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This paper studies the relation between node deletion and algebraic connectivity for graphs with a hierarchical structure represented by layers. To capture this structure, the concepts of layered path graph and its (sub)graph cone are introduced. The problem is motivated by a mobile robot formation control guided by a leader. In particular, we consider a scenario in which robots may leave the network resulting in the removal of the nodes and the associated edges. We show that the existence of at least one neighbor in the upper layer is crucial for the algebraic connectivity not to deteriorate by node deletion.

The capabilities of autonomous mobile robotic systems have been steadily improving due to recent advancements in computer science, engineering, and related disciplines such as cognitive science. In controlled environments, robots have achieved relatively high levels of autonomy. In more unstructured environments, however, the development of fully autonomous mobile robots remains challenging due to the complexity of understanding these environments. Many autonomous mobile robots use classical, learning-based or hybrid approaches for navigation. More recent learning-based methods may replace the complete navigation pipeline or selected stages of the classical approach. For effective deployment, autonomous robots must understand their external environments at a sophisticated level according to their intended applications. Therefore, in addition to robot perception, scene analysis and higher-level scene understanding (e.g., traversable/non-traversable, rough or smooth terrain, etc.) are required for autonomous robot navigation in unstructured outdoor environments. This paper provides a comprehensive review and critical analysis of these methods in the context of their applications to the problems of robot perception and scene understanding in unstructured environments and the related problems of localisation, environment mapping and path planning. State-of-the-art sensor fusion methods and multimodal scene understanding approaches are also discussed and evaluated within this context. The paper concludes with an in-depth discussion regarding the current state of the autonomous ground robot navigation challenge in unstructured outdoor environments and the most promising future research directions to overcome these challenges.

The effect produced by the coupling of trailers on thenavigability of mobile robots when moving on inclined surfaces has notbeen extensively studied. Therefore, this research studies the influence ofsome factors related to the morphology of the trailers on tip-over stability,steering capacity and propensity to slip in aSkid Steermobile robot thathas two coupled trailers. Specifically and based on simulation results, thefollowing factors were considered: type of articulation in the coupling,number of coupled bodies, length of the trailers and relative orientationbetween the trailers and the tractor. This, with the purpose of providingthe developers of robot-trailer systems with guiding parameters thatallow them to configure these systems more efficiently, and achieve betterperformance in terms of navigability., El efecto que produce el acoplamiento de remolques sobre lanavegabilidad de robots móviles cuando se desplazan sobre superficiesinclinadas no ha sido muy estudiado. Por tanto, en esta investigaciónse estudia la influencia de algunos factores relacionados con la morfo-logía de los remolques sobre la estabilidad al vuelco, la capacidad dedireccionamiento y la propensión al deslizamiento en un robot móvilSkid Steerque posee dos remolques acoplados. Específicamente, y conbase en resultados de simulación, se consideraron los siguientes factores:tipo de articulación en el acoplamiento, cantidad de cuerpos acoplados,longitud de los remolques y orientación relativa entre los remolques y eltractor. Esto con el propósito de brindar a los desarrolladores de sistemasrobot-remolques, parámetros orientativos que les permita configurar estossistemas de manera más eficiente y lograr un mejor desempeño en cuantoa la navegabilidad se refiere.

[Resumen] Junto con el desarrollo de la industria 4.0 y la inclusión de procesos distribuidos y flexibles en diferentes arquitecturas como puede ser la de fabricación en enjambre, se han realizado avances en las funciones de los robots móviles que pueden aparecer en dichos escenarios tales como los AGV o los AMR, para poder trabajar en entornos con humanos de forma eficiente. Además de reducir el consumo de las actividades de flotas de estos dispositivos, proveyendo a la industria de soluciones para optimizar los procesos de fabricación (principalmente en aspecto de suministro de recursos mediante tareas de logística). En este artículo se realiza un estudio del estado de la materia en los distintos campos que engloban las funciones de las flotas de robots móviles: Asignación de Tareas, Planificación de Trayectorias y Planificación del Movimiento, centrándose en los últimos avances en la creación óptima de trayectorias y en el desempeño en entornos con humanos., [Abstract] Together with the development of Industry 4.0 and the introduction of flexible and distributed manufacturing processes into different architectures such as Swarm Manufacturing, there have been several research progresses about the main functionalities of mobile robot fleets. These groups of end devices are present in today’s main industry-based scenarios, in terms of AGVs and AMRs to work within human centered activities, such as intra-logistics. In this article, it is presented an overview about the startof-the-art in the main functionalities of industry mobile robot fleets: Task Allocation, Path Planning and Motion Planning. However, the principal topic where this paper is focused on is Path Planning. The most promising advances in this area are analyzed and discussed targeting the optimization of trajectories and the capacity to move alongside with humans.

[Resumen] Los Vehículos Autónomos Guiados (AGV) son cada vez más populares en lo que a logística interna de las fábricas se refiere debido a su capacidad para transportar cargas pesadas y su alto grado de autonomía. No obstante, la dinámica de estos robots puede sufrir cambios debido a variaciones en la carga que transportan y/o a desgaste mecánico, lo cual implica una mayor complejidad en el control de velocidad. En muchas ocasiones se emplean controladores de tipo Proporcional Integral (PI) para dicho control. Sin embargo, este controlador exige un ajuste fino y carece de suficiente robustez ante variaciones de sus condiciones de trabajo. Con el fin de mejorar el rendimiento del control de velocidad, en este artículo se presenta el diseño de un neuro-controlador. Dado que encontrar unos valores óptimos para los hiperparámetros de aprendizaje puede ser difícil y requiere múltiples pruebas y ajustes, se opta por utilizar un Algoritmo Genético (AG) para buscar una solución válida de entre las óptimas., [Abstract] Autonomous Guided Vehicles (AGVs) are becoming increasingly popular in terms of internal factory logistics due to their ability to transport heavy loads and their high degree of autonomy. Nevertheless, the dynamics of these robots can undergo changes due to variations in their load and/or mechanical wear, which involves greater complexity in their speed control. Proportional Integral (PI) controllers are often used for this control. However, this controller requires fine tuning and lacks enough robustness against variations in working conditions. In order to improve the speed control performance, this article presents the design of a neuro-controller. Since finding optimal values for the learning hyperparameters can be difficult and requires multiple tests and adjustments, a Genetic Algorithm (GA) is used to find a valid solution among all the optimal.

