JSEG-based image segmentation in computer vision for agricultural mobile robot navigation

This project aims to apply image processing techniques in computer vision featuring an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained.

ANN statistical image recognition method for computer vision in agricultural mobile robot navigation

The main application area in this project, is to deploy image processing and segmentation techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. Thereby, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for image recognition. Hence, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave computational platforms, along with the application of customized Back-propagation Multilayer Perceptron (MLP) algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of segmented images in which reasonably accurate results were obtained. © 2010 IEEE.

Pattern recognition structured heuristics methods for image processing in mobile robot navigation

In this project, the main focus is to apply image processing techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained. ©2010 IEEE.

Mobile Robot navigation modelling, control and applications

This work presents and discusses the main topics involved on the design of a mobile robot system and focus on the control and navigation systems for autonomous mobile robots. Introduces the main aspects of the Robot design, which is a holistic vision about all the steps of the development process of an autonomous mobile robot; discusses the problems addressed to the conceptualization of the mobile robot physical structure and its relation to the world. Presents the dynamic and control analysis for navigation robots with kinematic and dynamic model and, for final, presents applications for a robotic platform of Automation, Simulation, Control and Supervision of Mobile Robots Navigation, with studies of dynamic and kinematic modelling, control algorithms, mechanisms for mapping and localization, trajectory planning and the platform simulator. © 2012 Praise Worthy Prize S.r.l. - All rights reserved.

Desenvolvimento de um sistema de navegação baseado em máquina de vetores de suporte para dirigibilidade de um robô móvel por caminhos em plantações

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Desenvolvimento de um sistema para navegação de robôs móveis por caminhos em plantações

The use of mobile robots in the agriculture turns out to be interesting in tasks of cultivation and application of pesticides in minute quantities to reduce environmental pollution. In this paper we present the development of a system to control an autonomous mobile robot navigation through tracks in plantations. Track images are used to control robot direction by preprocessing them to extract image features, and then submitting such characteristic features to a support vector machine to find out the most appropriate route. As the overall goal of the project to which this work is connected is the robot control in real time, the system will be embedded onto a hardware platform. However, in this paper we report the software implementation of a support vector machine, which so far presented around 93% accuracy in predicting the appropriate route.

Development of a support vector machine-based navigation system for driving a mobile robot through paths in plantations

The use of mobile robots turns out to be interesting in activities where the action of human specialist is difficult or dangerous. Mobile robots are often used for the exploration in areas of difficult access, such as rescue operations and space missions, to avoid human experts exposition to risky situations. Mobile robots are also used in agriculture for planting tasks as well as for keeping the application of pesticides within minimal amounts to mitigate environmental pollution. In this paper we present the development of a system to control the navigation of an autonomous mobile robot through tracks in plantations. Track images are used to control robot direction by preprocessing them to extract image features. Such features are then submitted to a support vector machine in order to find out the most appropriate route. The overall goal of the project to which this work is connected is to develop a real time robot control system to be embedded into a hardware platform. In this paper we report the software implementation of a support vector machine, which so far presented around 93% accuracy in predicting the appropriate route. © 2012 IEEE.

Multivalued adaptive neuro-fuzzy controller for robot vehicle - art. no. 1406

Robotic vehicle navigation in unstructured and uncertain environments is still a challenge. This paper presents the implementation of a multivalued neurofuzzy controller for autonomous ground vehicle (AGVs) in indoor environments. The control system consists of a hierarchy of mobile robot using multivalued adaptive neuro-fuzzy inference system behaviors.

Paraconsistent Autonomous Mobile Robot Emmy III

This work presents some improvements regarding to the autonomous mobile robot Emmy based on Paraconsistent Annotated Evidential Logic ET. A discussion on navigation system is presented.

Environment mapping for mobile robots navigation using hierarchical neural network and omnivision

Autonomous robots must be able to learn and maintain models of their environments. In this context, the present work considers techniques for the classification and extraction of features from images in joined with artificial neural networks in order to use them in the system of mapping and localization of the mobile robot of Laboratory of Automation and Evolutive Computer (LACE). To do this, the robot uses a sensorial system composed for ultrasound sensors and a catadioptric vision system formed by a camera and a conical mirror. The mapping system is composed by three modules. Two of them will be presented in this paper: the classifier and the characterizer module. The first module uses a hierarchical neural network to do the classification; the second uses techiniques of extraction of attributes of images and recognition of invariant patterns extracted from the places images set. The neural network of the classifier module is structured in two layers, reason and intuition, and is trained to classify each place explored for the robot amongst four predefine classes. The final result of the exploration is the construction of a topological map of the explored environment. Results gotten through the simulation of the both modules of the mapping system will be presented in this paper. © 2008 IEEE.

