ON TRAVERSABILITY COST EVALUATION FROM PROPRIOCEPTIVE SENSING FOR A CRAWLING ROBOT

Traversability characteristics of the robot working environment are crucial in planning an efficient path for a robot operating in rough unstructured areas. In the literature, approaches to wheeled or tracked robots can be found, but a relatively little attention is given to walking multi-legged robots. Moreover, the existing approaches for terrain traversability assessment seem to be focused on gathering key features from a terrain model acquired from range data or camera image and only occasionally supplemented with proprioceptive sensing that expresses the interaction of the robot with the terrain. This paper addresses the problem of traversability cost evaluation based on proprioceptive sensing for a hexapod walking robot while optimizing different criteria. We present several methods of evaluating the robot-terrain interaction that can be used as a cost function for an assessment of the robot motion that can be utilized in high-level path-planning algorithms

Stereo vision-based obstacle avoidance for micro air vehicles using an egocylindrical image space representation

Micro air vehicles which operate autonomously at low altitude in cluttered environments require a method for onboard obstacle avoidance for safe operation. Previous methods deploy either purely reactive approaches, mapping low-level visual features directly to actuator inputs to maneuver the vehicle around the obstacle, or deliberative methods that use on-board 3-D sensors to create a 3-D, voxel-based world model, which is then used to generate collision free 3-D trajectories. In this paper, we use forward-looking stereo vision with a large horizontal and vertical field of view and project range from stereo into a novel robot-centered, cylindrical, inverse range map we call an egocylinder. With this implementation we reduce the complexity of our world representation from a 3D map to a 2.5D image-space representation, which supports very efficient motion planning and collision checking, and allows to implement configuration space expansion as an image processing function directly on the egocylinder. Deploying a fast reactive motion planner directly on the configuration space expanded egocylinder image, we demonstrate the effectiveness of this new approach experimentally in an indoor environment

Online sensor information and redundancy resolution based obstacle avoidance for high DOF mobile manipulator teleoperation

High degrees of freedom (DOF) mobile manipulators provide more flexibility than conventional manipulators. They also provide manipulation operations with a mobility capacity and have potential in many applications. However, due to high redundancy, planning and control become more complicated and difficult, especially when obstacles occur. Most existing obstacle avoidance methods are based on off-line algorithms and most of them mainly focus on planning a new collision-free path, which is not appropriate for some applications, such as teleoperation and uses many system resources as well. Therefore, this paper presents an online planning and control method for obstacle avoidance in mobile manipulators using online sensor information and redundancy resolution. An obstacle contour reconstruction approach employing a mobile manipulator equipped with an active laser scanner system is also introduced in this paper. This method is implemented using a mobile manipulator with a seven-DOF manipulator and a four-wheel drive mobile base. The experimental results demonstrate the effectiveness of this method. © 2013 Zhang et al.; licensee InTech.Link_to_subscribed_fulltex

Terrain Classification from Body-mounted Cameras during Human Locomotion

Abstract—This paper presents a novel algorithm for terrain type classification based on monocular video captured from the viewpoint of human locomotion. A texture-based algorithm is developed to classify the path ahead into multiple groups that can be used to support terrain classification. Gait is taken into account in two ways. Firstly, for key frame selection, when regions with homogeneous texture characteristics are updated, the fre-quency variations of the textured surface are analysed and used to adaptively define filter coefficients. Secondly, it is incorporated in the parameter estimation process where probabilities of path consistency are employed to improve terrain-type estimation. When tested with multiple classes that directly affect mobility a hard surface, a soft surface and an unwalkable area- our proposed method outperforms existing methods by up to 16%, and also provides improved robustness. Index Terms—texture, classification, recursive filter, terrain classification I

Optimum Pipeline for Visual Terrain Classification Using Improved Bag of Visual Words and Fusion Methods

We propose an optimum pipeline and develop the hybrid representation to produce an effective and efficient visual terrain classification system. The bag of visual words (BOVW) framework has emerged as a promising approach and effective paradigm for visual terrain classification. The method includes four main steps: (1) feature extraction, (2) codebook generation, (3) feature coding, and (4) pooling and normalization. Recent researches have primarily focused on feature extraction in the development of new handcrafted descriptors that are specific to the visual terrain. However, the effects of other steps on visual terrain classification are still unknown. At the same time, fusion methods are often used to boost classification performance by exploring the complementarity of diverse features. We provide a comprehensive study of all steps in the BOVW framework and different fusion methods for visual terrain classification. Then, multiple approaches in each step and their effects are explored on the visual terrain dataset. Finally, the feature preprocessing technique, improved BOVW framework, and fusion method are used to construct an optimum pipeline for visual terrain classification. The hybrid representation developed by the optimum pipeline performs effectively and rapidly for visual terrain classification in the terrain dataset, outperforming those current methods. Furthermore, it is robust to diverse noises and illumination alterations