[Resumen] La robótica ha mejorado nuestra calidad de vida al interactuar con el entorno de formas innovadoras, siendo la microrrobótica capaz de realizar tareas específicas y delicadas en áreas inaccesibles para dispositivos convencionales, como la medicina. El objetivo de este trabajo es diseñar, fabricar y controlar un robot movido por cabeceo con la ayuda de un flagelo pasivo utilizando simulaciones computacionales, modelos matemáticos y controlador previos. Este robot servirá como prototipo para estudios iniciales sobre microrrobótica. El robot se fabricó utilizando impresión 3D y se utilizó electrónica y motores específicos para generar la propulsión. Se realizaron experimentos de natación con y sin flagelo. El movimiento del prototipo sin flagelo durante la natación tiende a descontrolarse rápidamente, mientras que el que posee el flagelo, se mantiene recto. Estos resultados concuerdan con las simulaciones realizadas en trabajos previos., [Abstract] Robotics has improved our quality of life by interacting with the environment in innovative ways, with microrobotics being able to perform specific and delicate tasks in areas inaccessible to conventional devices, such as medicine. The aim of this work is to design, manufacture and control a robot moved by pitching with the help of a passive flagellum using computational simulations, mathematical models and a previous controller. This robot will serve as a prototype for initial studies on microrobotics. The robot was fabricated using 3D printing and specific electronics and motors were used to generate propulsion. Swimming experiments were conducted with and without a flagellum. The movement of the prototype without the flagellum during swimming tends to rapidly get out of control, while the one with the flagellum stays straight. These results are in agreement with simulations carried out in previous work.

[Resumen] Hoy en día se están investigando robots de exploración terrestre y espacial más rápidos en respuesta a la creciente demanda de capacidades de exploración e investigación más rápidas, eficaces y rentables. Para estas plataformas móviles rápidas la identificación y evasión de obstáculos lejanos son críticas, ya que su alta velocidad implica la necesidad de tener en cuenta el mayor número posible de obstáculos cercanos y lejanos para el cálculo de la trayectoria global, evitando cualquier posible accidente debido a su velocidad y al tiempo de cálculo de los algoritmos de replanificación. Debido a su distancia, los obstáculos lejanos no se incluyen en los mapas locales, que están limitados por el alcance de las cámaras de profundidad. Por estas razones, este artículo propone el uso de técnicas de Inteligencia Artificial para detectarlos a partir de imágenes y estimar sus tamaños y posiciones con un cierto grado de incertidumbre. Los obstáculos detectados se incluirán posteriormente en los mapas globales, corrigiendo la trayectoria global en caso de colisionar con ellos., [Abstract] Nowadays faster terrestrial and space exploration robots are being investigated, in response to the growing demand for faster, more efficient, and cost-effective exploration and research apabilities. For these rapid mobile platforms, the identification and avoidance of far obstacles are critical, since their high speed implies the need to take into account as many near and far obstacles as possible for the global path computation, avoiding any possible accident due to their speed and the computation time of the replanning algorithms. Due to their distance, the far obstacles are not included within the local maps, which are limited by the range of the depth cameras. For these reasons, this paper proposes the use of Artificial Intelligence techniques to detect them from images and estimate their sizes and positions with a certain degree of uncertainty. The detected obstacles will be later included in the global maps, correcting the global path in case it collides with them.

[Resumen] En los últimos años ha aumentado el interés en el desarrollo de sistemas de medición para robots móviles, entre los que se encuentran los sistemas táctiles basados en antenas sensoras bioinspiradas en los bigotes y antenas de animales e insectos. En este trabajo se estudia un robot móvil con este tipo de sistemas. En concreto, se desarrolla el modelo dinámico del conjunto plataforma móvil y antena sensora que conforman el robot, considerando el movimiento del conjunto comprendido en el plano y teniendo en cuenta el efecto de la gravedad. Para ello se aplica el principio de Hamilton, obteniendo las ecuaciones de movimiento para la plataforma móvil y el planteamiento del problema de condiciones de contorno para la antena. Posteriormente, mediante el análisis modal se llega a una solución única del modelo de la antena sensora, el cual se valida mediante el software Adams al definir unas trayectorias de referencia del robot a partir de sus limitaciones físicas., [Abstract] In recent years, there has been a growing interest in the development of measurement systems for mobile robots, including tactile systems based on bioinspired sensor antennas, inspired by the whiskers and antennas of animals and insects. This work focuses on studying a mobile robot equipped with such systems. Specifically, a dynamic model for the mobile platform and sensor antenna that comprise the robot is developed, considering the planar motion of the system and taking into account the gravity effect. The extended Hamilton principle is applied to derive the equations of motion for the mobile platform, while the boundary-value problem is formulated for the antenna. Subsequently, by means of modal analysis, a unique solution for the sensor antenna model is obtained and validated using Adams software by defining reference trajectories for the robot, considering its physical limitations.

[Resumen] La obtención de modelos 3D precisos es importante en robótica. Una amplia gama de aplicaciones como mapear objetos requieren de un conocimiento preciso de su forma y disposición. Sin embargo, puede ser difícil lograr una alta precisión, especialmente cuando se trata de datos reales que incluyen factores como oclusiones, desorden y ruido, influyendo en el resultado final. En el estado del arte, se combina deep learning en imágenes 2D con segmentación de nubes de puntos. Sin embargo, los estudios comparativos al respecto son muy limitados. En este trabajo, implementamos y comparamos varios métodos existentes, aplicando mejoras para que estos funcionen correctamente en entornos complejos. Analizamos cuatro formas de obtener modelos 3D, tres de ellos basados en detección por bounding box y un cuarto basado en la obtención de una máscara mediante instance segmentation. Se realiza una comparación tanto cualitativa como cuantitativa, presentando las fortalezas y los límites de cada método, además de un ejemplo práctico de uso., [Abstract] Obtaining accurate 3D models is important in robotics. A wide range of applications such as mapping objects require precise knowledge of their shape and layout. However, it can be difficult to achieve high accuracy, especially when it comes to real data that includes factors such as occlusions, clutter and noise, influencing the final result. In the state of the art, deep learning on 2D images is combined with 3D point cloud segmentation. However, comparative studies in this area are very limited. In this paper, we implement and compare several existing methods, applying improvements to make them work properly in complex environments. We analyze four ways to obtain 3D models, three of them based on bounding box detection and a fourth one based on obtaining a mask by instance segmentation. A qualitative and quantitative comparison is made, presenting the advantages and disadvantages of each method, as well as a practical example of use.

