3,056 research outputs found
Global-referenced navigation grids for off-road vehicles and environments
[EN] The presence of automation and information technology in agricultural environments seems no longer questionable; smart spraying, variable rate fertilizing, or automatic guidance are becoming usual management tools in modern farms. Yet, such techniques are still in their nascence and offer a lively hotbed for innovation. In particular, significant research efforts are being directed toward vehicle navigation and awareness in off-road environments. However, the majority of solutions being developed are based on occupancy grids referenced with odometry and dead-reckoning, or alternatively based on GPS waypoint following, but never based on both. Yet, navigation in off-road environments highly benefits from both approaches: perception data effectively condensed in regular grids, and global references for every cell of the grid. This research proposes a framework to build globally referenced navigation grids by combining three-dimensional stereo vision with satellite-based global positioning. The construction process entails the in-field recording of perceptual information plus the geodetic coordinates of the vehicle at every image acquisition position, in addition to other basic data as velocity, heading, or GPS quality indices. The creation of local grids occurs in real time right after the stereo images have been captured by the vehicle in the field, but the final assembly of universal grids takes place after finishing the acquisition phase. Vehicle-fixed individual grids are then superposed onto the global grid, transferring original perception data to universal cells expressed in Local Tangent Plane coordinates. Global referencing allows the discontinuous appendage of data to succeed in the completion and updating of navigation grids along the time over multiple mapping sessions. This methodology was validated in a commercial vineyard, where several universal grids of the crops were generated. Vine rows were correctly reconstructed, although some difficulties appeared around the headland turns as a consequence of unreliable heading estimations. Navigation information conveyed through globally referenced regular grids turned out to be a powerful tool for upcoming practical implementations within agricultural robotics. (C) 2011 Elsevier B.V. All rights reserved.The author would like to thank Juan Jose Pena Suarez and Montano Perez Teruel for their assistance in the preparation of the prototype vehicle, Veronica Saiz Rubio for her help during most of the field experiments, Ratul Banerjee for his contribution in the development of software, and Luis Gil-Orozco Esteve for granting permission to perform multiple tests in the vineyards of his winery Finca Ardal. Gratitude is also extended to the Spanish Ministry of Science and Innovation for funding this research through project AGL2009-11731.Rovira Más, F. (2011). Global-referenced navigation grids for off-road vehicles and environments. Robotics and Autonomous Systems. 60(2):278-287. https://doi.org/10.1016/j.robot.2011.11.007S27828760
Percepção do ambiente urbano e navegação usando visĂŁo robĂłtica : concepção e implementação aplicado Ă veĂculo autĂ´nomo
Orientadores: Janito Vaqueiro Ferreira, Alessandro CorrĂŞa VictorinoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: O desenvolvimento de veĂculos autĂ´nomos capazes de se locomover em ruas urbanas pode proporcionar importantes benefĂcios na redução de acidentes, no aumentando da qualidade de vida e tambĂ©m na redução de custos. VeĂculos inteligentes, por exemplo, frequentemente baseiam suas decisões em observações obtidas a partir de vários sensores tais como LIDAR, GPS e câmeras. Atualmente, sensores de câmera tĂŞm recebido grande atenção pelo motivo de que eles sĂŁo de baixo custo, fáceis de utilizar e fornecem dados com rica informação. Ambientes urbanos representam um interessante mas tambĂ©m desafiador cenário neste contexto, onde o traçado das ruas podem ser muito complexos, a presença de objetos tais como árvores, bicicletas, veĂculos podem gerar observações parciais e tambĂ©m estas observações sĂŁo muitas vezes ruidosas ou ainda perdidas devido a completas oclusões. Portanto, o processo de percepção por natureza precisa ser capaz de lidar com a incerteza no conhecimento do mundo em torno do veĂculo. Nesta tese, este problema de percepção Ă© analisado para a condução nos ambientes urbanos associado com a capacidade