Path planning for automatic recharging system for steep-slope vineyard robots

Develop cost-effective ground robots for crop monitoring in steep slope vineyards is a complex challenge. The terrain presents harsh conditions for mobile robots and most of the time there is no one available to give support to the robots. So, a fully autonomous steep-slope robot requires a robust automatic recharging system. This work proposes a multilevel system that monitors a vineyard robot autonomy, to plan off-line the trajectory to the nearest recharging point and dock the robot on that recharging point considering visual tags. The proposed system called VineRecharge was developed to be deployed into a cost-effective robot with low computational power. Besides, this paper benchmarks several visual tags and detectors and integrates the best one into the VineRecharge system.This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project “POCI-01-0145-FEDER-006961”, and by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013.info:eu-repo/semantics/publishedVersio

In Depth Analysis of Power Balance, Handling, and the Traction Subsystem of an Articulated Skid-Steering Robot for Sustainable Agricultural Monitoring

This paper reports on the energy balance test performed on Agri.Q, an eight-wheel articulated robot intended to be a sustainable monitoring tool within the precision agriculture paradigm, and proposes an in-depth analysis of the traction subsystem in order to develop an appropriate traction allocation strategy to improve navigation through hilly or mountainous crops. Tests were conducted on the contribution of the orientable photovoltaic panel to the mission duration and overall sustainability, showing that a suitable mission plan, including dedicated charging phases, could significantly increase the robot’s operating time. A series of simulations of circular trajectories of different curvature and at different longitudinal velocities on flat ground were performed, with the aim of mapping the robot’s behaviour at steady state. The results of the simulations were analysed, paying particular attention to the required torques, manoeuvrability and forces exchanged on the ground. The simulations conducted demonstrated and extended previous results obtained on similar robotic architectures, which suffer from significant understeer behaviour due to significant lateral wheel slip during turning. They also showed the limitations of currently employed traction motors, but also the advantages of a proper traction allocation strategy involving the rear module. Article highlights. Agri.Q energy balance tests have been carried out to assess its endurance and sustainability The traction and handling behaviours of Agri.Q were mapped and discussed in detail in order to improve them Agri.Q has proven to be a basis for the future implementation of precision agriculture to advance the SDG

