Recent Advancements in Augmented Reality for Robotic Applications: A Survey

Robots are expanding from industrial applications to daily life, in areas such as medical robotics, rehabilitative robotics, social robotics, and mobile/aerial robotics systems. In recent years, augmented reality (AR) has been integrated into many robotic applications, including medical, industrial, human–robot interactions, and collaboration scenarios. In this work, AR for both medical and industrial robot applications is reviewed and summarized. For medical robot applications, we investigated the integration of AR in (1) preoperative and surgical task planning; (2) image-guided robotic surgery; (3) surgical training and simulation; and (4) telesurgery. AR for industrial scenarios is reviewed in (1) human–robot interactions and collaborations; (2) path planning and task allocation; (3) training and simulation; and (4) teleoperation control/assistance. In addition, the limitations and challenges are discussed. Overall, this article serves as a valuable resource for working in the field of AR and robotic research, offering insights into the recent state of the art and prospects for improvement

Federated Learning on Edge Sensing Devices: A Review

The ability to monitor ambient characteristics, interact with them, and derive information about the surroundings has been made possible by the rapid proliferation of edge sensing devices like IoT, mobile, and wearable devices and their measuring capabilities with integrated sensors. Even though these devices are small and have less capacity for data storage and processing, they produce vast amounts of data. Some example application areas where sensor data is collected and processed include healthcare, environmental (including air quality and pollution levels), automotive, industrial, aerospace, and agricultural applications. These enormous volumes of sensing data collected from the edge devices are analyzed using a variety of Machine Learning (ML) and Deep Learning (DL) approaches. However, analyzing them on the cloud or a server presents challenges related to privacy, hardware, and connectivity limitations. Federated Learning (FL) is emerging as a solution to these problems while preserving privacy by jointly training a model without sharing raw data. In this paper, we review the FL strategies from the perspective of edge sensing devices to get over the limitations of conventional machine learning techniques. We focus on the key FL principles, software frameworks, and testbeds. We also explore the current sensor technologies, properties of the sensing devices and sensing applications where FL is utilized. We conclude with a discussion on open issues and future research directions on FL for further studie

3D visualization of cadastre : assessing the suitability of visual variables and enhancement techniques in the 3D model of condominium property units