de realizar um deslocamento seguro baseado no processo de tomada de decisĂŁo em navegação autĂ´noma. Projeta-se um sistema de percepção que permita veĂculos robĂłticos a trafegar autonomamente nas ruas, sem a necessidade de adaptar a infraestrutura, sem o conhecimento prĂ©vio do ambiente e considerando a presença de objetos dinâmicos tais como veĂculos. Propõe-se um novo mĂ©todo baseado em aprendizado de máquina para extrair o contexto semântico usando um par de imagens estĂ©reo, a qual Ă© vinculada a uma grade de ocupação evidencial que modela as incertezas de um ambiente urbano desconhecido, aplicando a teoria de Dempster-Shafer. Para a tomada de decisĂŁo no planejamento do caminho, aplica-se a abordagem dos tentáculos virtuais para gerar possĂveis caminhos a partir do centro de referencia do veĂculo e com base nisto, duas novas estratĂ©gias sĂŁo propostas. Em primeiro, uma nova estratĂ©gia para escolher o caminho correto para melhor evitar obstáculos e seguir a tarefa local no contexto da navegação hibrida e, em segundo, um novo controle de malha fechada baseado na odometria visual e o tentáculo virtual Ă© modelado para execução do seguimento de caminho. Finalmente, um completo sistema automotivo integrando os modelos de percepção, planejamento e controle sĂŁo implementados e validados experimentalmente em condições reais usando um veĂculo autĂ´nomo experimental, onde os resultados mostram que a abordagem desenvolvida realiza com sucesso uma segura navegação local com base em sensores de câmeraAbstract: The development of autonomous vehicles capable of getting around on urban roads can provide important benefits in reducing accidents, in increasing life comfort and also in providing cost savings. Intelligent vehicles for example often base their decisions on observations obtained from various sensors such as LIDAR, GPS and Cameras. Actually, camera sensors have been receiving large attention due to they are cheap, easy to employ and provide rich data information. Inner-city environments represent an interesting but also very challenging scenario in this context, where the road layout may be very complex, the presence of objects such as trees, bicycles, cars might generate partial observations and also these observations are often noisy or even missing due to heavy occlusions. Thus, perception process by nature needs to be able to deal with uncertainties in the knowledge of the world around the car. While highway navigation and autonomous driving using a prior knowledge of the environment have been demonstrating successfully, understanding and navigating general inner-city scenarios with little prior knowledge remains an unsolved problem. In this thesis, this perception problem is analyzed for driving in the inner-city environments associated with the capacity to perform a safe displacement based on decision-making process in autonomous navigation. It is designed a perception system that allows robotic-cars to drive autonomously on roads, without the need to adapt the infrastructure, without requiring previous knowledge of the environment and considering the presence of dynamic objects such as cars. It is proposed a novel method based on machine learning to extract the semantic context using a pair of stereo images, which is merged in an evidential grid to model the uncertainties of an unknown urban environment, applying the Dempster-Shafer theory. To make decisions in path-planning, it is applied the virtual tentacle approach to generate possible paths starting from ego-referenced car and based on it, two news strategies are proposed. First one, a new strategy to select the correct path to better avoid obstacles and to follow the local task in the context of hybrid navigation, and second, a new closed loop control based on visual odometry and virtual tentacle is modeled to path-following execution. Finally, a complete automotive system integrating the perception, path-planning and control modules are implemented and experimentally validated in real situations using an experimental autonomous car, where the results show that the developed approach successfully performs a safe local navigation based on camera sensorsDoutoradoMecanica dos SĂłlidos e Projeto MecanicoDoutor em Engenharia Mecânic
AutonoVi: Autonomous Vehicle Planning with Dynamic Maneuvers and Traffic Constraints
We present AutonoVi:, a novel algorithm for autonomous vehicle navigation
that supports dynamic maneuvers and satisfies traffic constraints and norms.
