Accurately matching the LIDAR scans is a critical step for an Autonomous Land Vehicle (ALV). Whilst most previous works have focused on the urban environment, this paper focuses on the off-road environment. Due to the lack of a publicly available dat...
Autonomous exploration of autonomous vehicles in off-road environments remains challenging due to the adverse impact on exploration efficiency and safety caused by uneven terrain. In this paper, we propose a path planning framework for autonomous exp...
As with the broad field of deep learning, autonomy is a research topic that has experienced a heavy explosion in attention from both the scientific and commercial industries due to its potential for the advancement of humanity in many cross-cutting d...
This article presents the essential abilities and limitations of various sensors used for object recognition in the operation environment of unmanned ground vehicles (UGVs). The use of autonomous and unmanned vehicles for reconnaissance and logistics...
Uneven terrains and complex obstacles in off-road environments present significant challenges to the stability and safety of vehicle path planning. This paper presents a hierarchical DRL-DWA path planning framework for unmanned ground vehicles (UGVs)...
For autonomous vehicles driving in off-road environments, it is crucial to have a sensitive environmental perception ability. However, semantic segmentation in complex scenes remains a challenging task. Most current methods for off-road environments...
Path planning for an unmanned vehicle in an off-road uncertain environment is important for navigation safety and efficiency. Regarding this, a global improved A* algorithm is presented. Firstly, based on remote sensing images, the artificial potenti...
In this study, we propose a knowledge distillation (KD) method for segmenting off-road environment range images. Unlike urban environments, off-road terrains are irregular and pose a higher risk to hardware. Therefore, off-road self-driving systems a...
In recent years, autonomous driving technology has made remarkable progress in urban transportation and logistics, while its application in complex off-road environments has gradually become a research hotspot. Compared to traditional manned vehicles...
Most existing path-planning algorithms are applied in either trafficable environments or non-trafficable environments. Off-road vehicles (ORVs) are often faced with a mix of trafficable and non-trafficable environments. Therefore, trafficability shou...
When it comes to some essential abilities of autonomous ground vehicles (AGV), detection is one of them. In order to safely navigate through any known or unknown environment, AGV must be able to detect important elements on the path. Detection is app...
This study proposes a method for detecting and tracking traversable regions in off-road conditions for unmanned ground vehicles (UGVs). Off-road conditions, such as rough terrain or fields, present significant challenges for UGV navigation, and detec...
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 mi...
Navigating uneven, unstructured terrain with dynamic obstacles remains a challenge for autonomous mobile robots. This article introduces Dynamic Unstructured Environment (DUnE) for evaluating the performance of off-road navigation systems in simulati...
Aiming at the influence of fewer feature points and dynamic obstacles on location and mapping in off-road environments, we propose a dual-constraint LiDAR-based Simultaneous Localization and Mapping (SLAM) scheme. By abstracting LiDAR registration in...
Machine learning techniques have accelerated the development of autonomous navigation algorithms in recent years, especially algorithms for on-road autonomous navigation. However, off-road navigation in unstructured environments continues to challeng...
Off-road path planning involves navigating vehicles through areas lacking established road networks, which is critical for emergency response in disaster events, but is limited by the complex geographical environments in natural conditions. How to mo...
Digital models are the foundation of digital twins, which form the basis of autonomous off-road vehicles. Developing virtual models of off-road vehicles using dynamic reduction techniques is one of several approaches. The article commences with a com...
Since the state-of-the-art deep learning algorithms demand a large training dataset, which is often unavailable in some domains, the transfer of knowledge from one domain to another has been a trending technique in the computer vision field. However,...
Due to the global population increase and the recovery of agricultural demand after the COVID-19 pandemic, the importance of agricultural automation and autonomous agricultural vehicles is growing. Fallen person detection is critical to preventing fa...
This paper proposes a framework for predicting traversability probability in off-road environments by distilling knowledge from large language models (LLMs) such as GPT-4o into lightweight models. The GPT-4off approach utilizes GPT-generated data to...
In field driving activities, off-road areas usually lack existing paths that can be directly driven on by ground vehicles, but their surface environments can still satisfy the planning and passage requirements of some off-road vehicles. Additionally,...
Unstructured off-road environments with complex terrain obstacles and pavement properties bring obvious challenges for special purpose autonomous vehicle control. A cascade direct yaw moment control strategy (CDYC), which contains a main loop and a s...
Motion drive algorithms are a set of filters designed to simulate realistic motion and are an integral part of contemporary vehicle simulators. This paper presents the design of a novel intelligent interval type 2 fuzzy adaptive motion drive algorith...
One of the key issues in vehicle sustainability is their energy efficiency. The article concerns the complex issue of predicting the tractive efficiency of heavy off-road vehicles depending on the parameters of the transfer case. As part of the resea...
23 November 2023
In this paper, we present a comparative study of modern semantic segmentation loss functions and their resultant impact when applied with state-of-the-art off-road datasets. Class imbalance, inherent in these datasets, presents a significant challeng...