[Resumen] La localización y la detección de anomalías son desafíos importantes en la videovigilancia y en la detección de fallos. Considere el caso de un vehículo que patrulla en una estación de tren: este necesita saber dónde se encuentra e identificar las posibles anomalías, como son, un equipaje abandonado o comportamientos humanos sospechosos. En este artículo, abordamos la localización y la detección de anomalías mediante un marco bayesiano y modelos de aprendizaje profundo. Donde asumimos que el vehículo se mueve en un entorno conocido. Durante la fase de entrenamiento, se construye una Red Bayesiana Dinámica Acoplada a través del agrupamiento de los datos de odometría y el aprendizaje de un Autocodificador Variacional de Kalman utilizando las imágenes de una cámara. Posteriormente, en la fase de prueba, solo se proporcionan datos de video. El enfoque propuesto plantea el uso de un Filtro de Partículas de Salto de Markov Acoplado que aprovecha dicha Red Bayesiana Dinámica para extraer anomalías y localizar el vehículo. Por tanto, las anomalías son tanto una salida del método como un resultado intermedio para guiar el proceso de localización. En este trabajo hemos evaluado el método propuesto en dos conjuntos de datos reales., [Abstract] Localization and anomaly detection are important challenges in video surveillance and fault detection. Consider the case of a vehicle patrolling a train station: it needs to know where it is located and identify anomalies such as abandoned luggage or suspicious human behavior. In this article, we tackle localization and anomaly detection using a Bayesian framework and Deep Learning models.We assume that the vehicle is moving in a known environment. During the training phase of the model, a Coupled Dynamic Bayesian Network is built by clustering the odometry data and by learning a Kalman Variational Autoencoder on the camera data. Subsequently, in the testing phase, only video data is provided. A Coupled Markov Jump Particle Filter leverages the Dynamic Bayesian Network to extract anomalies and localize the vehicle. Anomalies are both an output of the method and an intermediate result to guide the localization process. We evaluate the method on two real-world datasets.

La deteccion y evitación de obstáculos es un componente importante de la robótica móvil. En este artículo, empleamos unicamente un sensor LIDAR 2D para segmentar el entornos en líneas, clasificandolas en suelo, obstaculos o baches. Para ello, el sensor fue colocado inclinado hacia el suelo para poder detectar obstaculos y baches de diferentes alturas. La geometrıa de los puntos detectados es analizada para segmentarlos en lıneas, generando un mapa 3D con estas. Se generan distintas trayectorias del veh ́ıculo y, dependiendo de la altura deducida a partir del mapa, las areas se consideran o no transitable. El m ́ etodo detecta correctamente áreas transitables en terrenos con pendientes mientras el robot se mueve. El objetivo se ha conseguido utilizando un sensor economico en oposicion de otros metodos que utilizan sistemas mas costosos., Detecting and evading obstacles are an important component in mobile robots. In this paper, we use only a 2D LIDAR sensor to segment the environment in lines, classifying them into the ground, obstacles and potholes. Therefore, the sensor was placed downward looking to detect potholes and obstacles with different heights. The geometry of detected points is analysed in order to segment them into lines, and a 3D map is generated with them. Different trajectories of the vehicle are generated and, depending on the height deduced from the map, the areas are considered or not trafficable. The method successfully detects the trafficable áreas in sloping terrains while the robot is moving. The objective has been achieved by using an economic sensor as opposed to other solutions that use more expensive systems.

The capability of a mobile robot to efficiently and safely perform complex missions is limited by its knowledge of the environment, namely the situation. Advanced reasoning, decision-making, and execution skills enable an intelligent agent to act autonomously in unknown environments. Situational Awareness (SA) is a fundamental capability of humans that has been deeply studied in various fields, such as psychology, military, aerospace, and education. Nevertheless, it has yet to be considered in robotics, which has focused on single compartmentalized concepts such as sensing, spatial perception, sensor fusion, state estimation, and Simultaneous Localization and Mapping (SLAM). Hence, the present research aims to connect the broad multidisciplinary existing knowledge to pave the way for a complete SA system for mobile robotics that we deem paramount for autonomy. To this aim, we define the principal components to structure a robotic SA and their area of competence. Accordingly, this paper investigates each aspect of SA, surveying the state-of-the-art robotics algorithms that cover them, and discusses their current limitations. Remarkably, essential aspects of SA are still immature since the current algorithmic development restricts their performance to only specific environments. Nevertheless, Artificial Intelligence (AI), particularly Deep Learning (DL), has brought new methods to bridge the gap that maintains these fields apart from the deployment to real-world scenarios. Furthermore, an opportunity has been discovered to interconnect the vastly fragmented space of robotic comprehension algorithms through the mechanism of Situational Graph (S-Graph), a generalization of the well-known scene graph. Therefore, we finally shape our vision for the future of robotic situational awareness by discussing interesting recent research directions.

The capability of a mobile robot to efficiently and safely perform complex missions is limited by its knowledge of the environment, namely the situation. Advanced reasoning, decision-making, and execution skills enable an intelligent agent to act autonomously in unknown environments. Situational Awareness (SA) is a fundamental capability of humans that has been deeply studied in various fields, such as psychology, military, aerospace, and education. Nevertheless, it has yet to be considered in robotics, which has focused on single compartmentalized concepts such as sensing, spatial perception, sensor fusion, state estimation, and Simultaneous Localization and Mapping (SLAM). Hence, the present research aims to connect the broad multidisciplinary existing knowledge to pave the way for a complete SA system for mobile robotics that we deem paramount for autonomy. To this aim, we define the principal components to structure a robotic SA and their area of competence. Accordingly, this paper investigates each aspect of SA, surveying the state-of-the-art robotics algorithms that cover them, and discusses their current limitations. Remarkably, essential aspects of SA are still immature since the current algorithmic development restricts their performance to only specific environments. Nevertheless, Artificial Intelligence (AI), particularly Deep Learning (DL), has brought new methods to bridge the gap that maintains these fields apart from the deployment to real-world scenarios. Furthermore, an opportunity has been discovered to interconnect the vastly fragmented space of robotic comprehension algorithms through the mechanism of Situational Graph (S-Graph), a generalization of the well-known scene graph. Therefore, we finally shape our vision for the future of robotic situational awareness by discussing interesting recent research directions.

Electric transmission lines suffer from many hazards, including wind-induced vibrations (WIV), which can lead to fatigue failure of the transmission conductors. Current vibration mitigation methods do not adequately address WIV because they overwhelmingly rely on narrow-band fixed absorbers. A mobile damping robot (MDR) can overcome the limitations of these fixed absorbers by actively transporting them to locations of highest amplitude on the cable; i.e., antinodes. These antinodes are where the absorbers can most efficiently remove energy from the system. While analyses have been performed for vibration absorbers on transmission line conductors, they have not been in the context of a mobile damping robot (MDR). There is a need to investigate the potential impact of the MDR on a transmission line and the resulting implications for the MDR's development. In this thesis, we explore the dynamics of a power line conductor through finite element analysis (FEA) and modal testing. We perform numerical analysis in MATLAB using equations of motion obtained via Hamilton's Principle. We discuss the design and validation of an appropriate test bench and MDR prototype. We also experimentally investigate the ability of the MDR prototype to transport a mass along a conductor to antinode locations. Experimental results indicate that the damping robot is indeed able to navigate to cable locations of highest amplitude corresponding to antinodes. We then conclude and discuss future work. The insights gained from this research lay a foundation to guide further development of the MDR. Through this work, we are better able to define the operating conditions of the MDR, which will facilitate the creation of a more robust, adaptable control framework for expanded capability.