Our approach is based on optimization-based maneuver planning that supports
dynamic lane-changes, swerving, and braking in all traffic scenarios and guides
the vehicle to its goal position. We take into account various traffic
constraints, including collision avoidance with other vehicles, pedestrians,
and cyclists using control velocity obstacles. We use a data-driven approach to
model the vehicle dynamics for control and collision avoidance. Furthermore,
our trajectory computation algorithm takes into account traffic rules and
behaviors, such as stopping at intersections and stoplights, based on an
arc-spline representation. We have evaluated our algorithm in a simulated
environment and tested its interactive performance in urban and highway driving
scenarios with tens of vehicles, pedestrians, and cyclists. These scenarios
include jaywalking pedestrians, sudden stops from high speeds, safely passing
cyclists, a vehicle suddenly swerving into the roadway, and high-density
traffic where the vehicle must change lanes to progress more effectively.Comment: 9 pages, 6 figure
Online self-reconfigurable robot navigation in heterogeneous environments
This paper presents a robot navigation system capable of online self-reconfiguration according to the needs imposed by the various contexts present in heterogeneous environments. The ability to cope with heterogeneous environments is key for a robust deployment of service robots in truly demanding scenarios. In the proposed system, flexibility is present at the several layers composing the robot's navigation system. At the lowest layer, proper locomotion modes are selected according to the environment's local context. At the highest layer, proper motion and path planning strategies are selected according to the environment's global context. While local context is obtained directly from the robot's sensory input, global context is inspected from semantic labels registered off-line on geo-referenced maps. The proposed system leverages on the well-known Robotics Operating System (ROS) framework for the implementation of the major navigation system components. The system was successfully validated over approximately 1 Km long experiments on INTROBOT, an all-terrain industrial-grade robot equipped with four independently steered wheels.info:eu-repo/semantics/acceptedVersio
Proximal sensing mapping method to generate field maps in vineyards
[EN] An innovative methodology to generate vegetative vigor maps in vineyards (Vitis vinifera L.) has been developed
and pre-validated. The architecture proposed implements a Global Positioning System (GPS) receiver and a computer vision
unit comprising a monocular charge-coupled device (CCD) camera equipped with an 8-mm lens and a pass-band near-infrared
(NIR) filter. Both sensors are mounted on a medium-size conventional agricultural tractor. The synchronization of
perception (camera) and localization (GPS) sensors allowed the creation of globally-referenced regular grids, denominated
universal grids, whose cells were filled with the estimated vegetative vigor of the monitored vines. Vine vigor was quantified
as the relative percentage of vegetation automatically estimated by the onboard algorithm through the images captured with the
camera. Validation tests compared spatial differences in vine vigor with yield differentials along the rows. The positive
correlation between vigor and yield variations showed the potential of proximal sensing and the advantages of acquiring top
view images from conventional vehicles.Sáiz Rubio, V.; Rovira Más, F. (2013). Proximal sensing mapping method to generate field maps in vineyards. Agricultural Engineering International: CIGR Journal. 15(2):47-59. http://hdl.handle.net/10251/102750S475915
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Towards secure & robust PNT for automated systems
This dissertation makes four contributions in support of secure and robust position, navigation, and timing (PNT) for automated systems. The first two relate to PNT security while the latter two address robust positioning for automated ground vehicles.
The first contribution is a fundamental theory for provably-secure clock synchronization between two agents in a distributed automated system. All one-way synchronization protocols, such as those based on the Global Positioning System (GPS) and other Global Navigation Satellite Systems (GNSS), are shown to be vulnerable to man-in-the-middle delay attacks. This contribution is the first to identify the necessary and sufficient conditions for provably secure clock synchronization.
The second contribution, also related to PNT security, is a three-year study of the world-wide GPS interference landscape based on data from a dual-frequency GNSS receiver operating continuously on the International Space Station (ISS). This work is the first publicly-reported space-based survey of GNSS interference, and unveils previously-unreported GNSS interference activity.
