Multilingual manager: a new strategic role in organizations

Today?s knowledge management (KM) systems seldom account for language management and, especially, multilingual information processing. Document management is one of the strongest components of KM systems. If these systems do not include a multilingual knowledge management policy, intranet searches, excessive document space occupancy and redundant information slow down what are the most effective processes in a single language environment. In this paper, we model information flow from the sources of knowledge to the persons/systems searching for specific information. Within this framework, we focus on the importance of multilingual information processing, which is a hugely complex component of modern organizations

Immersive Robotic Telepresence for Remote Educational Scenarios

[EN] : Social robots have an enormous potential for educational applications and allow for cognitive outcomes that are similar to those with human involvement. Remotely controlling a social robot to interact with students and peers in an immersive fashion opens up new possibilities for instructors and learners alike. Using immersive approaches can promote engagement and have beneficial effects on remote lesson delivery and participation. However, the performance and power consumption associated with the involved devices are often not sufficiently contemplated, despite being particularly important in light of sustainability considerations. The contributions of this research are thus twofold. On the one hand, we present telepresence solutions for a social robot’s location-independent operation using (a) a virtual reality headset with controllers and (b) a mobile augmented reality application. On the other hand, we perform a thorough analysis of their power consumption and system performance, discussing the impact of employing the various technologies. Using the QTrobot as a platform, direct and immersive control via different interaction modes, including motion, emotion, and voice output, is possible. By not focusing on individual subsystems or motor chains, but the cumulative energy consumption of an unaltered robot performing remote tasks, this research provides orientation regarding the actual cost of deploying immersive robotic telepresence solutions.S

Multi-thread impact on the performance of Monte Carlo based algorithms for self-localization of robots using RGBD sensors

Abstract—Using information from RGBD sensors requires huge amount of processing. To use these sensors improves the robustness of algorithms for object perception, self-localization and, in general, all the capabilities to be performed by a robot to improve its autonomy. In most cases, these algorithms are not computationally feasible using single-thread implementations. This paper describes two multi thread strategies proposed for self localize a mobile robot in a known environment using information from a RGBD sensor. The experiments will show the benefits obtained when different numbers of threads are compared, using different approaches: a pool of threads and creation/destruction scheme. The work has been carried out on a Kobuki mobile robot in the environment of the RoCKiN competition, similar to RoboCup@hom

Evolution of a Cognitive Architecture for Social Robots: Integrating Behaviors and Symbolic Knowledge

[EN] This paper presents the evolution of a robotic architecture intended for controlling autonomous social robots. The first instance of this architecture was originally designed according to behavior-based principles. The building blocks of this architecture were behaviors designed as a finite state machine and organized in an ethological inspired way. However, the need of managing explicit symbolic knowledge in human–robot interaction required the integration of planning capabilities into the architecture and a symbolic representation of the environment and the internal state of the robot. A major contribution of this paper is the description of the working memory that integrates these two approaches. This working memory has been implemented as a distributed graph. Another contribution is the use of behavior trees instead of state machine for implementing the behavior-based part of the architecture. This late version of the architecture has been tested in robotic competitions (RoboCup or European Robotics League, among others), whose performance is also discussed in this paper.SIEuropean Horizon 2020 research and innovation program under grant agreement No 732410.Ministerio de Ciencia, Innovación y Universidade

Reinforcement Learning Experiments and Benchmark for Solving Robotic Reaching Tasks

Reinforcement learning has shown great promise in robotics thanks to its ability to develop efficient robotic control procedures through self-training. In particular, reinforcement learning has been successfully applied to solving the reaching task with robotic arms. In this paper, we define a robust, reproducible and systematic experimental procedure to compare the performance of various model-free algorithms at solving this task. The policies are trained in simulation and are then transferred to a physical robotic manipulator. It is shown that augmenting the reward signal with the Hindsight Experience Replay exploration technique increases the average return of off-policy agents between 7 and 9 folds when the target position is initialised randomly at the beginning of each episode

rl_reach: Reproducible reinforcement learning experiments for robotic reaching tasks

rl_reach is publicly available at this URL: https://github.com/PierreExeter/rl_reach[EN] Training reinforcement learning agents at solving a given task is highly dependent on identifying optimal sets of hyperparameters and selecting suitable environment input/output configurations. This tedious process could be eased with a straightforward toolbox allowing its user to quickly compare different training parameter sets. We present rl_reach, a self-contained, open-source and easy-to-use software package designed to run reproducible reinforcement learning experiments for customisable robotic reaching tasks. rl_reach packs together training environments, agents, hyperparameter optimisation tools and policy evaluation scripts, allowing its users to quickly investigate and identify optimal training configurations. rl_reach is publicly available at this URL: https://github.com/PierreExeter/rl_reachSIEuropean Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 713654

