Autonomisten metsäkoneiden koneaistijärjestelmät

A prerequisite for increasing the autonomy of forest machinery is to provide robots with digital situational awareness, including a representation of the surrounding environment and the robot's own state in it. Therefore, this article-based dissertation proposes perception systems for autonomous or semi-autonomous forest machinery as a summary of seven publications. The work consists of several perception methods using machine vision, lidar, inertial sensors, and positioning sensors. The sensors are used together by means of probabilistic sensor fusion. Semi-autonomy is interpreted as a useful intermediary step, situated between current mechanized solutions and full autonomy, to assist the operator. In this work, the perception of the robot's self is achieved through estimation of its orientation and position in the world, the posture of its crane, and the pose of the attached tool. The view around the forest machine is produced with a rotating lidar, which provides approximately equal-density 3D measurements in all directions. Furthermore, a machine vision camera is used for detecting young trees among other vegetation, and sensor fusion of an actuated lidar and machine vision camera is utilized for detection and classification of tree species. In addition, in an operator-controlled semi-autonomous system, the operator requires a functional view of the data around the robot. To achieve this, the thesis proposes the use of an augmented reality interface, which requires measuring the pose of the operator's head-mounted display in the forest machine cabin. Here, this work adopts a sensor fusion solution for a head-mounted camera and inertial sensors. In order to increase the level of automation and productivity of forest machines, the work focuses on scientifically novel solutions that are also adaptable for industrial use in forest machinery. Therefore, all the proposed perception methods seek to address a real existing problem within current forest machinery. All the proposed solutions are implemented in a prototype forest machine and field tested in a forest. The proposed methods include posture measurement of a forestry crane, positioning of a freely hanging forestry crane attachment, attitude estimation of an all-terrain vehicle, positioning a head mounted camera in a forest machine cabin, detection of young trees for point cleaning, classification of tree species, and measurement of surrounding tree stems and the ground surface underneath.Metsäkoneiden autonomia-asteen kasvattaminen edellyttää, että robotilla on digitaalinen tilannetieto sekä ympäristöstä että robotin omasta toiminnasta. Tämän saavuttamiseksi työssä on kehitetty autonomisen tai puoliautonomisen metsäkoneen koneaistijärjestelmiä, jotka hyödyntävät konenäkö-, laserkeilaus- ja inertia-antureita sekä paikannusantureita. Työ liittää yhteen seitsemässä artikkelissa toteutetut havainnointimenetelmät, joissa useiden anturien mittauksia yhdistetään sensorifuusiomenetelmillä. Työssä puoliautonomialla tarkoitetaan hyödyllisiä kuljettajaa avustavia välivaiheita nykyisten mekanisoitujen ratkaisujen ja täyden autonomian välillä. Työssä esitettävissä autonomisen metsäkoneen koneaistijärjestelmissä koneen omaa toimintaa havainnoidaan estimoimalla koneen asentoa ja sijaintia, nosturin asentoa sekä siihen liitetyn työkalun asentoa suhteessa ympäristöön. Yleisnäkymä metsäkoneen ympärille toteutetaan pyörivällä laserkeilaimella, joka tuottaa lähes vakiotiheyksisiä 3D-mittauksia jokasuuntaisesti koneen ympäristöstä. Nuoret puut tunnistetaan muun kasvillisuuden joukosta käyttäen konenäkökameraa. Lisäksi puiden tunnistamisessa ja puulajien luokittelussa käytetään konenäkökameraa ja laserkeilainta yhdessä sensorifuusioratkaisun avulla. Lisäksi kuljettajan ohjaamassa puoliautonomisessa järjestelmässä kuljettaja tarvitsee toimivan tavan ymmärtää koneen tuottaman mallin ympäristöstä. Työssä tämä ehdotetaan toteutettavaksi lisätyn todellisuuden käyttöliittymän avulla, joka edellyttää metsäkoneen ohjaamossa istuvan kuljettajan lisätyn todellisuuden lasien paikan ja asennon mittaamista. Työssä se toteutetaan kypärään asennetun kameran ja inertia-anturien sensorifuusiona. Jotta metsäkoneiden automatisaatiotasoa ja tuottavuutta voidaan lisätä, työssä keskitytään uusiin tieteellisiin ratkaisuihin, jotka soveltuvat teolliseen käyttöön metsäkoneissa. Kaikki esitetyt koneaistijärjestelmät pyrkivät vastaamaan todelliseen olemassa olevaan tarpeeseen nykyisten metsäkoneiden käytössä. Siksi kaikki menetelmät on implementoitu prototyyppimetsäkoneisiin ja tulokset on testattu metsäympäristössä. Työssä esitetyt menetelmät mahdollistavat metsäkoneen nosturin, vapaasti riippuvan työkalun ja ajoneuvon asennon estimoinnin, lisätyn todellisuuden lasien asennon mittaamisen metsäkoneen ohjaamossa, nuorten puiden havaitsemisen reikäperkauksessa, ympäröivien puiden puulajien tunnistuksen, sekä puun runkojen ja maanpinnan mittauksen

