Terrain Traversability Analysis Research Articles

Terrain-Shape-Adaptive Coverage Path Planning With Traversability Analysis

Coverage path planning (CPP) is in great demand with applications in agriculture, mining, manufacturing, etc. Most research in this area focused on 2D CPP problems solving the coverage problem with irregular 2D maps. Comparatively, CPP on uneven terrains is not fully solved. When there are many slopy areas in the working field, it is necessary to adjust the path shape and make it adapt to the 3D terrain surface to save energy consumption. This article proposes a terrain-shape-adaptive CPP method with three significant features. First, the paths grow by themselves according to the local terrain surface shapes. Second, the growth rule utilizes the 3D terrain traversability analysis, which makes them automatically avoid entering hazardous zones. Third, the irregularly distributed paths are connected under an optimal sequence with an improved genetic algorithm. As a result, the method can provide an autonomously growing terrain-adaptive coverage path with high energy efficiency and coverage rate compared to previous research works. It is demonstrated on various maps and is proven to be robust to terrain conditions.

A Survey on Path Planning for Autonomous Ground Vehicles in Unstructured Environments

Autonomous driving in unstructured environments is crucial for various applications, including agriculture, military, and mining. However, research in unstructured environments significantly lags behind that in structured environments, mainly due to the challenges posed by harsh environmental conditions and the intricate interactions between vehicles and terrains. This article first categorizes unstructured path planning into hierarchical and end-to-end approaches and then the special parts compared to structured path planning are emphatically reviewed, such as terrain traversability analysis, cost estimation, and terrain-dependent constraints. This article offers a comprehensive review of the relevant factors, vehicle–terrain interactions, and methods of terrain traversability analysis. The estimation methods of safety cost, energy cost, and comfort cost are also emphatically summarized. Moreover, the constraints caused by the limits of terrains and vehicles are discussed. The applications of algorithms in recent articles for path planners are reviewed. Finally, crucial areas requiring further research are analyzed in unstructured path planning.

TTA-GEP: Terrain Traversability Analysis with Geometry and Environmental Perception for the Path Planning of Planetary Rovers

Terrain traversability analysis (TTA), the key to the navigation of planetary rovers, is significant to the safety of the rover. Therefore, owing to its complexity, the Martian terrain is worth analysing comprehensively based on the terrain variability and hazard level. In this work, we propose a novel method for terrain traversability analysis for the path planning of planetary rovers by integrating Martian terrain geometry features with terrain semantic information, which includes geometry and environmental perception (GEP). Specifically, we deploy semantic segmentation to classify common terrain types, such as rocks, bedrocks, and sand, obtaining semantic information as one part of terrain traversability analysis at the same time. Simultaneously, the point cloud is generated by using binocular images from the planetary rover navigation camera (Navcam) to construct a 2.5D elevation map of the environment to analyse the geometric characteristics of the terrain. Besides, we implement path planning based on the results of TTA-GEP. Overall, our proposed method improves the performance of the terrain traversability analysis and reduces the risk of planetary rovers while detecting in an unstructured environment.

End-to-End Learning for Visual Navigation of Forest Environments

Off-road navigation in forest environments is a challenging problem in field robotics. Rovers are required to infer their traversability over a priori unknown and dynamically changing forest terrain using noisy onboard navigation sensors. The problem is compounded for small-sized rovers, such as that of a swarm. Their size-proportional low-viewpoint affords them a restricted view for navigation, which may be partially occluded by forest vegetation. Hand-crafted features, typically employed for terrain traversability analysis, are often brittle and may fail to discriminate obstacles in varying lighting and weather conditions. We design a low-cost navigation system tailored for small-sized forest rovers using self-learned features. The MobileNet-V1 and MobileNet-V2 models, trained following an end-to-end learning approach, are deployed to steer a mobile platform, with a human-in-the-loop, towards traversable paths while avoiding obstacles. Receiving a 128 × 96 pixel RGB image from a monocular camera as input, the algorithm running on a Raspberry Pi 4, exhibited robustness to motion blur, low lighting, shadows and high-contrast lighting conditions. It was able to successfully navigate a total of over 3 km of real-world forest terrain comprising shrubs, dense bushes, tall grass, fallen branches, fallen tree trunks, and standing trees, in over five different weather conditions and four different times of day.

A Survey on Terrain Traversability Analysis for Autonomous Ground Vehicles: Methods, Sensors, and Challenges

Understanding the terrain in the upcoming path of a ground robot is one of the most challenging problems in field robotics. Terrain and traversability analysis is a multidisciplinary field combining robotics with image and signal processing, feature extraction, machine learning, three-dimensional (3D) mapping, and 3D geometry. Application scenarios range from autonomous vehicles on urban networks to agriculture, defence, exploration, mining, and search and rescue. Given the broad set of techniques available and the fast progress in this area, in this paper we organize and survey the corresponding literature, define unambiguous key terms, and discuss links among fundamental building blocks ranging from terrain classification to traversability regression. The advantages and the drawbacks of the methods are critically discussed, providing a comprehensive coverage of key aspects, including open code, available datasets for experimentation and comparisons, and important open research issues.

