Citation: Hua, C.; Niu, R.; Yu, B.;
Zheng, X.; Bai, R.; Zhang, S. A Global
Path Planning Method for
Unmanned Ground Vehicles in
Off-Road Environments Based on
Mobility Prediction. Machines 2022,
10, 375. https://doi.org/10.3390/
machines10050375
Academic Editor: Luis Payá
Received: 15 April 2022
Accepted: 12 May 2022
Published: 16 May 2022
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Article
A Global Path Planning Method for Unmanned Ground Vehicles
in Off-Road Environments Based on Mobility Prediction
Chen Hua
1,2
, Runxin Niu
1
, Biao Yu
1,
* , Xiaokun Zheng
1,2
, Rengui Bai
1,2
and Song Zhang
1,2
1
Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China;
ba20168187@mail.ustc.edu.cn (C.H.); rxniu@iim.ac.cn (R.N.); zhengkun@mail.ustc.edu.cn (X.Z.);
zaowuzhu@mail.ustc.edu.cn (R.B.); sz1997@mail.ustc.edu.cn (S.Z.)
2
Science Island Branch, University of Science and Technology of China, Hefei 230026, China
* Correspondence: byu@hfcas.ac.cn
Abstract:
In a complex off-road environment, due to the low bearing capacity of the soil and the
uneven features of the terrain, generating a safe and effective global route for unmanned ground
vehicles (UGVs) is critical for the success of their motion and mission. Most traditional global path
planning methods simply take the shortest path length as the optimization objective, which makes it
difficult to plan a feasible and safe route in complex off-road environments. To address this problem,
this research proposes a global path planning method, which considers the influence of terrain
factors and soil mechanics on UGV mobility. First, we established a high-resolution
3D terrain
model
with remote sensing elevation terrain data, land use and soil type distribution data, based on a
geostatistical method. Second, we analyzed the vehicle mobility by the terramechanical method
(i.e., vehicle cone index and Bakker’s theory), and then calculated the mobility cost based on a fuzzy
inference method. Finally, based on the calculated mobility cost, the probabilistic roadmap method
was used to establish the connected matrix and the multi-dimensional traffic cost evaluation matrix
among the sampling nodes, and then an improved A* algorithm was proposed to generate the
global route.
Keywords: soil terrain; vehicle mobility; terramechanics; fuzzy inference; route generation
1. Introduction
Due to their unnecessary requirement of human interference and their ability to replace
human operations in risky environments, unmanned ground vehicles (UGVs) have several
applications, such as disaster rescue, nuclear inspection, planet exploration and military
operations, etc. In order to enable a UGV to traverse complex off-road environments quickly
and safely and achieve its missions, an effective global path planning method is required,
of which the key objective is to generate a safe and feasible route from the initial position to
the specified targets based on given off-road environmental map information [1,2].
In recent decades, path planning methods developed rapidly, resulting in numerous
new such methods [
3
]. Path planning methods can be mainly divided into two categories,
i.e., graph search-based and sampling-based [
3
]. Typical graph search-based methods
include Dijkstra, A*, D*, field D* and several other variants [
4
–
7
]. The graph search-
based path planning methods can find the optimal path accurately, but such algorithms
should traverse the surrounding of the current node and select the minimum cost node,
which leads to large computational efforts. To reduce the calculation time the algorithm
takes to search each interval, an efficient A* algorithm was proposed [
8
]. Guruji et al. [
9
]
proposed an online path planning method for a mobile robot in a grid-map environment,
using a modified real time A* algorithm. In obstacle-avoidance path planning, Duan et al.
introduced a safety–cost matrix in the A* algorithm and the heuristic function was modified
to ensure the optimal safety path in different environments [10].
Machines 2022, 10, 375. https://doi.org/10.3390/machines10050375 https://www.mdpi.com/journal/machines
