Projeto e desenvolvimento da arquitetura de um robô agrícola móvel

Parameters such as tolerance, scale and agility utilized in data sampling for using in Precision Agriculture required an expressive number of researches and development of techniques and instruments for automation. It is highlighted the employment of methodologies in remote sensing used in coupled to a Geographic Information System (GIS), adapted or developed for agricultural use. Aiming this, the application of Agricultural Mobile Robots is a strong tendency, mainly in the European Union, the USA and Japan. In Brazil, researches are necessary for the development of robotics platforms, serving as a basis for semi-autonomous and autonomous navigation systems. The aim of this work is to describe the project of an experimental platform for data acquisition in field for the study of the spatial variability and development of agricultural robotics technologies to operate in agricultural environments. The proposal is based on a systematization of scientific work to choose the design parameters utilized for the construction of the model. The kinematic study of the mechanical structure was made by the virtual prototyping process, based on modeling and simulating of the tension applied in frame, using the.Parâmetros, como tolerância, escala e agilidade empregados na amostragem de dados para uso em Agricultura de Precisão, exigem um expressivo número de pesquisas no desenvolvimento de instrumentos e técnicas para automação. Destacam-se a utilização de metodologias em sensoriamento remoto utilizadas em conjunto com o Sistema de Informação Geográfica (SIG), adaptados ou desenvolvidos para o uso agrícola. Visando a isso, a aplicação de Robôs Agrícolas Móveis é uma forte tendência, principalmente na União Europeia, EUA e Japão. No Brasil, pesquisas são necessárias para o desenvolvimento de plataformas robóticas, que sirvam de base para sistemas de navegação autônomos ou semiautônomos. O objetivo deste trabalho é descrever o projeto de uma plataforma experimental para a aquisição de dados em campo para o estudo da variabilidade espacial e desenvolvimento de tecnologias para operação de robôs em ambiente agrícola. A proposta baseia-se em uma sistematização de trabalhos científicos que norteiam a escolha dos parâmetros de projeto utilizados para a construção do modelo. O estudo cinemático da estrutura mecânica foi feito pelo processo de prototipagem virtual, baseado na modelagem e simulação das tensões aplicadas no chassi, utilizando o método dos elementos finitos, em função dos conceitos básicos de cinemática de robôs móveis e experiências em trabalhos anteriores.(FAPESP) São Paulo Research Foundation(CNPq) National Council of Scientific and Technological Developmen

Diseño de un sistema automático de control mecánico de malezas en cultivos de algodón

El algodón peruano (también llamado “oro blanco”) es considerado como uno de los mejores materiales en la industria textil del mundo. No obstante, en las últimas décadas, nuestro país ha enfrentado una pérdida de competitividad frente a variedades extranjeras y frente a otros cultivos. Una de las razones de este hecho es la escasa tecnología que existe debido a la poca inversión en estas prácticas agrícolas, específicamente en el control de malezas, las cuales pueden afectar hasta en un 90% la cosecha cuando no se efectúa ningún tipo de medida preventiva o correctiva. Dicha tarea es usualmente realizada a través de una labor manual o de un control químico. El primero exige mucho esfuerzo por parte de los trabajadores y demanda, asimismo, mucho tiempo. El segundo es perjudicial para el medio ambiente y requiere que su uso se realice por expertos, aumentando su costo. Es por ello que en el presente trabajo se planteó el diseño de un sistema mecatrónico con autonomía energética capaz de realizar la tarea de remoción mecánica de malezas en un cultivo con 120 cm de distancia entre surcos y 40 cm de distancia entre plantas. El sistema cuenta con un móvil que se moviliza a través de los surcos del cultivo siguiendo una trayectoria definida por el usuario. El control de seguimiento se logra haciendo uso de un sistema de localización y navegación basado en datos de sensores tipo IMU y GPS. El móvil cuenta con un sistema de suspensión basado en un mecanismo diferencial que permite que las ruedas estén siempre en contacto con el suelo. Finalmente, la detección de plantas, que acciona el removedor de malezas, se logra procesando información de una cámara. La metodología del diseño empezó con la delimitación del trabajo y búsqueda de información para poder desarrollar conceptos de solución y elegir el vehículo óptimo. Se definieron las funciones, las lógicas de control, los sensores y actuadores y la estructura mecánica para poder cumplir con los requerimientos propuestos. Como resultado, se tiene un móvil con un costo bajo (tomando en cuenta que actualmente no existen productos comerciales similares), que utiliza los componentes necesarios para darle una autonomía energética al sistema de aproximadamente 2 horas. Las estrategias de control planteadas permiten que el móvil se traslade por el cultivo sin la necesidad de la supervisión o intervención de un operador.Tesi