SLAM research for port AGV based on 2D LIDAR

With the increase in international trade, the transshipment of goods at international container ports is very busy. The AGV (Automated Guided Vehicle) has been used as a new generation of automated container horizontal transport equipment. The AGV is an automated unmanned vehicle that can work 24 hours a day, increasing productivity and reducing labor costs compared to using container trucks. The ability to obtain information about the surrounding environment is a prerequisite for the AGV to automatically complete tasks in the port area. At present, the method of AGV based on RFID tag positioning and navigation has a problem of excessive cost. This dissertation has carried out a research on applying light detection and ranging (LIDAR) simultaneous localization and mapping (SLAM) technology to port AGV. In this master's thesis, a mobile test platform based on a laser range finder is developed to scan 360-degree environmental information (distance and angle) centered on the LIDAR and upload the information to a real-time database to generate surrounding environmental maps, and the obstacle avoidance strategy was developed based on the acquired information. The effectiveness of the platform was verified by the experiments from multiple scenarios. Then based on the first platform, another experimental platform with encoder and IMU sensor was developed. In this platform, the functionality of SLAM is enabled by the GMapping algorithm and the installation of the encoder and IMU sensor. Based on the established environment SLAM map, the path planning and obstacle avoidance functions of the platform were realized.Com o aumento do comércio internacional, o transbordo de mercadorias em portos internacionais de contentores é muito movimentado. O AGV (“Automated Guided Vehicle”) foi usado como uma nova geração de equipamentos para transporte horizontal de contentores de forma automatizada. O AGV é um veículo não tripulado automatizado que pode funcionar 24 horas por dia, aumentando a produtividade e reduzindo os custos de mão-de-obra em comparação com o uso de camiões porta-contentores. A capacidade de obter informações sobre o ambiente circundante é um pré-requisito para o AGV concluir automaticamente tarefas na área portuária. Atualmente, o método de AGV baseado no posicionamento e navegação de etiquetas RFID apresenta um problema de custo excessivo. Nesta dissertação foi realizada uma pesquisa sobre a aplicação da tecnologia LIDAR de localização e mapeamento simultâneo (SLAM) num AGV. Uma plataforma de teste móvel baseada num telémetro a laser é desenvolvida para examinar o ambiente em redor em 360 graus (distância e ângulo), centrado no LIDAR, e fazer upload da informação para uma base de dados em tempo real para gerar um mapa do ambiente em redor. Uma estratégia de prevenção de obstáculos foi também desenvolvida com base nas informações adquiridas. A eficácia da plataforma foi verificada através da realização de testes com vários cenários e obstáculos. Por fim, com base na primeira plataforma, uma outra plataforma experimental com codificador e sensor IMU foi também desenvolvida. Nesta plataforma, a funcionalidade do SLAM é ativada pelo algoritmo GMapping e pela instalação do codificador e do sensor IMU. Com base no estabelecimento do ambiente circundante SLAM, foram realizadas as funções de planeamento de trajetória e prevenção de obstáculos pela plataforma

Human-Centric Machine Vision

Recently, the algorithms for the processing of the visual information have greatly evolved, providing efficient and effective solutions to cope with the variability and the complexity of real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. The Human-Centric Machine Vision can help to solve the problems raised by the needs of our society, e.g. security and safety, health care, medical imaging, and human machine interface. In such applications it is necessary to handle changing, unpredictable and complex situations, and to take care of the presence of humans