El proyecto tiene por objetivo la implementación de un módulo de detección y seguimiento de personas que permite el guiado de un robot móvil dotado de un sensor LIDAR. El proyecto está implementado en ROS y consta de dos partes principales, el primero es el módulo de detección de personas y el segundo es el módulo de navegación que permite al robot seguir a la persona que ha detectado. El primer módulo toma la nube de puntos que proporciona el sensor LIDAR y lo procesa en tres etapas. La primera consiste en delimitar la nube de puntos, retirando aquellos puntos cercanos al suelo y techo. En la segunda etapa se agrupan aquellos puntos que representan a objetos candidatos y se extraen sus características. En la última etapa se clasifican los objetos candidatos, según sus características, en humanos y no humanos haciendo uso de un clasificador basado en máquina de soporte vectorial (SVM). El segundo módulo calcula la orientación de la persona detectada, y planifica la trayectoria que seguirá el robot en función de la posición y orientación de la persona detectada.

El propósito de este trabajo es el de realizar un planificador de trayectorias para un vehículo autónomo. En concreto, el estudio se focaliza en el desarrollo de un supervisor, subsistema que forma parte del planificador. El supervisor es un sistema dentro del módulo de planificación del coche encargado de generar los puntos de paso dada una trayectoria y de supervisar que tales puntos han sido alcanzados para así proveer al controlador del vehículo de la información necesaria para mover el automóvil a través de la ruta. La meta es programar el subsistema e implementarlo en el programa de un robot móvil autónomo de cuatro ruedas. Se ha realizado un análisis del estado actual del vehículo, que cuenta ya con un programa base implementado del cuál partir, para estudiar y entender el comportamiento. Esto posibilita conocer qué parte del código se debe modificar, eliminar, reestructurar o añadir. Se ha llevado a cabo una fase de programación y de experimentación en paralelo. Un aula con el suficiente espacio libre ha permitido la realización de los experimentos. Los ensayos han sido fundamentales para la demostración veraz de los objetivos alcanzados. En el final de la memoria, unas conclusiones en las que constan los objetivos alcanzados, el nivel de satisfacción respecto a lo logrado y características que también pudieron haberse llevado a cabo

3D object detection is crucial for the safety and reliability of mobile robots. Mobile robots must understand dynamic environments to operate safely and successfully carry out their tasks. However, most of the open-source datasets and methods are built for autonomous driving. In this paper, we present a detailed review of available 3D object detection methods, focusing on the ones that could be easily adapted and used on mobile robots. We show that the methods do not perform well if used off-the-shelf on mobile robots or are too computationally expensive to run on mobile robotic platforms. Therefore, we propose a domain adaptation approach to use publicly available data to retrain the perception modules of mobile robots, resulting in higher performance. Finally, we run the tests on the real-world robot and provide data for testing our approach., QC 20230810

This paper addresses the problem of task assignment and trajectory generation for installing bird diverters using a fleet of multi-rotors. The proposed solution extends our previous motion planner to compute feasible and constrained trajectories, considering payload capacity limitations and recharging constraints. Signal Temporal Logic (STL) specifications are employed to encode the mission objectives and temporal requirements. Additionally, an event-based replanning strategy is introduced to handle unforeseen failures. An energy minimization term is also employed to implicitly save multi-rotor flight time during installation operations. The effectiveness and validity of the approach are demonstrated through simulations in MATLAB and Gazebo, as well as field experiments carried out in a mock-up scenario.

Real-time spatial awareness is essential in areas such as robotics and autonomous navigation. However, as environments expand and become increasingly complex, maintaining both a low computational load and high mapping accuracy remains a significant challenge. This thesis addresses these challenges by proposing a novel method for real-time construction of continuous Euclidean distance fields (EDF) using Gaussian process (GP) regression, hereafter referred to as GP-EDF, tailored specifically for large indoor environments. The proposed approach focuses on leveraging the inherent structural information of indoor spaces by partitioning them into rooms and constructing a local GP-EDF model for each, reducing the computational cost tied to large matrix operations in GPs. By also exploiting the geometric regularities commonly found in indoor spaces it detects walls and represents them as line segments. This information is integrated into the models’ priors to both improve accuracy and further reduce the computational expense. Comparison with two baselines demonstrated the proposed approach’s effectiveness. It maintained low computation times despite increasing amounts of sensor data, signifying a significant improvement in scalability. Results also confirmed that the EDF quality remains high and isn’t affected by partitioning the GP-EDF into local models. The method also reduced the influence of sensor noise on the EDF’s accuracy when incorporating the line segments into the model. Additionally, the proposed room segmentation method proved to be efficient and generated accurately partitioned rooms, with a high degree of independence between them. In conclusion, the proposed approach offers a scalable, accurate and efficient solution for real-time construction of EDFs, demonstrating significant potential in aiding autonomous navigation within large indoor spaces., Realtidsrumslig medvetenhet är avgörande inom områden som robotik och autonom navigering. Emellertid, när miljöer expanderar och blir alltmer komplexa, kvarstår det en betydande utmaning att bibehålla både en låg beräkningsbelastning och hög kartläggningsnoggrannhet. Denna avhandling bemöter dessa utmaningar genom att föreslå en ny metod för realtidskonstruktion av kontinuerliga euklidiska avståndsfält (EDF) med hjälp av regression via gaussiska processer (GP), hädanefter benämnd GP-EDF, specifikt anpassad för stora inomhusmiljöer. Den föreslagna metoden fokuserar på att utnyttja den inneboende strukturella informationen i inomhusmiljöer genom att dela upp dem i rum och konstruera en lokal GP-EDF-modell för varje rum, vilket minskar den beräkningsbelastning som är kopplad till stora matrisoperationer i GP:er. Genom att även utnyttja de geometriska regelbundenheter som vanligtvis finns i inomhusutrymmen, detekterar den väggar och representerar dem som linjesegment. Denna information integreras sedan i modellernas a priori-fördelningar, både för att förbättra noggrannheten och ytterligare minska den beräkningsmässiga kostnaden. Jämförelse med två baslinjemodeller demonstrerade den föreslagna metodens effektivitet. Den bibehöll låga beräkningstider trots ökande mängder sensordata, vilket indikerar en betydande förbättring av skalbarheten. Resultaten bekräftade även att kvaliteten på EDF:en förblir hög och påverkas inte av uppdelningen av GP-EDF:en i lokala modeller. Metoden minskade även sensorbrusets inverkan på EDF:ens noggrannhet vid integrering av linjesegment i modellen. Dessutom visade sig den föreslagna rumsegmenteringsmetoden vara effektiv och genererade korrekt uppdelade rum, med en hög grad av oberoende mellan dem. Sammanfattningsvis erbjuder den föreslagna metoden en skalbar och effektiv lösning för realtidskonstruktion av EDF:er, och visar på betydande potential att underlätta autonom navigering inom stora inomhusutrymmen.