Perception for context awareness of agricultural robots

Context awareness is one key point for the realisation of robust autonomous systems in unstructured environments like agriculture. Robots need a precise description of their environment so that tasks could be planned and executed correctly. When using a robot system in a controlled, not changing environment, the programmer maybe could model all possible circumstances to get the system reliable. However, the situation gets more complex when the environment and the objects are changing their shape, position or behaviour. Perception for context awareness in agriculture means to detect and classify objects of interest in the environment correctly and react to them. The aim of this cumulative dissertation was to apply different strategies to increase context awareness with perception in mobile robots in agriculture. The objectives of this thesis were to address five aspects of environment perception: (I) test static local sensor communication with a mobile vehicle, (II) detect unstructured objects in a controlled environment, (III) describe the influence of growth stage to algorithm outcomes, (IV) use the gained sensor information to detect single plants and (V) improve the robustness of algorithms under noisy conditions. First, the communication between a static Wireless Sensor Network and a mobile robot was investigated. The wireless sensor nodes were able to send local data from sensors attached to the systems. The sensors were placed in a vineyard and the robot followed automatically the row structure to receive the data. It was possible to localize the single nodes just with the exact robot position and the attenuation model of the received signal strength with triangulation. The precision was 0.6 m and more precise than a provided differential global navigation satellite system signal. The second research area focused on the detection of unstructured objects in point clouds. Therefore, a low-cost sonar sensor was attached to a 3D-frame with millimetre level accuracy to exactly localize the sensor position. With the sensor position and the sensor reading, a 3D point cloud was created. In the workspace, 10 individual plant species were placed. They could be detected automatically with an accuracy of 2.7 cm. An attached valve was able to spray these specific plant positions, which resulted in a liquid saving of 72%, compared to a conventional spraying method, covering the whole crop row area. As plants are dynamic objects, the third objective of describing the plant growth with adequate sensor data, was important to characterise the unstructured agriculture domain. For revering and testing algorithms to the same data, maize rows were planted in a greenhouse. The exact positions of all plants were measured with a total station. Then a robot vehicle was guided through the crop rows and the data of attached sensors were recorded. With the help of the total station, it was possible to track down the vehicle position and to refer all data to the same coordinate frame. The data recording was performed over 7 times over a period of 6 weeks. This created datasets could afterwards be used to assess different algorithms and to test them against different growth changes of the plants. It could be shown that a basic RANSAC line following algorithm could not perform correctly under all growth stages without additional filtering. The fourth paper used this created datasets to search for single plants with a sensor normally used for obstacle avoidance. One tilted laser scanner was used with the exact robot position to create 3D point clouds, where two different methods for single plant detection were applied. Both methods used the spacing to detect single plants. The second method used the fixed plant spacing and row beginning, to resolve the plant positions iteratively. The first method reached detection rates of 73.7% and a root mean square error of 3.6 cm. The iterative second method reached a detection rate of 100% with an accuracy of 2.6 - 3.0 cm. For assessing the robustness of the plant detection, an algorithm was used to detect the plant positions in six different growth stages of the given datasets. A graph-cut based algorithm was used, what improved the results for single plant detection. As the algorithm was not sensitive against overlaying and noisy point clouds, a detection rate of 100% was realised, with an accuracy for the estimated height of the plants with 1.55 cm. The stem position was resolved with an accuracy of 2.05 cm. This thesis showed up different methods of perception for context awareness, which could help to improve the robustness of robots in agriculture. When the objects in the environment are known, it could be possible to react and interact smarter with the environment as it is the case in agricultural robotics. Especially the detection of single plants before the robot reaches them could help to improve the navigation and interaction of agricultural robots.Kontextwahrnehmung ist eine Schlüsselfunktion für die Realisierung von robusten autonomen Systemen in einer unstrukturierten Umgebung wie der Landwirtschaft. Roboter benötigen eine präzise Beschreibung ihrer Umgebung, so dass Aufgaben korrekt geplant und durchgeführt werden können. Wenn ein Roboter System in einer kontrollierten und sich nicht ändernden Umgebung eingesetzt wird, kann der Programmierer