Toward Vision-based Control of Heavy-Duty and Long-Reach Robotic Manipulators

Heavy-duty mobile machines are an important part of the industry, and they are used for various work tasks in mining, construction, forestry, and agriculture. Many of these machines have heavy-duty, long-reach (HDLR) manipulators attached to them, which are used for work tasks such as drilling, lifting, and grabbing. A robotic manipulator, by definition, is a device used for manipulating materials without direct physical contact by a human operator. HDLR manipulators differ from manipulators of conventional industrial robots in the sense that they are subject to much larger kinematic and non-kinematic errors, which hinder the overall accuracy and repeatability of the robot’s tool center point (TCP). Kinematic errors result from modeling inaccuracies, while non-kinematic errors include structural flexibility and bending, thermal effects, backlash, and sensor resolution. Furthermore, conventional six degrees of freedom (DOF) industrial robots are more general-purpose systems, whereas HDLR manipulators are mostly designed for special (or single) purposes. HDLR manipulators are typically built as lightweight as possible while being able to handle significant load masses. Consequently, they have long reaches and high payload-to-own-weight ratios, which contribute to the increased errors compared to conventional industrial robots. For example, a joint angle measurement error of 0.5◦ associated with a 5-m-long rigid link results in an error of approximately 4.4 cm at the end of the link, with further errors resulting from flexibility and other non-kinematic aspects. The target TCP positioning accuracy for HDLR manipulators is in the sub-centimeter range, which is very difficult to achieve in practical systems. These challenges have somewhat delayed the automation of HDLR manipulators, while conventional industrial robots have long been commercially available. This is also attributed to the fact that machines with HDLR manipulators have much lower production volumes, and the work tasks are more non-repetitive in nature compared to conventional industrial robots in factories. Sensors are a key requirement in order to achieve automated operations and eventually full autonomy. For example, humans mostly rely on their visual perception in work tasks, while the collected information is processed in the brain. Much like humans, autonomous machines also require both sensing and intelligent processing of the collected sensor data. This dissertation investigates new visual sensing solutions for HDLR manipulators, which are striving toward increased automation levels in various work tasks. The focus is on visual perception and generic 6 DOF TCP pose estimation of HDLR manipulators in unknown (or unstructured) environments. Methods for increasing the robustness and reliability of visual perception systems are examined by exploiting sensor redundancy and data fusion. Vision-aided control using targetless, motion-based local calibration between an HDLR manipulator and a visual sensor is also proposed to improve the absolute positioning accuracy of the TCP despite the kinematic and non-kinematic errors present in the system. It is experimentally shown that a sub-centimeter TCP positioning accuracy was reliably achieved in the tested cases using a developed trajectory-matching-based method. Overall, this compendium thesis includes four publications and one unpublished manuscript related to these topics. Two main research problems, inspired by the industry, are considered and investigated in the presented publications. The outcome of this thesis provides insight into possible applications and benefits of advanced visual perception systems for HDLR manipulators in dynamic, unstructured environments. The main contribution is related to achieving sub-centimeter TCP positioning accuracy for an HDLR manipulator using a low-cost camera. The numerous challenges and complexities related to HDLR manipulators and visual sensing are also highlighted and discussed

Enabling the Development and Implementation of Digital Twins : Proceedings of the 20th International Conference on Construction Applications of Virtual Reality

Welcome to the 20th International Conference on Construction Applications of Virtual Reality (CONVR 2020). This year we are meeting on-line due to the current Coronavirus pandemic. The overarching theme for CONVR2020 is "Enabling the development and implementation of Digital Twins". CONVR is one of the world-leading conferences in the areas of virtual reality, augmented reality and building information modelling. Each year, more than 100 participants from all around the globe meet to discuss and exchange the latest developments and applications of virtual technologies in the architectural, engineering, construction and operation industry (AECO). The conference is also known for having a unique blend of participants from both academia and industry. This year, with all the difficulties of replicating a real face to face meetings, we are carefully planning the conference to ensure that all participants have a perfect experience. We have a group of leading keynote speakers from industry and academia who are covering up to date hot topics and are enthusiastic and keen to share their knowledge with you. CONVR participants are very loyal to the conference and have attended most of the editions over the last eighteen editions. This year we are welcoming numerous first timers and we aim to help them make the most of the conference by introducing them to other participants

Earth Resources: A continuing bibliography with indexes, issue 11, October 1976

This bibliography lists 714 reports, articles, and other documents introduced into the NASA scientific and technical information system between July 1976 and September 1976. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Geometric data understanding : deriving case specific features