Simulation and optimization of robotic tasks for UV treatment of diseases in horticulture

Robotization is increasingly used in the agriculture since the last few decades. It is progressively replacing the human workforce that is deserting the agricultural sector, partly because of the harshness of its activities and health risks they may present. Moreover, robotization aims to improve efficiency and competitiveness of the agricultural sector. However, it leads to several research and development challenges regarding robots supervision, control and optimization. This paper presents a simulation and optimization approach for the optimization of robotized treatment tasks using type-c ultraviolet radiation in horticulture. The optimization of tasks scheduling problem is formalized and a heuristic and a genetic algorithms are proposed to solve it. These algorithms are evaluated compared to an exact method using a multiagent-based simulation approach. The simulator takes into account the evolution of the disease during time and simulates the execution of treatment tasks by the robot.Interreg North-West Europe Programme in context of UV-ROBOT

Development of a vision-based local positioning system for weed detection

Herbicides applications could possibly be reduced if targeted. Targeting the applications requires prior identification and quantification of the weed population. This task could possibly be done by a weed scout robot. The ability to position a camera over the inter-row space of densely seeded crops will help to simplify the task of automatically quantifying weed infestations. As part of the development of an autonomous weed scout, a vision-based local positioning system for weed detection has been developed and tested in a laboratory setting. Four Line-detection algorithms have been tested and a robotic positioning device, or XYZtheta-table, was developed and tested. The Line-detection algorithms were based respectively on a stripe analysis, a blob analysis, a linear regression and the Hough Transform. The last two also included an edge-detection step. Images of parallel line patterns representing crop rows were collected at different angles, with and without weed-simulating noise. The images were processed by the four programs. The ability of the programs to determine the angle of the rows and the location of an inter-row space centreline was evaluated in a laboratory setting. All algorithms behaved approximately the same when determining the rows’ angle in the noise-free images, with a mean error of 0.5°. In the same situation, all algorithms could find the centreline of an inter-row space within 2.7 mm. Generally, the mean errors increased when noise was added to the images, up to 1.1° and 8.5 mm for the Linear Regression algorithm. Specific dispersions of the weeds were identified as possible causes of increase of the error in noisy images. Because of its insensitivity to noise, the Stripe Analysis algorithm was considered the best overall. The fastest program was the Blob Analysis algorithm with a mean processing time of 0.35 s per image. Future work involves evaluation of the Line-detection algorithms with field images. The XYZtheta-table consisted of rails allowing movement of a camera in the 3 orthogonal directions and of a rotational table that could rotate the camera about a vertical axis. The ability of the XYZtheta-table to accurately move the camera within the XY-space and rotate it at a desired angle was evaluated in a laboratory setting. The XYZtheta-table was able to move the camera within 7 mm of a target and to rotate it with a mean error of 0.07°. The positioning accuracy could be improved by simple mechanical modifications on the XYZtheta-table

Embedded System Design of Robot Control Architectures for Unmanned Agricultural Ground Vehicles

Engineering technology has matured to the extent where accompanying methods for unmanned field management is now becoming a technologically achievable and economically viable solution to agricultural tasks that have been traditionally performed by humans or human operated machines. Additionally, the rapidly increasing world population and the daunting burden it places on farmers in regards to the food production and crop yield demands, only makes such advancements in the agriculture industry all the more imperative. Consequently, the sector is beginning to observe a noticeable shift, where there exist a number of scalable infrastructural changes that are in the process of slowly being implemented onto the modular machinery design of agricultural equipment. This work is being pursued in effort to provide firmware descriptions and hardware architectures that integrate cutting edge technology onto the embedded control architectures of agricultural machinery designs to assist in achieving the end goal of complete and reliable unmanned agricultural automation. In this thesis, various types of autonomous control algorithms integrated with obstacle avoidance or guidance schemes, were implemented onto controller area network (CAN) based distributive real-time systems (DRTSs) in form of the two unmanned agricultural ground vehicles (UAGVs). Both vehicles are tailored to different applications in the agriculture domain as they both leverage state-of-the-art sensors and modules to attain the end objective of complete autonomy to allow for the automation of various types of agricultural related tasks. The further development of the embedded system design of these machines called for the developed firmware and hardware to be implemented onto both an event triggered and time triggered CAN bus control architecture as each robot employed its own separate embedded control scheme. For the first UAGV, a multiple GPS waypoint navigation scheme is derived, developed, and evaluated to yield a fully controllable GPS-driven vehicle. Additionally, obstacle detection and avoidance capabilities were also implemented onto the vehicle to serve as a safety layer for the robot control architecture, giving the ground vehicle the ability to reliability detect and navigate around any obstacles that may happen to be in the vicinity of the assigned path. The second UAGV was a smaller robot designed for field navigation applications. For this robot, a fully autonomous sensor based algorithm was proposed and implemented onto the machine. It is demonstrated that the utilization and implementation of laser, LIDAR, and IMU sensors onto a mobile robot platform allowed for the realization of a fully autonomous non-GPS sensor based algorithm to be employed for field navigation. The developed algorithm can serve as a viable solution for the application of microclimate sensing in a field. Advisors: A. John Boye and Santosh Pitl