Traversability analysis in unstructured forested terrains for off-road autonomy using LIDAR data

Scene perception and traversability analysis are real challenges for autonomous driving systems. In the context of off-road autonomy, there are additional challenges due to the unstructured environments and the existence of various vegetation types. It is necessary for the Autonomous Ground Vehicles (AGVs) to be able to identify obstacles and load-bearing surfaces in the terrain to ensure a safe navigation (McDaniel et al. 2012). The presence of vegetation in off-road autonomy applications presents unique challenges for scene understanding: 1) understory vegetation makes it difficult to detect obstacles or to identify load-bearing surfaces; and 2) trees are usually regarded as obstacles even though only trunks of the trees pose collision risk in navigation. The overarching goal of this dissertation was to study traversability analysis in unstructured forested terrains for off-road autonomy using LIDAR data. More specifically, to address the aforementioned challenges, this dissertation studied the impacts of the understory vegetation density on the solid obstacle detection performance of the off-road autonomous systems. By leveraging a physics-based autonomous driving simulator, a classification-based machine learning framework was proposed for obstacle detection based on point cloud data captured by LIDAR. Features were extracted based on a cumulative approach meaning that information related to each feature was updated at each timeframe when new data was collected by LIDAR. It was concluded that the increase in the density of understory vegetation adversely affected the classification performance in correctly detecting solid obstacles. Additionally, a regression-based framework was proposed for estimating the understory vegetation density for safe path planning purposes according to which the traversabilty risk level was regarded as a function of estimated density. Thus, the denser the predicted density of an area, the higher the risk of collision if the AGV traversed through that area. Finally, for the trees in the terrain, the dissertation investigated statistical features that can be used in machine learning algorithms to differentiate trees from solid obstacles in the context of forested off-road scenes. Using the proposed extracted features, the classification algorithm was able to generate high precision results for differentiating trees from solid obstacles. Such differentiation can result in more optimized path planning in off-road applications

Autonomous Soil Assessment System for Planetary Rovers

Planetary rovers face mobility hazards associated with various classes of terrains they traverse, and hence it is desirable to enable remote prediction of terrain trafficability (ability to traverse) properties. For that reason, the development of algorithms for assessing terrain type and mobility properties, as well as for coupling these data in an online learning framework, represent important capabilities for next-generation rovers. This work focuses mainly on 3-way terrain classification (classifying as one of the types: Sand, Bedrock and Gravel) as well as on the correlation of terrain types and their mobility properties in a framework that enables online learning. For terrain classification, visual descriptors are developed, which are primarily based on visual texture and are captured in form of histograms of edge filter responses at various scales and orientations. The descriptors investigated in this work are HOG (Histogram of Oriented Gradients), GIST, MR8 (Maximum Response) Textons and the classification techniques implemented here are nearest and k-nearest neighbors. Further, monochrome image intensity is used as an additional feature to further distinguish bedrock from the other terrain types. No major differences in performance are observed between the three descriptors, leading to the adoption of the HOG approach due to its lower computational complexity (over 3 orders of magnitude difference in complexity between HOG and Textons) and thus higher applicability to planetary missions. Tests demonstrate an accuracy between 70% and 93% (81% average) for the classification using the HOG descriptor, on images taken by NASA’s Mars rovers. To predict terrain trafficability ahead of the rover, exteroceptive data namely terrain type and slope, are correlated with the trafficability metrics namely slip, sinkage and roughness, in a learning framework. A queue based data structure has been implemented for the correlation, which keeps discarding the older data so as to avoid diminishing the effect of newer data samples, when there is a large amount of data. This also ensures that the rover will be able to adapt to changing terrains responses and predict the risk level (low, medium or high) accordingly. Finally, all the algorithms developed in this work were tested and verified in a field test demo at the CSA (Canadian Space Agency) mars yard. The risk metric in combination with the queue based data structure, can achieve stable predictions in consistent terrains, while also being responsive to sudden changes in terrain trafficability