Real-time spatial awareness is essential in areas such as robotics and autonomous navigation. However, as environments expand and become increasingly complex, maintaining both a low computational load and high mapping accuracy remains a significant challenge. This thesis addresses these challenges by proposing a novel method for real-time construction of continuous Euclidean distance fields (EDF) using Gaussian process (GP) regression, hereafter referred to as GP-EDF, tailored specifically for large indoor environments. The proposed approach focuses on leveraging the inherent structural information of indoor spaces by partitioning them into rooms and constructing a local GP-EDF model for each, reducing the computational cost tied to large matrix operations in GPs. By also exploiting the geometric regularities commonly found in indoor spaces it detects walls and represents them as line segments. This information is integrated into the models’ priors to both improve accuracy and further reduce the computational expense. Comparison with two baselines demonstrated the proposed approach’s effectiveness. It maintained low computation times despite increasing amounts of sensor data, signifying a significant improvement in scalability. Results also confirmed that the EDF quality remains high and isn’t affected by partitioning the GP-EDF into local models. The method also reduced the influence of sensor noise on the EDF’s accuracy when incorporating the line segments into the model. Additionally, the proposed room segmentation method proved to be efficient and generated accurately partitioned rooms, with a high degree of independence between them. In conclusion, the proposed approach offers a scalable, accurate and efficient solution for real-time construction of EDFs, demonstrating significant potential in aiding autonomous navigation within large indoor spaces., Realtidsrumslig medvetenhet är avgörande inom områden som robotik och autonom navigering. Emellertid, när miljöer expanderar och blir alltmer komplexa, kvarstår det en betydande utmaning att bibehålla både en låg beräkningsbelastning och hög kartläggningsnoggrannhet. Denna avhandling bemöter dessa utmaningar genom att föreslå en ny metod för realtidskonstruktion av kontinuerliga euklidiska avståndsfält (EDF) med hjälp av regression via gaussiska processer (GP), hädanefter benämnd GP-EDF, specifikt anpassad för stora inomhusmiljöer. Den föreslagna metoden fokuserar på att utnyttja den inneboende strukturella informationen i inomhusmiljöer genom att dela upp dem i rum och konstruera en lokal GP-EDF-modell för varje rum, vilket minskar den beräkningsbelastning som är kopplad till stora matrisoperationer i GP:er. Genom att även utnyttja de geometriska regelbundenheter som vanligtvis finns i inomhusutrymmen, detekterar den väggar och representerar dem som linjesegment. Denna information integreras sedan i modellernas a priori-fördelningar, både för att förbättra noggrannheten och ytterligare minska den beräkningsmässiga kostnaden. Jämförelse med två baslinjemodeller demonstrerade den föreslagna metodens effektivitet. Den bibehöll låga beräkningstider trots ökande mängder sensordata, vilket indikerar en betydande förbättring av skalbarheten. Resultaten bekräftade även att kvaliteten på EDF:en förblir hög och påverkas inte av uppdelningen av GP-EDF:en i lokala modeller. Metoden minskade även sensorbrusets inverkan på EDF:ens noggrannhet vid integrering av linjesegment i modellen. Dessutom visade sig den föreslagna rumsegmenteringsmetoden vara effektiv och genererade korrekt uppdelade rum, med en hög grad av oberoende mellan dem. Sammanfattningsvis erbjuder den föreslagna metoden en skalbar och effektiv lösning för realtidskonstruktion av EDF:er, och visar på betydande potential att underlätta autonom navigering inom stora inomhusutrymmen.

Current intralogistics services require keeping up with e-commerce demands, reducing delivery times and waste, and increasing overall flexibility. As a consequence, the use of automated guided vehicles (AGVs) and, more recently, autonomous mobile robots (AMRs) for logistics operations is steadily increasing.

[EN] This project outlines the process of designing and customizing a mobile robot based on the RB-Vogui model from Robotnik Automation S.L., a leading global manufacturer of mobile robots and manipulators. The primary aim of this project is to meet specific customer requirements by integrating a novel safety system dependent on an external PLC connected to the customer’s LIDAR scanners. In addition to the new safety system, the project encompasses the upgrade of the robot’s software to support ROS2 middleware, reflecting an important transition for Robotnik’s software systems. This upgrade involves not only the integration of the standard robot software based on ROS1 but also a comprehensive research and development phase to enable the simultaneous operation of ROS1 and ROS2 within the same system. The project further involves a complete 3D design of the robot, assembling and testing individual components, and integrating additional software to accommodate the new safety system and the customer’s sensors. Systematic tests in both simulated and real environments are also integral to the project, aiming to ensure successful integration hat meets both the customer’s and Robotnik’s standards and specifications. The future vision of this project entails integrating the newly developed safety system into the company’s database for future usage and running extensive tests in diverse environments, including the customer’s infrastructure. By undertaking this project, the goal is to deliver a customized robot equipped with an advanced safety system, facilitating a safer, more adaptable, and functional mobile robotic solution.

This paper describes the behavioural tests carried out with real fish to study their behaviour and stress against the different prototypes of mini-robot fish with the aim of identifying stressors and reducing them when designing future robots for aquaculture., Peer Reviewed

This paper studies the relation between node deletion and algebraic connectivity for graphs with a hierarchical structure represented by layers. To capture this structure, the concepts of layered path graph and its (sub)graph cone are introduced. The problem is motivated by a mobile robot formation control guided by a leader. In particular, we consider a scenario in which robots may leave the network resulting in the removal of the nodes and the associated edges. We show that the existence of at least one neighbor in the upper layer is crucial for the algebraic connectivity not to deteriorate by node deletion.

