1,721,108 research outputs found
Le anomalie nel processo di audit applicato al settore agro-alimentare. Un’analisi per filiera e per norma
No full textThe conformity to the main quality standards must be periodically checked through audits during which several types of anomalies can emerge. The aim of this work was to analyze the anomalies detected during 26 audits carried out in different food companies (producing wine, dairy products, olive oil, wheat-base products, canned food, as well as catering companies). The Standards of reference were UNI EN ISO 9001:2008, UNI EN ISO 22000:2005, UNIEN ISO 22005:2008, British Retail Consortium (BRC) and International Food Standard (IIS). A total of 227 anomalies were detected. The most frequent anomalies were related to documentation, with the exception of the catering sector, where the most frequent anomalies were related to the practical activities of the production process. This was probably due to the high complexity of this type of company, which has a considerably higher offer in respect to the other food companies audited
FLane: An Adaptive Fuzzy Logic Lane Tracking System for Driver Assistance
No full textIn the last few years, driver assistance systems are increasingly being investigated in the automotive field to provide a higher degree of safety and comfort. Lane position determination plays a critical role toward the development of autonomous and computer-aided driving. This paper presents an accurate and robust method for detecting road markings with applications to autonomous vehicles and driver support. Much like other lane detection systems, ours is based on computer vision and Hough transform. The proposed approach, however, is unique in that it uses fuzzy reasoning to combine adaptively geometrical and intensity information of the scene in order to handle varying driving and environmental conditions. Since our system uses fuzzy logic operations for lane detection and tracking, we call it “FLane.” This paper also presents a method for building the initial lane model in real time, during vehicle motion, and without any a priori information. Details of the main components of the FLane system are presented along with experimental results obtained in the field under different lighting and road conditions
Rough-Terrain Mobile Robot Localization Using Stereovision
No full textMobile robots are increasingly being used in high-risk rough terrain situations, such as reconnaissance, planetary exploration, safety and rescue applications. Conventional localization algorithms are not well suited to rough terrain, since sensor drift and the dynamic effects occurring at wheel-terrain interface, such as slipping and sinkage, largely compromise their accuracy. In this paper, we follow a novel approach for 6-DoF ego-motion estimation, using stereovision. It integrates image intensity information and 3D stereo data within an Iterative Closest Point (ICP) scheme. Neither a-priori knowledge of the motion and the terrain properties nor inputs from other sensors are required, while the only assumption is that the scene always contains visually distinctive features, which can be tracked over subsequent stereo pairs. This generates what is usually referred to as visual odometry. The paper details the various steps of the algorithm and presents the results of experimental tests performed with an all-terrain rover, proving the method to be effective and robust
A Fuzzy Lane Tracking System for Driver Assistance
No full textIn the last few years, driver-assistance systems are increasingly being investigated in automotive field to provide a higher degree of comfort and safety. Lane position determination plays a critical role toward the development of autonomous and computer-aided driving. This paper presents an accurate and robust method for detecting lateral road marking with applications in autonomous vehicles and driver support systems. Much like other lane detection systems, ours is based on computer vision and Hough transform. Our approach, however, is unique in that it combines geometrical and intensity information of the image, based on a fuzzy logic inference system implementing in-depth understanding of different driving and environmental conditions. We call it Fuzzy Logic lane (FLane) tracking system. Details of the main components of the FLane module are presented along with experimental results obtained under varying lighting and road conditions. It is shown that the proposed method is reliable and effective in detecting road border and can be successfully employed for driver assistance
Radar-Vision Integration for Self-Supervised Scene Segmentation
No full textThis paper presents a radar-vision classification approach to segment the visual scene into ground and nonground regions. The proposed system features two main phases: a radar-supervised training phase and a visual classification phase. The training stage relies on a radar-based classifier to drive the selection of ground patches in the camera images, and learn online the visual appearance of the ground. In the classification stage, the visual model of the ground is used for image segmentation. Experimental results, obtained with an unmanned ground vehicle operating in a rural environment, are presented to validate the proposed system
Terrain assessment for precision agriculture using vehicle dynamic modelling
No full textAdvances in precision agriculture greatly rely on innovative control and sensing technologies that allow service units to increase their level of driving automation while ensuring at the same time high safety standards. This paper deals with automatic terrain estimation and classification that is performed simultaneously by an agricultural vehicle during normal operations. Vehicle mobility and safety, and the successful implementation of important agricultural tasks including seeding, ploughing, fertilising and controlled traffic depend or can be improved by a correct identification of the terrain that is traversed. The novelty of this research lies in that terrain estimation is performed by using not only traditional appearance-based features, that is colour and geometric properties, but also contact-based features, that is measuring physics-based dynamic effects that govern the vehicle–terrain interaction and that greatly affect its mobility. Experimental results obtained from an all-terrain vehicle operating on different surfaces are presented to validate the system in the field. It was shown that a terrain classifier trained with contact features was able to achieve a correct prediction rate of 85.1%, which is comparable or better than that obtained with approaches using traditional feature sets. To further improve the classification performance, all feature sets were merged in an augmented feature space, reaching, for these tests, 89.1% of correct predictions
Vision-based Wheel Sinkage Estimation for Rough-Terrain Mobile Robots
No full textFor mobile robots driving across soft soils, such as
sand, loose dirt, or snow, it is critical that the dynamic effects occurring at the wheel-terrain interface be taken into account. One of the most prevalent of these effects is wheel sinkage. Wheels can sink in soft soils to depths sufficient to prohibit further motion, leading to danger of entrapment with consequent mission failure. This paper presents an algorithm for visual estimation of wheel sinkage in deformable terrain. We call it the Visual Sinkage Estimation (VSE) method. It assumes the
presence of a monocular camera mounted on the wheel assembly, with a field of view containing the wheel–terrain interface. An artificial pattern, composed of concentric circumferences equally spaced apart on a white background, is attached to the wheel side in order to determine the contact angle with the terrain, following an edge detection strategy. The paper also introduces an analytical model for wheel sinkage in soft, deformable terrain based on terramechanics. In order to validate the VSE module, several tests were, first, performed on a single-wheel test bed, under different operating conditions including non-flat terrains, variable lighting conditions, and terrain with and without rocks. Successively, the effectiveness of the proposed approach in real
context was proved, employing an all-terrain rover traveling on a sandy beach
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