Social Navigation in a Cognitive Architecture Using Dynamic Proxemic Zones

[EN] Robots have begun to populate the everyday environments of human beings. These social robots must perform their tasks without disturbing the people with whom they share their environment. This paper proposes a navigation algorithm for robots that is acceptable to people. Robots will detect the personal areas of humans, to carry out their tasks, generating navigation routes that have less impact on human activities. The main novelty of this work is that the robot will perceive the moods of people to adjust the size of proxemic areas. This work will contribute to making the presence of robots in human-populated environments more acceptable. As a result, we have integrated this approach into a cognitive architecture designed to perform tasks in human-populated environments. The paper provides quantitative experimental results in two scenarios: controlled, including social navigation metrics in comparison with a traditional navigation method, and non-controlled, in robotic competitions where different studies of social robotics are measured.SIGobierno de España (TIN2016-76515-R grant for the COMBAHO project, supported with Feder funds )Comunidad de Madrid (RoboCity2030-DIH-CM (S2018/NMT-4331)

KANT: A tool for Grounding and Knowledge Management

The intelligent robotics community usually organizes knowledge into symbolic and sub-symbolic levels. These two levels establish the set of symbols and rules for manipulating knowledge based on their (symbol system - dictionary). Thus, the correspondences -- Grounding or knowledge representation -- require specific software techniques for anchoring continuous and discrete state variables between these two levels. This paper presents the design and evaluation of an Open Source tool called KANT(Knowledge mAnagemeNT) to let different components of the system architecture controlling the robot query, save, edit, and delete the data from the Knowledge Base without having to worry about the type and the implementation of the source data. Using KANT, components managing subsymbolic information can smoothly interact with symbolic components. Besides, implementation mechanisms used in KANT, such as the use of in-memory and non-SQL databases, improve the performance of the knowledge management systems in ROS middleware, as shown by the evaluations presented in this work