A key challenge for SaR robotics is to avoid dynamic obstacles in cluttered environments, with limited and noisy information. In this research, a controller for SaR robots is developed by coupling a local heuristic motion planner with a model predictive control (MPC) based trajectory tracker. Constraint tightening and tube-based control are used to make the MPC robust to model mismatch and additive measurement noise, while the motion planner is integrated with the MPC. The motion planner periodically supplies a reference trajectory to the trajectory tracker, but the MPC can request additional updates in case of a noticeable mismatch between the predicted and measured environment, based on a user-defined threshold. A case study is designed in MATLAB where a single robot needs to reach a goal through a cluttered environment with dynamic obstacles. Results from the case study show that the MPC method outperforms two state-of-the-art control approaches that are based on the rapidly-exploring random tree (RRT) and artificial potential function (APF) methods. In particular, the heuristic and MPC coupled controller showed a higher success rate in reaching the goal without collisions, and displayed a lower path length in cases with both low and high computational budget., Aerospace Engineering | Control & Simulation

This study is concerned with a large-scale telepresence system based on remote control of mobile robot or aerial vehicle. The proposed system provides a user with not only view of remote site but also related information by AR technique. Such systems are referred to as augmented telepresence in this paper. Aerial imagery can capture a wider area at once than image capturing from the ground. However, it is difficult for a user to change position and direction of viewpoint freely because of the difficulty in remote control and limitation of hardware. To overcome these problems, the proposed system uses an autopilot airship to support changing user's viewpoint and employs an omni-directional camera for changing viewing direction easily. This paper describes hardware configuration for aerial imagery, an approach for overlaying virtual objects, and automatic control of the airship, as well as experimental results using a prototype system., ISMAR 2010 : 9th IEEE International Symposium on Mixed and Augmented Reality , Oct 13-16, 2010 , Seoul, Korea

This paper proposes a teleoperation interface by which an operator can control a robot from freely configured viewpoints using realistic images of the physical world. The viewpoints generated by the proposed interface provide human operators with intuitive control using a head-mounted display and head tracker, and assist them to grasp the environment surrounding the robot. A state-of-the-art free-viewpoint image generation technique is employed to generate the scene presented to the operator. In addition, an augmented reality technique is used to superimpose a 3D model of the robot onto the generated scenes. Through evaluations under virtual and physical environments, we confirmed that the proposed interface improves the accuracy of teleoperation., IROS 2013 : IEEE/RSJ International Conference on Intelligent Robots and Systems , Nov 3-7, 2013 , Tokyo, Japan

We investigate in this paper the gathering problem on ring shaped networks where the aim is to ensure that a collection of identical, oblivious and asynchronous mobile robots meet (gather) in one location not known in advance. Robots operate in cycles that consist of three phases: Look, Compute and Move. During the first phase, robots take a snapshot to see the positions of the other robots. In the second phase, they make a decision to move or to stay idle. In the last phase, they move towards a computed destination if any. While most of the previous works in the discrete universe, under this setting consider that robots have an unlimited visibility i.e., each robot is able to see the position of all the other robots in the system, only few studies have been devoted recently to the case where robots have a restricted visibility. In our work, we continue the investigation aiming to characterize the cases in which the problem can be solved., SRDSW : 2014 IEEE 33rd International Symposium on Reliable Distributed Systems Workshops , 6-9 Oct. 2014 , Nara, Japan

Recently, the concept of behavioral media using a mobile robot has emerged in tele-robotics, where the mobile robot behaves as a user's second body or an agent in a remote site. An important issue in behavioral media is how to control the robot intuitively immersing a user into a remote site. This paper describes a new approach which combines locomotion interface and immersive projection display of a remote site for controlling a networked mobile robot. We have developed a prototype system and have evaluated the usability of the system with experiments., ICME 2006 : IEEE International Conference on Multimedia & Expo , Jul 9-12, 2006 , Toronto, Canada

This dissertation explores visual perception within the game of autonomous racing.Pocket Racer, is a scaled, high performance vehicle designed to learn to imitate the behavior of the world champion driver with vision based internal models, similar to that of the dragonfly when exhibiting predictive prey interception behaviors. This algorithm, Race-Net, predicts and targets future vehicle trajectory states with a combination of offline deep supervised learning and predictive forward inverse modelling. Through Race-Net, we attempt to demonstrate super human autonomous driving capabilities on our Pocket Racer platform. Pocket Car, our educational outreach platform, is designed for STEM education. Versatile while being low-cost and accessible, Pocket Car is designed with a wide assortment of sensors. Pocket Car is capable of learning to mimic driving behaviors and also compute state estimation for control via Adaptive Monte Carlo Localization (AMCL) using cones as landmarks. Alternatively, it is equipped with a low cost 2D LIDAR for Simultaneous Localization and Mapping (SLAM), applicable for competitions involving school hallways. We demonstrate the educational merits of Pocket Car through our successful collaboration with the IEEE undergraduate club at UCLA.

In this paper, we investigate a scenario of one-human-multiple-robot navigation in three dimensions, and examine the impacts of the VR (Virtual Reality) technology on human properties from a control-Theoretic perspective. We start by reviewing a passivity-based distributed control architecture that takes complementary interactions such that motion synchronization is autonomously completed by a distributed robot controller while the operator is dedicated to robot navigation. Due to the limited human capability of 3-D recognition and limited dimensionality on the real-Time manipulability, 3-D navigation is completely different from that of the one-or two-dimensional case and we need to carefully design both feedback and command interfaces between the operator and robots. Specifically, we employ two different pairs of the interfaces, traditional joystick controller with 2-D display monitor and HMD (Head Mounted Display) with VR controller. We then build human models from the operation data with these interfaces on a human-in-The-loop simulator. Through the human modeling, we present two novel findings: (i) VR interfaces improve the accuracy of the human model with about 25∼45% of fitting ratio, which must drastically eases the design of human-robot collaboration systems, (ii) VR interfaces enhance human passivity, which is a key to ensuring closed-loop stability for the human-in-The-loop system.

In this paper, we investigate a scenario of one-human-multiple-robot navigation in three dimensions, and examine the impacts of the VR (Virtual Reality) technology on human properties from a control-Theoretic perspective. We start by reviewing a passivity-based distributed control architecture that takes complementary interactions such that motion synchronization is autonomously completed by a distributed robot controller while the operator is dedicated to robot navigation. Due to the limited human capability of 3-D recognition and limited dimensionality on the real-Time manipulability, 3-D navigation is completely different from that of the one-or two-dimensional case and we need to carefully design both feedback and command interfaces between the operator and robots. Specifically, we employ two different pairs of the interfaces, traditional joystick controller with 2-D display monitor and HMD (Head Mounted Display) with VR controller. We then build human models from the operation data with these interfaces on a human-in-The-loop simulator. Through the human modeling, we present two novel findings: (i) VR interfaces improve the accuracy of the human model with about 25∼45% of fitting ratio, which must drastically eases the design of human-robot collaboration systems, (ii) VR interfaces enhance human passivity, which is a key to ensuring closed-loop stability for the human-in-The-loop system.