In off-road environments, the lateral rollover stability of articulated unmanned rollers (URs) is critical to ensure operational safety and efficiency. This paper introduces the concept of a rollover energy barrier (REB), a symmetry-based metric that...
Despite significant advancements in roll stability control for individual vehicle types, comparative research across on-road and off-road vehicles remains limited, hindering cross-disciplinary innovation. This study bridges this gap by systematically...
Heavy duty off-road vehicle (HDOR) duty cycles are very different than passenger car cycles. HDOR duty cycles are characterized by heavy transient loads and highly varying constant power demands. Straddle carriers and mine loaders can operate 20 hour...
14 December 2022
This paper addresses the problem of adapting a control system to unseen conditions, specifically to the problem of trajectory tracking in off-road conditions. Three different approaches are considered and compared for this comparative study: The firs...
In order to deal with the threat of forest fire to the original forest resources, improve the efficiency of firefighting, and better protect the forest ecological resources in the original forest area. In this paper, the ant colony algorithm is used...
This paper introduces a novel lateral guidance strategy for autonomous ground vehicles operating in deformable environments. The strategy combines a geometric algorithm with a dynamic controller to leverage the advantages of both methods. The geometr...
The mobile laser scanning (MLS) technique has attracted considerable attention for providing high-density, high-accuracy, unstructured, three-dimensional (3D) geo-referenced point-cloud coverage of the road environment. Recently, there has been an in...
With the increasingly stringent regulations on air quality and the consequent emission limits for internal combustion engines, researchers are concentrating on studying new solutions for improving efficiency and energy saving even in off-road mobile...
As off-road vehicles, in addition to field transportation, another vital function of agricultural tractors is to provide power for field machinery. Therefore, the dynamic performance of the power take-off (PTO) driveline directly affects the field re...
This paper presents the use of deep Reinforcement Learning (RL) for autonomous navigation of an Unmanned Ground Vehicle (UGV) with an onboard three-dimensional (3D) Light Detection and Ranging (LiDAR) sensor in off-road environments. For training, bo...
Pathfinding for autonomous vehicles in large-scale complex terrain environments is difficult when aiming to balance efficiency and quality. To solve the problem, this paper proposes Hierarchical Path-Finding A* based on Multi-Scale Rectangle, called...
Perception and vehicle control remain major challenges in the autonomous driving domain. To find a proper system configuration, thorough testing is needed. Recent advances in graphics and physics simulation allow researchers to build highly realistic...
For over a decade, urban road environment detection has been a target of intensive research. The topic is relevant for the design and implementation of advanced driver assistance systems. Typically, embedded systems are deployed in these for the oper...
In the face of increasing demands for tasks such as mountain rescue, geological exploration, and military operations in complex wilderness environments, planning an efficient walking route is crucial. To address the inefficiency of traditional two-di...
Topography estimation is essential for autonomous off-road navigation. Common methods rely on point cloud data from, e.g., Light Detection and Ranging sensors (LIDARs) and stereo cameras. Stereo cameras produce dense point clouds with larger coverage...
Road maintenance management aims to satisfy quality, comfort, and safety requirements for the various assets. To overcome delays and barriers in the widespread adoption of road management systems, the Building Information Modeling (BIM) approach may...
Road networks are important for modelling the urban geographic environment. It is necessary to determine the spatial relationships of road intersections when using maps to help researchers conduct virtual urban geographic experiments (because a road...
A novel approach to evaluating night-time road and street environment lighting conditions through 3D point clouds is presented. The combination of luminance imaging and 3D point cloud acquired with a terrestrial laser scanner was used for analyzing 3...
Object detection is an essential task for autonomous vehicles to ensure safety. Due to the complexity of mining environments, it is difficult to detect objects accurately and robustly. To address these issues, this paper proposes a novel 3D LiDAR-bas...
With the rapid advancement of technologies related to unmanned ground systems, ground vehicles are being widely deployed across various domains. However, when operating in complex, soft terrain environments, the low bearing capacity of such terrains...
Road vehicle lateral positioning is a key aspect of many assistance applications and autonomous driving. However, conventional GNSS-based positioning systems and fusion with inertial systems are not able to achieve these levels of accuracy under real...
The article presents the application of a hyperspectral camera in mobile robot navigation. Hyperspectral cameras are imaging systems that can capture a wide range of electromagnetic spectra. This feature allows them to detect a broader range of color...
The detection of drivable areas in off-road scenes is a challenging problem due to the presence of unstructured class boundaries, irregular features, and dust noise. Three-dimensional LiDAR data can effectively describe the terrain features, and a bi...
In this paper, we present a novel method for visual-inertial odometry for land vehicles. Our technique is robust to unintended, but unavoidable bumps, encountered when an off-road land vehicle traverses over potholes, speed-bumps or general change in...
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