Embedded System Design of Robot Control Architectures for Unmanned Agricultural Ground Vehicles

Engineering technology has matured to the extent where accompanying methods for unmanned field management is now becoming a technologically achievable and economically viable solution to agricultural tasks that have been traditionally performed by humans or human operated machines. Additionally, the rapidly increasing world population and the daunting burden it places on farmers in regards to the food production and crop yield demands, only makes such advancements in the agriculture industry all the more imperative. Consequently, the sector is beginning to observe a noticeable shift, where there exist a number of scalable infrastructural changes that are in the process of slowly being implemented onto the modular machinery design of agricultural equipment. This work is being pursued in effort to provide firmware descriptions and hardware architectures that integrate cutting edge technology onto the embedded control architectures of agricultural machinery designs to assist in achieving the end goal of complete and reliable unmanned agricultural automation. In this thesis, various types of autonomous control algorithms integrated with obstacle avoidance or guidance schemes, were implemented onto controller area network (CAN) based distributive real-time systems (DRTSs) in form of the two unmanned agricultural ground vehicles (UAGVs). Both vehicles are tailored to different applications in the agriculture domain as they both leverage state-of-the-art sensors and modules to attain the end objective of complete autonomy to allow for the automation of various types of agricultural related tasks. The further development of the embedded system design of these machines called for the developed firmware and hardware to be implemented onto both an event triggered and time triggered CAN bus control architecture as each robot employed its own separate embedded control scheme. For the first UAGV, a multiple GPS waypoint navigation scheme is derived, developed, and evaluated to yield a fully controllable GPS-driven vehicle. Additionally, obstacle detection and avoidance capabilities were also implemented onto the vehicle to serve as a safety layer for the robot control architecture, giving the ground vehicle the ability to reliability detect and navigate around any obstacles that may happen to be in the vicinity of the assigned path. The second UAGV was a smaller robot designed for field navigation applications. For this robot, a fully autonomous sensor based algorithm was proposed and implemented onto the machine. It is demonstrated that the utilization and implementation of laser, LIDAR, and IMU sensors onto a mobile robot platform allowed for the realization of a fully autonomous non-GPS sensor based algorithm to be employed for field navigation. The developed algorithm can serve as a viable solution for the application of microclimate sensing in a field. Advisors: A. John Boye and Santosh Pitl

A stochastic method for representation, modelling and fusion of excavated material in mining

The ability to safely and economically extract raw materials such as iron ore from a greater number of remote, isolated and possibly dangerous locations will become more pressing over the coming decades as easily accessible deposits become depleted. An autonomous mining system has the potential to make the mining process more efficient, predictable and safe under these changing conditions. One of the key parts of the mining process is the estimation and tracking of bulk material through the mining production chain. Current state-of-the-art tracking and estimation systems use a deterministic representation for bulk material. This is problematic for wide-scale automation of mine processes as there is no measurement of the uncertainty in the estimates provided. A probabilistic representation is critical for autonomous systems to correctly interpret and fuse the available data in order to make the most informed decision given the available information without human intervention. This thesis investigates whether bulk material properties can be represented probabilistically through a mining production chain to provide statistically consistent estimates of the material at each stage of the production chain. Experiments and methods within this thesis focus on the load-haul-dump cycle. The development of a representation of bulk material using lumped masses is presented. A method for tracking and estimation of these lumped masses within the mining production chain using an 'Augmented State Kalman Filter' (ASKF) is developed. The method ensures that the fusion of new information at different stages will provide statistically consistent estimates of the lumped mass. There is a particular focus on the feasibility and practicality of implementing a solution on a production mine site given the current sensing technology available and how it can be adapted for use within the developed estimation system (with particular focus on remote sensing and volume estimation)