Distributed model predictive control (DMPC) concerns how to online control multiple robotic systems with constraints effectively. However, the nonlinearity, nonconvexity, and strong interconnections of dynamic system models and constraints can make the real-time and real-world DMPC implementations nontrivial. Reinforcement learning (RL) algorithms are promising for control policy design. However, how to ensure safety in terms of state constraints in RL remains a significant issue. This paper proposes a barrier function-based safe reinforcement learning algorithm for DMPC of nonlinear multi-robot systems under state constraints. The proposed approach is composed of several local learning-based MPC regulators. Each regulator, associated with a local system, learns and deploys the local control policy using a safe reinforcement learning algorithm in a distributed manner, i.e., with state information only among the neighbor agents. As a prominent feature of the proposed algorithm, we present a novel barrier-based policy structure to ensure safety, which has a clear mechanistic interpretation. Both simulated and real-world experiments on the formation control of mobile robots with collision avoidance show the effectiveness of the proposed safe reinforcement learning algorithm for DMPC., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Robot Dynamics

This work introduces a new single-stage adaptive controller for Euler-Lagrange systems with nonholonomic constraints. The proposed mechanism provides a simpler design philosophy compared to double-stage mechanisms (that address kinematics and dynamics in two steps), while achieving analogous stability properties, i.e. stability of both original and internal states. Meanwhile, we do not require direct access to the internal states as required in state-of-the-art single-stage mechanisms. The proposed approach is studied via Lyapunov analysis, validated numerically on wheeled mobile robot dynamics and compared to a standard double-stage approach., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Bart De Schutter

The tracking problem (that is, how to follow a previously memorized path) is one of the most important problems in mobile robots. Several methods can be formulated depending on the way the robot state is related to the path. “Trajectory tracking” is the most common method, with the controller aiming to move the robot toward a moving target point, like in a real-time servosystem. In the case of complex systems or systems under perturbations or unmodeled effects, such as UAVs (Unmanned Aerial Vehicles), other tracking methods can offer additional benefits. In this paper, methods that consider the dynamics of the path’s descriptor parameter (which can be called “error adaptive tracking”) are contrasted with trajectory tracking. A formal description of tracking methods is first presented, showing that two types of error adaptive tracking can be used with the same controller in any system. Then, it is shown that the selection of an appropriate tracking rate improves error convergence and robustness for a UAV system, which is illustrated by simulation experiments. It is concluded that error adaptive tracking methods outperform trajectory tracking ones, producing a faster and more robust convergence tracking, while preserving, if required, the same tracking rate when convergence is achieved.

This paper presents an approach for smooth trajectory planning in semi-rigid nonholonomic mobile robot formations using Bezier-splines. Unlike most existing approaches, the focus is on maintaining a semi-rigid formation, as required in many scenarios such as object transport, handling or assembly. We use a Relaxed A* planner to create an optimal collision-free global path and then smooth this path using splines. The smoothed global path serves to create target paths for every robot in the formation. From these paths, we then calculate the trajectories for each robot. In an iterative process, we match the velocities of the robots so that all trajectories are synchronized, and the dynamic limits of all robots are maintained. We provide experimental validation, which confirms no violation of the dynamic limits and shows an excellent control performance for a system of three robots moving at 0.3 m/s.

In recent years, mobile robots have gained tremendous attention from the entire society: the industry is aiming at selling more intelligent products while the academia is improving their performance from all perspectives. Real world examples include autnomous driving vehicles, multirotors, legged robots, etc. One of the challenging tasks commonly faced by all game players, and all robotics platforms, is to plan motion or locomotion of the robot, calculate an optimal trajectory according to certain criterion and control it accordingly. Difficulty of solving such task usually arises from high-dimensionality and complexity of the system dynamics, fast changing conditions imposed as constraints and necessity for real-time deployment. This work proposes a method over the aforementioned mission by solving an optimal control problem in a receding horizon fashion. Unlike the existing Sequential Linear Quadratic [1] algorithm which is a continuous-time variant of Differential Dynamic Programming [2], we tackle the problem in a discretized multiple shooting fashion. Sequential Quadratic Programming is employed as optimization technique to solve the constrained Nonlinear Programming iteratively. Moreover, we apply trust region method in the sub Quadratic Programming to handle potential indefiniteness of Hessian matrix as well as to improve robustness of the solver. Simulation and benchmark with previous method have been conducted on robotics platforms to show the effectiveness of our solution and superiority under certain circumstances. Experiments have demonstrated that our method is capable of generating trajectories under complicated scenarios where the Hessian matrix contains negative eigenvalues (e.g. obstacle avoidance)., De senaste åren har mobila robotar fått enorm uppmärksamhet från hela samhället: branschen siktar på att sälja mer intelligenta produkter samtidigt som akademin förbättrar sina prestationer ur alla perspektiv. Exempel på verkligheten inkluderar autonoma körande fordon, multirotorer, robotar med ben, etc. En av de utmanande uppgifterna som vanligtvis alla spelare och alla robotplattformar står inför är att planera robotens rörelse eller rörelse, beräkna en optimal bana enligt vissa kriterier och kontrollera det därefter. Svårigheter att lösa en sådan uppgift beror vanligtvis på hög dimensionalitet och komplexitet hos systemdynamiken, snabbt föränderliga villkor som åläggs som begränsningar och nödvändighet för realtidsdistribution. Detta arbete föreslår en metod över det tidigare nämnda uppdraget genom att lösa ett optimalt kontrollproblem på ett vikande horisont. Till skillnad från den befintliga Sequential Linear Quadratic [1] algoritmen som är en kontinuerlig tidsvariant av Differential Dynamic Programming [2], tar vi oss an problemet på ett diskretiserat multipelfotograferingssätt. Sekventiell kvadratisk programmering används som optimeringsteknik för att lösa den begränsade olinjära programmeringen iterativt. Dessutom tillämpar vi trust region-metoden i den sub-kvadratiska programmeringen för att hantera potentiell obestämdhet av hessisk matris samt för att förbättra lösarens robusthet. Simulering och benchmark med tidigare metod har utförts på robotplattformar för att visa effektiviteten hos vår lösning och överlägsenhet under vissa omständigheter. Experiment har visat att vår metod är kapabel att generera banor under komplicerade scenarier där den hessiska matrisen innehåller negativa egenvärden (t.ex. undvikande av hinder).