La visualisation 3D de données cadastrales a été exploitée dans de nombreuses études, car elle offre de nouvelles possibilités d’examiner des situations de supervision verticale des propriétés. Les chercheurs actifs dans ce domaine estiment que la visualisation 3D pourrait fournir aux utilisateurs une compréhension plus intuitive d’une situation où des propriétés se superposent, ainsi qu’une plus grande capacité et avec moins d’ambiguïté de montrer des problèmes potentiels de chevauchement des unités de propriété. Cependant, la visualisation 3D est une approche qui apporte de nombreux défis par rapport à la visualisation 2D. Les précédentes recherches effectuées en cadastre 3D, et qui utilisent la visualisation 3D, ont très peu enquêté l’impact du choix des variables visuelles (ex. couleur, style) sur la prise de décision. Dans l’optique d'améliorer la visualisation 3D de données cadastres, cette thèse de doctorat examine l’adéquation du choix des variables visuelles et des techniques de rehaussement associées afin de produire un modèle de condominium 3D optimal, et ce, en fonction de certaines tâches spécifiques de visualisation. Les tâches visées sont celles dédiées à la compréhension dans l’espace 3D des limites de propriété du condominium. En ce sens, ce sont principalement des tâches notariales qui ont été ciblées. De plus, cette thèse va mettre en lumière les différences de l’impact des variables visuelles entre une visualisation 2D et 3D. Cette thèse identifie dans un premier temps un cadre théorique pour l'interprétation des variables visuelles dans le contexte d’une visualisation 3D et de données cadastrales au regard d’une revue de littéraire. Dans un deuxième temps, des expérimentations ont été réalisées afin de mettre à l’épreuve la performance des variables visuelles (ex. couleur, valeur, texture) et des techniques de rehaussement (transparence, annotation, déplacement). Trois approches distinctes ont été utilisées : 1) discussion directe avec des personnes œuvrant en géomatique, 2) entrevue face à face avec des notaires et 3) questionnaire en ligne avec des groupes ciblés. L’utilisabilité mesurée en termes d’efficacité, d’efficience et de degré de satisfaction a servi aux comparaisons des expérimentations. Les principaux résultats de cette recherche sont : 1) Une liste de tâches visuelles notariales utiles à la délimitation des unités de propriété dans le contexte de la visualisation 3D de condominium ; 2) Des recommandations quant à l'adéquation de huit variables visuelles et de trois techniques de rehaussement afin d’optimiser la réalisation d’un certain nombre de tâches notariales ; 3) Une analyse comparative de la performance de ces variables entre une visualisation 2D et 3D.3D visualization is being widely used in GIS (geographic information system) and CAD (computer-aided design) applications. It has also been introduced in cadastre studies to better communicate overlaps to the viewer, where the property units vertically stretch over or cover one part of the land parcel. Researchers believe that 3D visualization could provide viewers with a more intuitive perception, and it has the capability to demonstrate overlapping property units in condominiums unambiguously. However, 3D visualization has many challenges compared with 2D visualization. Many cadastre researchers adopted 3D visualization without thoroughly investigating the potential users, the visual tasks for decision-making, and the appropriateness of their representation design. Neither designers nor users may be aware of the risk of producing an inadequate 3D visualization, especially in an era when 3D visualization is relatively novel in the cadastre domain. With a general aim to improve the 3D visualization of cadastre data, this dissertation addresses the design of the 3D cadastre model from a graphics semiotics viewpoint including visual variables and enhancement techniques. The research questions are, firstly, what is the suitability of the visual variables and enhancement techniques in the 3D cadastre model to support the intended users' decision-making goal of delimitating condominium property units, and secondly, what are the perceptual properties of visual variables in 3D visualization compared with 2D visualization? This dissertation firstly identifies the theoretical framework for the interpretation of visual variables in 3D visualization as well as cadastre-related knowledge with literature review. Then, we carry out a preliminary evaluation of the feasibility of visual variables and enhancement techniques in a form of an expert-group review. With the result of the preliminary evaluation, this research then performs the hypothetico-deductive scientific approach to establishing a list of hypotheses to be validated by empirical tests regarding the suitability of visual variables and enhancement techniques in a cartographic representation of property units in condominiums for 3D visualization. The evaluation is based on the usability specification, which contains three measurements: effectiveness, efficiency, and preference. Several empirical tests are conducted with cadastral users in the forms of face-to-face interviews and online questionnaires, followed by statistical analysis. Size, shape, brightness, saturation, hue, orientation, texture, and transparency are the most discussed and used visual variables in existing cartographic research and implementations; thus, these eight visual variables have been involved in the tests. Their perceptual properties exhibited in the empirical test with concrete 3D models in this work are compared with those in a 2D visualization, which is derived from a literature-based synthesis. Three enhancement techniques, including labeling, 3D explosion, and highlighting, are tested as well. There are three main outcomes of this work. First, we established a list of visual tasks adapted to notaries for delimiting property units in the context of 3D visualization of condominium cadastres. Second, we describe the suitability of eight visual variables (Size, Shape, Brightness, Saturation, Hue, Orientation, Texture, and Transparency) of the property units and three enhancement techniques (labeling, 3D explosion and highlighting) in the context of 3D visualisation of condominium property units, based on the usability specification for delimitating visual tasks. For example, brightness only shows good performance in helping users distinguish private and common parts in the context of 3D visualization of property units in condominiums. As well, color hue and saturation are effective and preferable. The third outcome is a statement of the perceptual properties’ differences of visual variables between 3D visualization and 2D visualization. For example, according to Bertin (1983)’s definition, orientation is associative and selective in 2D, yet it does not perform in a 3D visualization. In addition, 3D visualization affects the performance of brightness, making it marginally dissociative and selective

Applying RGB- and thermal-based vegetation indices from UAVs for high-throughput field phenotyping of drought tolerance in forage grasses

The persistence and productivity of forage grasses, important sources for feed production, are threatened by climate change-induced drought. Breeding programs are in search of new drought tolerant forage grass varieties, but those programs still rely on time-consuming and less consistent visual scoring by breeders. In this study, we evaluate whether Unmanned Aerial Vehicle (UAV) based remote sensing can complement or replace this visual breeder score. A field experiment was set up to test the drought tolerance of genotypes from three common forage types of two different species: Festuca arundinacea, diploid Lolium perenne and tetraploid Lolium perenne. Drought stress was imposed by using mobile rainout shelters. UAV flights with RGB and thermal sensors were conducted at five time points during the experiment. Visual-based indices from different colour spaces were selected that were closely correlated to the breeder score. Furthermore, several indices, in particular H and NDLab, from the HSV (Hue Saturation Value) and CIELab (Commission Internationale de l’éclairage) colour space, respectively, displayed a broad-sense heritability that was as high or higher than the visual breeder score, making these indices highly suited for high-throughput field phenotyping applications that can complement or even replace the breeder score. The thermal-based Crop Water Stress Index CWSI provided complementary information to visual-based indices, enabling the analysis of differences in ecophysiological mechanisms for coping with reduced water availability between species and ploidy levels. All species/types displayed variation in drought stress tolerance, which confirms that there is sufficient variation for selection within these groups of grasses. Our results confirmed the better drought tolerance potential of Festuca arundinacea, but also showed which Lolium perenne genotypes are more tolerant