Propagation, Localization and Navigation in Tunnel-like Environments

La robótica de servicio, entendida como aquella destinada al uso de uno o varios robots con fines de, por ejemplo, vigilancia, rescate e inspecciones, ha ido tomando cada vez más relevancia en los últimos años. Debido a los grandes avances en las distintas áreas de la robótica, los robots han sido capaces de ejecutar satisfactoriamente tareas que resultan peligrosas o incluso imposibles para los humanos, en diversos entornos. Entre ellos, los entornos confinados como túneles, minas y tuberías, han atraído la atención en aplicaciones relacionadas con transporte ferroviario, redes vehiculares, búsqueda y rescate, y vigilancia, tanto en el ámbito civil como militar. En muchas tareas, la utilización de varios robots resulta más provechoso que utilizar sólo uno. Para cooperar, los robots deben intercambiar información sobre el entorno y su propio estado, por lo que la comunicación entre ellos resulta crucial. Debido a la imposibilidad de utilizar redes cableadas entre robots móviles, se despliegan redes inalámbricas. Para determinar la calidad de señal entre dos robots, inicialmente se utilizaban modelos de propagación basados únicamente en la distancia entre ellos. Sin embargo, estas predicciones sólo resultan útiles en exteriores y sin la presencia de obstáculos, que sólo componen una pequeña parte de los escenarios de la robótica de servicio. Mas aún, la naturaleza altamente multi-trayecto de la propagación electromagnética en túneles hace que éstos actúen como guías de onda para cierto rango de frecuencias, extendiendo considerablemente el alcance de comunicación en comparación con entornos exteriores. Sin embargo, la señal se ve afectada con profundos desvanecimientos (llamados fadings en inglés). Esto los convierte en un reto para la robótica que considera la comunicación entre robots como fundamental. Además, la naturaleza hostil de estos entornos, así como también la falta de características visuales y estructurales, dificultan la localización en estos escenarios, cuestión que resulta fundamental para ejecutar con éxito una tarea con un robot. Los métodos de localización utilizados en interiores, como aquellos basados en SLAM visual, resultan imprecisos por la falta de características distintivas para cámaras o lásers, mientras que los sensores utilizados en exteriores, como el GPS, no funcionan dentro de túneles o tuberías. En esta tesis abordamos problemas fundamentales para la robótica con el fin de proporcionar herramientas necesarias para la exploración con robots en entornos tipo túnel, manteniendo la conectividad de la red de comunicaciones formada por varios robots y una estación base. Para ello, primeramente caracterizamos, en términos de propagación, los dos escenarios tipo túnel más comunes: un túnel de hormigón y una tubería metálica. Hacemos énfasis en el fenómeno de los fadings, ya que son el problema más importante a considerar para mantener la comunicación. Posteriormente presentamos una estrategia de navegación para desplegar un equipo de robots en un túnel, lidiando con los fadings para mantener la conectividad de la red formada por los robots. Esta estrategia ha sido validada a través de numerosos experimentos realizados en un túnel real, el túnel de Somport. Luego, abordamos el problema de la localización, proponiendo e implementando una técnica que permite estimar la posición de un robot dentro de una tubería, basada en la periodicidad de los fadings. El método es validado a través de experimentos reales en tuberías de pequeña y grandes dimensiones. Finalmente, proponemos esquemas de diversidad espacial, de forma que se facilita la navegación mientras se mejora la localización.Deploying a team of robots for search and rescue, inspection, or surveillance, has increasingly gained attention in the last years. As a result of the advances in several areas of robotics, robots have been able to successfully execute tasks that are hazardous or even impossible for humans in a variety of scenarios, such as outdoors, indoors, or even underground. Among these scenarios, tunnel-like environments (such as tunnels, mines, or pipes) have attracted attention for train applications, vehicular networks, search and rescue, and even service and surveillance missions in both military and civilian contexts. In most of the tasks, utilizing a multi-robot team yields better results than a singlerobot system, as it makes the system more robust while reducing the time required to complete tasks. In order to cooperate, robots must exchange information about their current state and the surrounding environment, making communication between them a crucial task. However, due to the mobile nature of robots used for exploration, a wired architecture is not possible nor convenient. Instead, a wireless network is often deployed. Wireless propagation in tunnel-like environments, characterized for the presence of strong fading phenomena, differs from regular indoor and outdoor scenarios, posing multiple challenges for communication-aware robotics. In addition, accurate localization is a problem in environments such as tunnels or pipes. These environments generally lack distinctive visual and/or structural features and are longer than they are wide in shape. Standard indoor localization techniques do not perform well in pipelines or tunnels given the lack of exploitable features, while outdoor techniques (GPS in particular) do not work in these scenarios. In this thesis, we address basic robotics-related problems in order to provide some tools necessary for robotics exploration in tunnel-like scenarios under connectivity constraints. In the first part, we characterize, in terms of propagation, two of the most common tunnel-like environments: a pipe and a tunnel. We emphasize the spatial-fadings phenomena, as it is one of the most relevant issues to deal with, in a communications context. Secondly, we present a navigation strategy to deploy a team of robots for tunnel exploration, in particular maintaining network connectivity in the presence of these fadings. Several experiments conducted in a tunnel allow us to validate the connectivity maintenance of the system. Next, we address the localization problem and propose a technique that uses the periodicity of the fadings to estimate the position of the robots from the base station. The method is validated in small-scale and large-scale pipes. Finally, we propose spatial diversity schemes in order to ease the navigation while improving the localization

Testing of a commercial vector network analyzer as low-cost TDR device to measure soil moisture and electrical conductivity

Time Domain Reflectometry (TDR) is a non-destructive technique to determine the soil apparent dielectric constant, εa, the volumetric water content, θ, and bulk electrical conductivity, σ. However, the high cost of TDR devices may limit its use. This study evaluates two different low-cost Vector Network Analyzers (VNA) commercially available (NanoVNA), with 1.5 (VNA1.5) and 3.0 (VNA3.0) GHz maximum operating frequency. NanoVNA can be used for measurements of Frequency Domain Reflectometry (FDR) or, after suitable post-processing, for θ and σ TDR measures. Although FDR and TDR are dual procedures, TDR is easier to interpret for soil experiments. The TDR waveforms and εa measured with NanoVNA connected to 10 and 20 cm length three-rod probes immersed in air, distilled water, and a soil column with different θ were compared to those measured using a TDR100 (Campbell Sci.) instrument. The capacity of VNAs to measure σ was evaluated by immersing a 10 cm length three-rod probe in different NaCl-water solutions. Measurements obtained with the VNA and TDR100 were compared in a field test using two-rod 22 cm length TDR probes inserted in soil plots with increasing water content. A robust fit was observed between TDR waveforms registered with the two VNAs and the TDR100. Although VNA3.0 doubles the frequency range of VNA1.5, both devices allowed for good estimates of εa (εaVNA1.5, 3.0 = 1.001 εaTDR100 – 0.2125; R2 = 0.999). These results indicate that the low-cost VNA devices can measure soil water content with similar accuracy and precision as the TDR100. A good agreement (σVNA1.5, 3.0 = 0.999 σCM + 0.0023; R2 = 0.999) was also observed between the σ measured using a conductivity meter (CM) and that estimated with the VNAs. Finally, a good correlation was also observed between θ measured in the field experiment with TDR100 and the VNA1.5 and VNA3.0 devices