted pitch and roll. Established D* Lite and vehicle-state search use these evaluations to plan routes with forward and reverse motion. When observations change, VCTP recomputes every affected body or contact query. In fully observed two-track simulations, sampling at wheel contacts rather than at the vehicle center lowers modeled surface cost by 39.3% while shortening the route. In offline planning on RGator recordings, VCTP also reduces modeled costs over identified surfaces and observed support relative to distance-focused planning, although incomplete coverage leaves full-route rankings unresolved. Selective updates match full recomputation in all 474 comparisons using recorded map changes. These results identify when wheel-contact placement and vehicle heading affect route choice.

Abstract:A vehicle's heading affects both the surfaces beneath its tires and its pitch and roll. We present Vehicle-Conditioned Terrain Planning (VCTP), which retains these relationships by evaluating shared elevation and surface-ID layers at eight headings. Body geometry constrains admissibility, while loaded wheel contacts determine modeled surface cost and predicted pitch and roll. Established D* Lite and vehicle-state search use these evaluations to plan routes with forward and reverse motion. When observations change, VCTP recomputes every affected body or contact query. In fully observed two-track simulations, sampling at wheel contacts rather than at the vehicle center lowers modeled surface cost by 39.3% while shortening the route. In offline planning on RGator recordings, VCTP also reduces modeled costs over identified surfaces and observed support relative to distance-focused planning, although incomplete coverage leaves full-route rankings unresolved. Selective updates match full recomputation in all 474 comparisons using recorded map changes. These results identify when wheel-contact placement and vehicle heading affect route choice.

8 pages, 5 figures. Submitted to the 2027 IEEE International Conference on Robotics and Automation (ICRA)

Robotics (cs.RO)

arXiv:2609.17834 [cs.RO]

(or arXiv:2609.17834v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.17834 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 267 words

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