One of the major aspects of Industry 4.0 is enabling the manufacturing entities to operate in the dynamical systems autonomously. Therefore, to be autonomous, manufacturing entities need to have sensors to perceive their environment and utilize that information to make decisions regarding their actions. Having that in mind, in this paper, the authors propose a mobile robot decision-making system based on the integration of visual data and mobile robot pose. Mobile robot pose (current position and orientation) is integrated with two images gathered by two cameras and utilized to predict the possibility of gripping the part to be manufactured. A decision-making system is created by utilizing the deep learning model Resnet18 with an additional input for the mobile robot pose. The model is trained end-to-end and experimental evaluation is performed by using the mobile robot RACIO (Robot with Artificial Intelligence based COgnition).

The project is about using an AGV (Automatic Guided Vehicle) to capture the robot's data and process them in MATLAB with the aim of creating an application that shows a 2D map of the space with the route taken and at the same time the image, captured by the camera, corresponding to the position of the robot's kinematic model. To achieve this, the different MATLAB tools were studied and the data were processed in different ways, but they were captured in the same file in order to relate them later. In conclusion, during the project the main objective of programming in MATLAB has been achieved, as well as the creation of an application that will allow people with limited knowledge of MATLAB to visualise the data captured by the AGV and analyse it for their own projects., El proyecto trata sobre utilizar un AGV (Vehículo de guiado automático) para capturar los datos del robot y procesarlos en MATLAB con el objetivo de crear una aplicación que muestre un mapa 2D del espacio con la ruta realizada y a su vez la imagen, captada por la cámara, correspondiente a posición del modelo cinemático del robot. Para lograrlo se estudiaron las diferentes herramientas de MATLAB y se procesaron los datos de diferentes maneras, pero se capturaron en el mismo archivo para relacionarlos a posteriori. En conclusión, durante el proyecto se ha cumplido el principal objetivo de programar en MATLAB, como también la creación de una aplicación que permitirá a aquellas personas con conocimientos limitados de MATLAB visualizar los datos captados por el AGV y analizar estos para sus propios proyectos., El projecte tracta sobre utilitzar un AGV (Vehicle de guiatge automàtic) per capturar les dades del robot i processar-les a MATLAB amb l'objectiu de crear una aplicació que mostri un mapa 2D de l'espai amb la ruta realitzada i alhora la imatge, captada per la càmera, corresponent a posició del model cinemàtic del robot. Per aconseguir-ho es van estudiar les diferents eines de MATLAB i es van processar les dades de diferents maneres, però es van capturar al mateix arxiu per relacionar-les a posteriori. En conclusió, durant el projecte s'ha complert el principal objectiu de programar a MATLAB, com també la creació d'una aplicació que permetrà a aquelles persones amb coneixements limitats de MATLAB visualitzar les dades captades per l'AGV i analitzar-les per als seus propis projectes.

L'objecte d'aquest treball és la integració d'un dispositiu electrònic amb funció de giroscopi a un robot mòbil anomenat Robotino (FESTO). Per a la seva integració s'haurà de realitzar tant en l'àmbit del maquinari com el del programari, és a dir, el cablejat entre el dispositiu i el robot i la programació d'una llibreria per al seu ús incorporant aquesta llibreria a l'API ja existent d'aquest robot, és a dir, a la llibreria de funcions que ja té el Robotino. Per assolir aquest treball, primer s'haurà de fer un estudi dels diferents elements que tenim disponibles per fer el projecte, que serà el nostre punt de partida, com per exemple, el Robotino i el sistema QNX o treballs anteriors que s'han fet amb aquest robot. Pel que respecta al seu cablejat hi ha dos possibilitats, via USB o via RS-232, per tant, s'ha d'avaluar quina de les alternatives d'integració física és l'adequada. Per desenvolupar la llibreria de funcions, es durà a terme diferents proves per confeccionarla, com per exemple, l'obtenció de dades del Giroscopi, i unes proves de moviment utilitzant el giroscopi i sense fer-ho servir. Finalment, el resultat final que es vol aconseguir és millorar la capacitat de navegació que ja té el robot, i realitzar un cop tot integrat una determinada tasca de navegació, per avaluar el grau de millora que ha suposat l'ús d'aquest dispositiu

Developing digital dashboards (DD) that support staff in monitoring, identifying anomalies, and facilitating corrective actions are decisive for achieving the benefits of Smart Production Logistics (SPL). However, existing literature about SPL has not sufficiently investigated the characteristics of DD allowing staff to enhance operational performance. This conceptual study identifies the characteristics of DD in SPL for enhancing operational performance of material handling. The study presents preliminary findings from an ongoing laboratory development, and identifies six characteristics of DD. These include monitoring, analysis, prediction, identification, recommendation, and control. The study discusses the implications of these characteristics when applied to energy consumption, makespan, on-time delivery, and status for material handling. The study proposes the prototype of a DD in a laboratory environment involving Autonomous Mobile Robots., © 2022, IFIP International Federation for Information Processing.© 2022 Springer Nature Switzerland AG. Part of Springer Nature.The authors would like to acknowledge the support of Swedish Innovation Agency (VINNOVA), and its funding program Produktion2030. This study is part of the Explainable and Learning Production & Logistics by Artificial Intelligence (EXPLAIN) project.

The objective of this project is to develop an integrated framework for the automatic execution of complex Task and Motion planning problems in a multi-robot environment. In dynamic environments, such as the introduction of a human in the loop, the system cannot rely solely on an original plan. Possible disruptions occurring in the environment mid-execution, like an object being placed where it should not be by the human, or an object falling on its side, require a way to react to them and obtain a new plan in order to achieve the goal. The chosen tool to center the framework around is Behavior Trees (BTs). BTs have been chosen due to their ease of use, their modularity, and their portability. Each of the steps that are executed in the plan is represented as an Action Node. Through the use of Control Nodes, the order in which the different branches of the tree are executed can be dictated. The use of Reactive Control nodes allows for the system to react to the changes in the environment as soon as they occur. The framework begins by executing an original plan which has been planned offline based on the initial state of the environment. Once the BT executor is run, the system will check that the environment has not deviated from the original. If it has, a replan is triggered, and the BT will be rewritten mid-execution with the updated Task and Motion plan. Good monitoring of the system state is required in order to react to disruptions at either the geometric or the symbolic level. This led to the development of the State Module. Two different States are defined: the Expected State collects all the information from the original plan and represents the state the system should be in, while the Observed State uses the sensors in the perception module to sense the state the system is in. A mismatch between the two signifies that the original plan is no longer valid, and a replan is required. The robot chosen to perform most of the testing is a dual-armed 7 Degree of Free