Evidence map›Paper›PMID 41829432›Full record

ArticleSensors (Basel, Switzerland)2026

Global Path Planning Methods Based on the Relationship Between Traversability Capability and Terrain Matching.

Zengbin Wu, Hongchao Zhang, Zhen Zhang, Da Jiang, Shuhui Li, Yunlong Sun

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Zengbin WuSchool of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Hongchao ZhangSchool of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Zhen ZhangNorthern Vehicle Research Institute, Beijing 100072, China.ORCID 0009-0009-5540-4786
Da JiangNorthern Vehicle Research Institute, Beijing 100072, China.
Shuhui LiNorthern Vehicle Research Institute, Beijing 100072, China.
Yunlong SunNorthern Vehicle Research Institute, Beijing 100072, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In contrast to structured urban settings, road networks in post-disaster or unstructured wildland environments are often incomplete or compromised. Navigation in these contexts requires navigating complex terrains and mitigating potential hazards that impede unmanned ground vehicles (UGVs). While high-mobility off-road vehicles are specifically designed to traverse challenging features like ditches and steep slopes, traditional path planning algorithms often fail to exploit these capabilities. These algorithms typically suffer from a binary focus, either relying strictly on road networks or ignoring them altogether, thereby neglecting the synergy between infrastructure and vehicle mobility. This chapter introduces a global path planning method based on traversability analysis and terrain matching to bridge this gap. The methodology incorporates a grid-based traversability evaluation, a road network expansion algorithm for densifying critical segments, and a unified planning strategy. By correlating terrain characteristics with vehicle mobility limits and optimizing the road network density, the proposed framework achieves an integrated on-road and off-road planning solution that maximizes the operational efficiency of high-mobility vehicles in degraded environments.

Indexed as

path planningroad network expansionterrain traversabilityunstructured environment

Identifiers

PMID41829432
PMCPMC12986966

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.