TORP: The Open Robot Project A Framework for Module-Based Robots

The development of robots has shown itself as a very complex interdisciplinary research field. The predominant procedure for these developments in the last decades is based on the assumption that each robot is a fully personalized project, with the direct embedding of hardware and software technologies in robot parts with no level of abstraction. Although this methodology has brought countless benefits to the robotics research, on the other hand, it has imposed major drawbacks: (i) the difficulty to reuse hardware and software parts in new robots or new versions; (ii) the difficulty to compare performance of different robots parts; and (iii) the difficulty to adapt development needs-in hardware and software levels-to local groups expertise. Large advances might be reached, for example, if physical parts of a robot could be reused in a different robot constructed with other technologies by other researcher or group. This paper proposes a framework for robots, TORP (The Open Robot Project), that aims to put forward a standardization in all dimensions (electrical, mechanical and computational) of a robot shared development model. This architecture is based on the dissociation between the robot and its parts, and between the robot parts and their technologies. In this paper, the first specification for a TORP family and the first humanoid robot constructed following the TORP specification set are presented, as well as the advances proposed for their improvement.

Evaluation of the Cartographic Communication Performance of a Route Guidance and Navigation System

This study evaluates the influence of different cartographic representations of in-car navigation systems on visual demand, subjective preference, and navigational error. It takes into account the type and complexity of the representation, maneuvering complexity, road layout, and driver gender. A group of 28 drivers (14 male and 14 female) participated in this experiment which was performed in a low-cost driving simulator. The tests were performed on a limited number of instances for each type of representation, and their purpose was to carry out a preliminary assessment and provide future avenues for further studies. Data collected for the visual demand study were analyzed using non-parametric statistical analyses. Results confirmed previous research that showed that different levels of design complexity significantly influence visual demand. Non-grid-like road networks, for example, influence significantly visual demand and navigational error. An analysis of simple maneuvers on a grid-like road network showed that static and blinking arrows did not present significant differences. From the set of representations analyzed to assess visual demand, both arrows were equally efficient. From a gender perspective, women seem to took at the display more than men, but this factor was not significant. With respect to subjective preferences, drivers prefer representations with mimetic landmarks when they perform straight-ahead tasks. For maneuvering tasks, landmarks in a perspective model created higher visual demands.

Observation of persistent photoconductivity in vanadyl phthalocyanine

Persistent photoconductivity (PPC) in vanadyl phthalocyanine (VOPc) organic light-emitting diodes was investigated using photoconductive time response, photocurrent-voltage characteristics and charge extraction in linearly increasing voltage (CELIV) measurements. The experiments were performed in phase 1 (amorphous) and in phase 2 (crystalline) samples obtained by the physical vapour deposition (PVD) technique over ITO/glass electrodes with an Al covering electrode. The results indicated a photoconductivity with a long decay time in phase 1 VOPc described by a stretched exponential relaxation. The device showed a rectifying behaviour and the mobility of holes was measured by CELIV, following a dispersive model. In crystalline samples the PPC effect was not observed and the dominant mechanism of transport of holes was hopping in a Gaussian density of states.

Persistent Pleuropulmonary Air Leak Treated with Autologous Blood: Results from a University Hospital and Review of Literature

Background: Persistent air leak after pulmonary resection is a difficult complication for thoracic surgeons to manage. Objectives: To show the results of our experience treating persistent pleuropulmonary air leak with autologous blood and review the literature on this specific method of treatment. Methods: Retrospective study of patients with persistent aerial pleuropulmonary fistula treated with autologous blood. The patient's own blood was collected from a peripheral vein and directly introduced through the pleural drain. An inverted siphon was located in the drainage system to avoid prolonged clamping of the drain. This siphon impeded blood return but not air escape. Results: Between January 2001 and August 2008, 27 patients were treated by the above method. Patient age ranged from 2 to 74 years, and 78% were male. Each procedure used a mean quantity of 92 ml blood. Mean persistent air leak time before pleurodesis was 10.6 days and mean time to fistula resolution after pleurodesis was 1.5 days. Twenty-three (85%) patients had persistent pleuropulmonary air leak closed with the above procedure. Conclusion: Treating persistent pleuropulmonary air leak with autologous blood is promising, but further studies are required to quantify its real effectiveness. Copyright (C) 2009 S. Karger AG, Basel

Treatment of Acral Persistent Papular Mucinosis with Electrocoagulation

Background: Acral persistent papular mucinosis is a rare localized form of lichen myxedematosus with few case reports and no documented therapeutic options.Objective: To report full resolution of acral persistent papular mucinosis after electrocoagulation.Methods: Case report of a 51-year-old white female diagnosed with an acral persistent papular mucinosis. The clinical and histopathologic features, treatment provided, and response to treatment are detailed.Results: Acral persistent papular mucinosis presented as multiple asymptomatic normochromic papules on the wrists. Treatment with topical and intralesional steroids was unsatisfactory. Gentle electrocoagulation led to complete resolution of the lesions and negligible scarring. The favorable results remained for 6 months of follow-up, and no new lesions have emerged.Conclusion: Our case of acral persistent papular mucinosis was successfully treated with electrocoagulation and long-lasting, excellent cosmetic results.