möglicherweise ein Modell erstellen, welches alle möglichen Umstände einbindet, um ein zuverlässiges System zu erhalten. Jedoch wird dies komplexer, wenn die Objekte und die Umwelt ihr Erscheinungsbild, Position und Verhalten ändern. Umgebungserkennung für Kontextwahrnehmung in der Landwirtschaft bedeutet relevante Objekte in der Umgebung zu erkennen, zu klassifizieren und auf diese zu reagieren. Ziel dieser kumulativen Dissertation war, verschiedene Strategien anzuwenden, um das Kontextbewusstsein mit Wahrnehmung bei mobilen Robotern in der Landwirtschaft zu erhöhen. Die Ziele dieser Arbeit waren fünf Aspekte von Umgebungserkennung zu adressieren: (I) Statische lokale Sensorkommunikation mit einem mobilen Fahrzeug zu testen, (II) unstrukturierte Objekte in einer kontrollierten Umgebung erkennen, (III) die Einflüsse von Wachstum der Pflanzen auf Algorithmen und ihre Ergebnisse zu beschreiben, (IV) gewonnene Sensorinformation zu benutzen, um Einzelpflanzen zu erkennen und (V) die Robustheit von Algorithmen unter verschiedenen Fehlereinflüssen zu verbessern. Als erstes wurde die Kommunikation zwischen einem statischen drahtlosen Sensor-Netzwerk und einem mobilen Roboter untersucht. Die drahtlosen Sensorknoten konnten Daten von lokal angeschlossenen Sensoren übermitteln. Die Sensoren wurden in einem Weingut verteilt und der Roboter folgte automatisch der Reihenstruktur, um die gesendeten Daten zu empfangen. Es war möglich, die Sendeknoten mithilfe von Triangulation aus der exakten Roboterposition und eines Sendesignal-Dämpfung-Modells zu lokalisieren. Die Genauigkeit war 0.6 m und somit genauer als das verfügbare Positionssignal eines differential global navigation satellite system. Der zweite Forschungsbereich fokussierte sich auf die Entdeckung von unstrukturierten Objekten in Punktewolken. Dafür wurde ein kostengünstiger Ultraschallsensor auf einen 3D Bewegungsrahmen mit einer Millimeter Genauigkeit befestigt, um die genaue Sensorposition bestimmen zu können. Mit der Sensorposition und den Sensordaten wurde eine 3D Punktewolke erstellt. Innerhalb des Arbeitsbereichs des 3D Bewegungsrahmens wurden 10 einzelne Pflanzen platziert. Diese konnten automatisch mit einer Genauigkeit von 2.7 cm erkannt werden. Eine angebaute Pumpe ermöglichte das punktuelle Besprühen der spezifischen Pflanzenpositionen, was zu einer Flüssigkeitsersparnis von 72%, verglichen mit einer konventionellen Methode welche die gesamte Pflanzenfläche benetzt, führte. Da Pflanzen sich ändernde Objekte sind, war das dritte Ziel das Pflanzenwachstum mit geeigneten Sensordaten zu beschreiben, was wichtig ist, um unstrukturierte Umgebung der Landwirtschaft zu charakterisieren. Um Algorithmen mit denselben Daten zu referenzieren und zu testen, wurden Maisreihen in einem Gewächshaus gepflanzt. Die exakte Position jeder einzelnen Pflanze wurde mit einer Totalstation gemessen. Anschließend wurde ein Roboterfahrzeug durch die Reihen gelenkt und die Daten der angebauten Sensoren wurden aufgezeichnet. Mithilfe der Totalstation war es möglich, die Fahrzeugposition zu ermitteln und alle Daten in dasselbe Koordinatensystem zu transformieren. Die Datenaufzeichnungen erfolgten 7-mal über einen Zeitraum von 6 Wochen. Diese generierten Datensätze konnten anschließend benutzt werden, um verschiedene Algorithmen unter verschiedenen Wachstumsstufen der Pflanzen zu testen. Es konnte gezeigt werden, dass ein Standard RANSAC Linien Erkennungsalgorithmus nicht fehlerfrei arbeiten kann, wenn keine zusätzliche Filterung eingesetzt wird. Die vierte Publikation nutzte diese generierten Datensätze, um nach Einzelpflanzen mithilfe eines Sensors zu suchen, der normalerweise für die Hinderniserkennung benutzt wird. Ein gekippter Laserscanner wurde zusammen mit der exakten Roboterposition benutzt, um eine 3D Punktewolke zu generieren. Zwei verschiedene Methoden für Einzelpflanzenerkennung wurden angewendet. Beide Methoden nutzten Abstände, um die Einzelpflanzen zu erkennen. Die zweite Methode nutzte den bekannten Pflanzenabstand und den Reihenanfang, um die Pflanzenpositionen iterativ zu erkennen. Die erste Methode erreichte eine Erkennungsrate von 73.7% und damit einen quadratischen Mittelwertfehler von 3.6 cm. Die iterative zweite Methode erreichte eine Erkennungsrate von bis zu 100% mit einer Genauigkeit von 2.6-3.0 cm. Um die Robustheit der Pflanzenerkennung zu bewerten, wurde ein Algorithmus zur Erkennung von Einzelpflanzen in sechs verschiedenen Wachstumsstufen der Datasets eingesetzt. Hier wurde ein graph-cut basierter Algorithmus benutzt, welcher die Robustheit der Ergebnisse für die Einzelpflanzenerkennung erhöhte. Da der Algorithmus nicht empfindlich gegen ungenaue und fehlerhafte Punktewolken ist, wurde eine Erkennungsrate von 100% mit einer Genauigkeit von 1.55 cm für die Höhe der Pflanzen erreicht. Der Stiel der Pflanzen wurde mit einer Genauigkeit von 2.05 cm erkannt. Diese Arbeit zeigte verschiedene Methoden für die Erkennung von Kontextwahrnehmung, was helfen kann, um die Robustheit von Robotern in der Landwirtschaft zu erhöhen. Wenn die Objekte in der Umwelt bekannt sind, könnte es möglich sein, intelligenter auf die Umwelt zu reagieren und zu interagieren, wie es aktuell der Fall in der Landwirtschaftsrobotik ist. Besonders die Erkennung von Einzelpflanzen bevor der Roboter sie erreicht, könnte helfen die Navigation und Interaktion von Robotern in der Landwirtschaft verbessern