ODS-Bot: Mobile Robot Navigation for Outdoor Delivery Services

Autonomous mobile robots have been used in outdoor delivery services. Delivery robots have to cope with dynamic obstacles and various environmental conditions. Although several successful technological solutions are available for indoor applications, there are still plenty of unsolved problems in outdoor environments. In this study, we concentrate on three technological challenges hindering the development of campus delivery robots. The first challenge is robust localization in various dynamic outdoor environments. Localization results obtained using lidars and Global Navigation Satellite System (GNSS) sensors show complementary advantages and disadvantages. The proposed localization strategy efficiently combines information from multiple sensors. The second challenge is safe navigation based on the detection of the traversable region. The presented terrain traversability analysis provides clear, real-time positive and negative obstacle information. The third challenge is the requirement of an effective path planning strategy to reduce the risk of collisions or deadlocks. We collected the appearance history of regional dynamic obstacles in the map. The resultant path was carefully generated to avoid crowded areas. Experimental verifications have been successfully conducted on the Korea University campus. The presented results clearly indicate that the proposed methods are essential to achieve safe and reliable navigation in dynamic, real-world environments.

A terrain description method for traversability analysis based on elevation grid map

Terrain traversability analysis is a challenging problem for mobile robots to adapt to complex environments, including the detection of cluttered obstacles, potholes, or even slopes. With the accurate distance information, using distance sensors such as three-dimensional light detection and ranging (LiDAR) for terrain description becomes a preferred choice. In this article, a terrain description method for traversability analysis based on elevation grid map is presented. After the elevation grid map is generated, the ground is segmented with the aid of a height difference kernel and the non-ground grids in the map are then clustered. The terrain description features, including height index, roughness, and slope angle, are calculated and estimated. The slope angle is estimated using random sample consensus (RANSAC) and least squares method, and specifically, the roughness is combined to eliminate false slopes. Experimental results verified the effectiveness of the proposed method.

移動ロボットの性能に応じた走行可能性推定が可能な不整地に対する走行可能性推定および行動生成手法

This paper describes a novel rough terrain traversability analysis and behavior generation method for mobile robot navigation. We focused on the scenario of mobile robot operation in complex environment such as disaster sites. The proposed method enables mobile robots to analyze terrain traversability of surrounding area and select safe course direction. According to the selected course direction, appropriate control input is calculated and mobile robot moves following the calculated control input. Terrain traversability analysis and course direction judgment are realized based on feature value calculation of terrain, fuzzy inference and vector field histogram. Mobile robot's behavior generation is realized based on control method which takes advantage of reference posture tracking. The experimental results show that the proposed method can navigate mobile robot successfully to the target location in complex environment including terrain area.

3D traversability awareness for rough terrain mobile robots

Purpose – This research aims to address the issue of safe navigation for autonomous vehicles in highly challenging outdoor environments. Indeed, robust navigation of autonomous mobile robots over long distances requires advanced perception means for terrain traversability assessment. Design/methodology/approach – The use of visual systems may represent an efficient solution. This paper discusses recent findings in terrain traversability analysis from RGB-D images. In this context, the concept of point as described only by its Cartesian coordinates is reinterpreted in terms of local description. As a result, a novel descriptor for inferring the traversability of a terrain through its 3D representation, referred to as the unevenness point descriptor (UPD), is conceived. This descriptor features robustness and simplicity. Findings – The UPD-based algorithm shows robust terrain perception capabilities in both indoor and outdoor environment. The algorithm is able to detect obstacles and terrain irregularities. The system performance is validated in field experiments in both indoor and outdoor environments. Research limitations/implications – The UPD enhances the interpretation of 3D scene to improve the ambient awareness of unmanned vehicles. The larger implications of this method reside in its applicability for path planning purposes. Originality/value – This paper describes a visual algorithm for traversability assessment based on normal vectors analysis. The algorithm is simple and efficient providing fast real-time implementation, since the UPD does not require any data processing or previously generated digital elevation map to classify the scene. Moreover, it defines a local descriptor, which can be of general value for segmentation purposes of 3D point clouds and allows the underlining geometric pattern associated with each single 3D point to be fully captured and difficult scenarios to be correctly handled.

Terrain traversability analysis methods for unmanned ground vehicles: A survey

Motion planning for unmanned ground vehicles (UGV) constitutes a domain of research where several disciplines meet, ranging from artificial intelligence and machine learning to robot perception and computer vision. In view of the plurality of related applications such as planetary exploration, search and rescue, agriculture, mining and off-road exploration, the aim of the present survey is to review the field of 3D terrain traversability analysis that is employed at a preceding stage as a means to effectively and efficiently guide the task of motion planning. We identify that in the epicenter of all related methodologies, 3D terrain information is used which is acquired from LIDAR, stereo range data, color or other sensory data and occasionally combined with static or dynamic vehicle models expressing the interaction of the vehicle with the terrain. By taxonomizing the various directions that have been explored in terrain perception and analysis, this review takes a step toward agglomerating the dispersed contributions from individual domains by elaborating on a number of key similarities as well as differences, in order to stimulate research in addressing the open challenges and inspire future developments.