The third contribution is a novel ground vehicle positioning technique that is robust to GNSS signal blockage, poor lighting conditions, and adverse weather events such as heavy rain and dense fog. The technique relies on sensors that are commonly available on automated vehicles and are insensitive to lighting and inclement weather: automotive radar, low-cost inertial measurement units (IMUs), and GNSS. Remarkably, it is shown that, given a prior radar map, the proposed technique operating on data from off-the-shelf all-weather automotive sensors can maintain sub-50-cm horizontal position accuracy during 60 min of GNSS-denied driving in downtown Austin, TX.
This dissertation’s final contribution is an analysis and demonstration of the feasibility of crowd-sourced digital mapping for automated vehicles. Localization techniques, such as the one described in the previous contribution, rely on such digital maps for accuracy and robustness. A key enabler for large-scale up-to-date maps is enlisting the help of the very consumer vehicles that need the map to build and update it. A method for fusing multi-session vision data into a unified digital map is developed. The asymptotic limit of such a map’s globally-referenced position accuracy is explored for the case in which the mapping agents rely on low-cost GNSS receivers performing standard code-phase-based navigation. Experimental validation along a semi-urban route shows that low-cost consumer vehicles incrementally tighten the accuracy of the jointly-optimized digital map over time enough to support sub-lane-level positioning in a global frame of reference.Electrical and Computer Engineerin
Combining Satellite Images and Cadastral Information for Outdoor Autonomous Mapping and Navigation: A Proof-of-Concept Study in Citric Groves
The development of robotic applications for agricultural environments has several problems which are not present in the robotic systems used for indoor environments. Some of these problems can be solved with an efficient navigation system. In this paper, a new system is introduced to improve the navigation tasks for those robots which operate in agricultural environments. Concretely, the paper focuses on the problem related to the autonomous mapping of agricultural parcels (i.e., an orange grove). The map created by the system will be used to help the robots navigate into the parcel to perform maintenance tasks such as weed removal, harvest, or pest inspection. The proposed system connects to a satellite positioning service to obtain the real coordinates where the robotic system is placed. With these coordinates, the parcel information is downloaded from an online map service in order to autonomously obtain a map of the parcel in a readable format for the robot. Finally, path planning is performed by means of Fast Marching techniques using the robot or a team of two robots. This paper introduces the proof-of-concept and describes all the necessary steps and algorithms to obtain the path planning just from the initial coordinates of the robot
Augmented Perception for Agricultural Robots Navigation
[EN] Producing food in a sustainable way is becoming very challenging today due to the lack of skilled labor, the unaffordable costs of labor when available, and the limited returns for growers as a result of low produce prices demanded by big supermarket chains in contrast to ever-increasing costs of inputs such as fuel, chemicals, seeds, or water. Robotics emerges as a technological advance that can counterweight some of these challenges, mainly in industrialized countries. However, the deployment of autonomous machines in open environments exposed to uncertainty and harsh ambient conditions poses an important defiance to reliability and safety. Consequently, a deep parametrization of the working environment in real time is necessary to achieve autonomous navigation. This article proposes a navigation strategy for guiding a robot along vineyard rows for field monitoring. Given that global positioning cannot be granted permanently in any vineyard, the strategy is based on local perception, and results from fusing three complementary technologies: 3D vision, lidar, and ultrasonics. Several perception-based navigation algorithms were developed between 2015 and 2019. After their comparison in real environments and conditions, results showed that the augmented perception derived from combining these three technologies provides a consistent basis for outlining the intelligent behavior of agricultural robots operating within orchards.This work was supported by the European Union Research and Innovation Programs under Grant N. 737669 and Grant N. 610953. The associate editor coordinating the review of this article and approving it for publication was Dr. Oleg Sergiyenko.Rovira Más, F.; Sáiz Rubio, V.; Cuenca-Cuenca, A. (2021). Augmented Perception for Agricultural Robots Navigation. IEEE Sensors Journal. 21(10):11712-11727. https://doi.org/10.1109/JSEN.2020.3016081S1171211727211
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