Optimization and Communication in UAV Networks

UAVs are becoming a reality and attract increasing attention. They can be remotely controlled or completely autonomous and be used alone or as a fleet and in a large set of applications. They are constrained by hardware since they cannot be too heavy and rely on batteries. Their use still raises a large set of exciting new challenges in terms of trajectory optimization and positioning when they are used alone or in cooperation, and communication when they evolve in swarm, to name but a few examples. This book presents some new original contributions regarding UAV or UAV swarm optimization and communication aspects

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application

Políticas de Copyright de Publicações Científicas em Repositórios Institucionais: O Caso do INESC TEC

A progressiva transformação das práticas científicas, impulsionada pelo desenvolvimento das novas Tecnologias de Informação e Comunicação (TIC), têm possibilitado aumentar o acesso à informação, caminhando gradualmente para uma abertura do ciclo de pesquisa. Isto permitirá resolver a longo prazo uma adversidade que se tem colocado aos investigadores, que passa pela existência de barreiras que limitam as condições de acesso, sejam estas geográficas ou financeiras. Apesar da produção científica ser dominada, maioritariamente, por grandes editoras comerciais, estando sujeita às regras por estas impostas, o Movimento do Acesso Aberto cuja primeira declaração pública, a Declaração de Budapeste (BOAI), é de 2002, vem propor alterações significativas que beneficiam os autores e os leitores. Este Movimento vem a ganhar importância em Portugal desde 2003, com a constituição do primeiro repositório institucional a nível nacional. Os repositórios institucionais surgiram como uma ferramenta de divulgação da produção científica de uma instituição, com o intuito de permitir abrir aos resultados da investigação, quer antes da publicação e do próprio processo de arbitragem (preprint), quer depois (postprint), e, consequentemente, aumentar a visibilidade do trabalho desenvolvido por um investigador e a respetiva instituição. O estudo apresentado, que passou por uma análise das políticas de copyright das publicações científicas mais relevantes do INESC TEC, permitiu não só perceber que as editoras adotam cada vez mais políticas que possibilitam o auto-arquivo das publicações em repositórios institucionais, como também que existe todo um trabalho de sensibilização a percorrer, não só para os investigadores, como para a instituição e toda a sociedade. A produção de um conjunto de recomendações, que passam pela implementação de uma política institucional que incentive o auto-arquivo das publicações desenvolvidas no âmbito institucional no repositório, serve como mote para uma maior valorização da produção científica do INESC TEC.The progressive transformation of scientific practices, driven by the development of new Information and Communication Technologies (ICT), which made it possible to increase access to information, gradually moving towards an opening of the research cycle. This opening makes it possible to resolve, in the long term, the adversity that has been placed on researchers, which involves the existence of barriers that limit access conditions, whether geographical or financial. Although large commercial publishers predominantly dominate scientific production and subject it to the rules imposed by them, the Open Access movement whose first public declaration, the Budapest Declaration (BOAI), was in 2002, proposes significant changes that benefit the authors and the readers. This Movement has gained importance in Portugal since 2003, with the constitution of the first institutional repository at the national level. Institutional repositories have emerged as a tool for disseminating the scientific production of an institution to open the results of the research, both before publication and the preprint process and postprint, increase the visibility of work done by an investigator and his or her institution. The present study, which underwent an analysis of the copyright policies of INESC TEC most relevant scientific publications, allowed not only to realize that publishers are increasingly adopting policies that make it possible to self-archive publications in institutional repositories, all the work of raising awareness, not only for researchers but also for the institution and the whole society. The production of a set of recommendations, which go through the implementation of an institutional policy that encourages the self-archiving of the publications developed in the institutional scope in the repository, serves as a motto for a greater appreciation of the scientific production of INESC TEC

Aplicación de algoritmos Deep Policy Iteration en vehículos acuáticos para el patrullaje autónomo de escenarios medioambientales

En este trabajo se ha realizado un estudio de la implantación de la metodología Deep Reinforcement Learning, específicamente algoritmos policy gradient “on policy”, en la resolución de un problema de explotación y toma de muestras en un entorno acuático definido como emplazamiento de experimentación. Se han aplicado algoritmos de esta familia como el REINFORCE y el PPO, donde tras realizar un proceso de sintonización de sus parámetros se ha extraído los modelos obtenidos en cada caso para su posterior simulación. Se han comprobado las planificaciones de ruta desarrolladas por cada algoritmo, así como una comparativa con otras técnicas más usualmente empleadas en navegación automática autónoma. Este estudio trata de continuar una investigación presente de la Escuela Técnica Superior de Ingeniería de Sevilla para la evaluación y monitorización de los parámetros que caracterizan el estado biológico del lago Ypacaraí en la que se plantea el uso de técnicas de aprendizaje por refuerzo “off_policy”. Mediante el uso de un entorno que recrea las características físicas del medio real se aplicarán las técnicas comentadas para garantizar una cobertura, navegación y recogida de datos autónoma optimizadas.In this work we have carried out a study of the implementation of the Deep Reinforcement Learning methodology, specifically "on-policy" policy gradient algorithms, in the resolution of an exploitation and sampling problem in an aquatic environment defined as an experimental site. Algorithms from this family, such as REINFORCE and PPO, have been applied, where, after a process of tuning their parameters, the models obtained in each case have been extracted for subsequent simulation. The route planning developed by each algorithm has been checked, as well as a comparison with other techniques more commonly used in autonomous automatic navigation. This study is a continuation of a current research project of the Escuela Técnica Superior de Ingeniería de Sevilla for the evaluation and monitoring of the parameters that characterize the biological state of Lake Ypacaraí, in which the use of "off-policy" reinforcement learning techniques is proposed. Using an environment that recreates the physical characteristics of the real environment, the techniques will be applied to guarantee optimized coverage, navigation and autonomous data collection.Universidad de Sevilla. Grado en Ingeniería de las Tecnologías Industriale