There exists a tradition using precise geometric modeling, where uncertainties in data can be considered noise. Another tradition relies on statistical nature of vast quantity of data, where geometric regularity is intrinsic to data and statistical models usually grasp this level only indirectly. This work focuses on point cloud data of natural resources and the silhouette recognition from video input as two real world examples of problems having geometric content which is intangible at the raw data presentation. This content could be discovered and modeled to some degree by such machine learning (ML) approaches like deep learning, but either a direct coverage of geometry in samples or addition of special geometry invariant layer is necessary. Geometric content is central when there is a need for direct observations of spatial variables, or one needs to gain a mapping to a geometrically consistent data representation, where e.g. outliers or noise can be easily discerned. In this thesis we consider transformation of original input data to a geometric feature space in two example problems. The first example is curvature of surfaces, which has met renewed interest since the introduction of ubiquitous point cloud data and the maturation of the discrete differential geometry. Curvature spectra can characterize a spatial sample rather well, and provide useful features for ML purposes. The second example involves projective methods used to video stereo-signal analysis in swimming analytics. The aim is to find meaningful local geometric representations for feature generation, which also facilitate additional analysis based on geometric understanding of the model. The features are associated directly to some geometric quantity, and this makes it easier to express the geometric constraints in a natural way, as shown in the thesis. Also, the visualization and further feature generation is much easier. Third, the approach provides sound baseline methods to more traditional ML approaches, e.g. neural network methods. Fourth, most of the ML methods can utilize the geometric features presented in this work as additional features.Geometriassa käytetään perinteisesti tarkkoja malleja, jolloin datassa esiintyvät epätarkkuudet edustavat melua. Toisessa perinteessä nojataan suuren datamäärän tilastolliseen luonteeseen, jolloin geometrinen säännönmukaisuus on datan sisäsyntyinen ominaisuus, joka hahmotetaan tilastollisilla malleilla ainoastaan epäsuorasti. Tämä työ keskittyy kahteen esimerkkiin: luonnonvaroja kuvaaviin pistepilviin ja videohahmontunnistukseen. Nämä ovat todellisia ongelmia, joissa geometrinen sisältö on tavoittamattomissa raakadatan tasolla. Tämä sisältö voitaisiin jossain määrin löytää ja mallintaa koneoppimisen keinoin, esim. syväoppimisen avulla, mutta joko geometria pitää kattaa suoraan näytteistämällä tai tarvitaan neuronien lisäkerros geometrisia invariansseja varten. Geometrinen sisältö on keskeinen, kun tarvitaan suoraa avaruudellisten suureiden havainnointia, tai kun tarvitaan kuvaus geometrisesti yhtenäiseen dataesitykseen, jossa poikkeavat näytteet tai melu voidaan helposti erottaa. Tässä työssä tarkastellaan datan muuntamista geometriseen piirreavaruuteen kahden esimerkkiohjelman suhteen. Ensimmäinen esimerkki on pintakaarevuus, joka on uudelleen virinneen kiinnostuksen kohde kaikkialle saatavissa olevan datan ja diskreetin geometrian kypsymisen takia. Kaarevuusspektrit voivat luonnehtia avaruudellista kohdetta melko hyvin ja tarjota koneoppimisessa hyödyllisiä piirteitä. Toinen esimerkki koskee projektiivisia menetelmiä käytettäessä stereovideosignaalia uinnin analytiikkaan. Tavoite on löytää merkityksellisiä paikallisen geometrian esityksiä, jotka samalla mahdollistavat muun geometrian ymmärrykseen perustuvan analyysin. Piirteet liittyvät suoraan johonkin geometriseen suureeseen, ja tämä helpottaa luonnollisella tavalla geometristen rajoitteiden käsittelyä, kuten väitöstyössä osoitetaan. Myös visualisointi ja lisäpiirteiden luonti muuttuu helpommaksi. Kolmanneksi, lähestymistapa suo selkeän vertailumenetelmän perinteisemmille koneoppimisen lähestymistavoille, esim. hermoverkkomenetelmille. Neljänneksi, useimmat koneoppimismenetelmät voivat hyödyntää tässä työssä esitettyjä geometrisia piirteitä lisäämällä ne muiden piirteiden joukkoon