<p>  </p> <p>With robot utilization reaching $300 million in the hospitality industry, this paper aims to examine the difference in customer response between two types of anthropomorphic features (cute vs. cool) of service robots. Four scenario-based experiments (2 [robot appearances: cute vs. cool] x 2 [customer–company relationship norms: communal vs. exchange]) were employed in two different contexts (Study 1: service successful and Study 2: failure). The results showed that cute robots elicit higher customer satisfaction, repatronage intention, and willingness to spread positive word of mouth when customers were in a communal relationship with a company. The difference was significant only in the situation in which the robots’ service failed. This study offers the industry guidelines to decide on robot design according to their relationship with the customer and develops the topic of anthropomorphism in robots in that it looked into the different traits within anthropomorphism rather than human likeness versus nonhuman likeness.</p>

This compilation thesis presents two main contributions in path planning and obstacle avoidance, as well as an integration of the proposed modules with other frameworks to enable resilient robotic missions in complex environments.In general, through different types of robotic missions it is important to have a collision tolerant and reliable system, both regarding potential risks from collisions with dynamic and static obstacles, but also to secure the overall mission success.%Particularly, a common trend in the presented work is safety regarding collisions with dynamic and static obstacles, as well as reliable overall systems that are capable of executing missions. The work included in this thesis presents the risk-aware path planner D$^*_+$ that is capable of planning traversable paths for both ground and aerial robots. D$^*_+$ is developed on top of D$^*$-lite with a risk layer close to occupied space, modeling the unknown areas as a risk, and is implemented with a dynamic map to enable updates and adjustments to a changing environment. The risk layer aids in solving two common challenges with path planning for real robots: a) it creates a safety margin that gives free space between the path and obstacles so that robots with the corresponding size can follow the path, and b) it masks smaller holes in walls that occur when building maps from real data. Using a dynamic map makes it possible to use D$^*_+$ for an exploration mission, it also enables for the re-planning of the path if the environment changes for example, if an obstacle suddenly blocks a path, a new path will be planned. D$^*_+$ have been tested in different real-life experiments with both an Unmanned Areal Vehicle (UAV) and a quadruped-legged robot and shown to produce traversable paths in different application scenarios, such as exploration, return to base, and navigation on known maps. This thesis also presents an obstacle avoidance architecture for velocity objects, structured around an object detection and tracking scheme that is combined with non-linear model predictive controller (NMPC) to plan the avoidance maneuver. %that uses a Convolutional Neural Network to detect obstacles that are tracked so they can be avoided by a non-linear model predictive controller (NMPC).In this case, the detection is done with the Convoluitonal Neural Network (CNN) You Only Lock Once v4 (YOLO) where the most certain human is tracked with a Kalman filter, and the velocity of the human is estimated.The proposed scheme models the object motion as constant velocity, which is utilized from the NMPC to plan control inputs for the robot to avoid the identified obstacle. A merit of the approach is that the avoidance maneuver does not only consider the current identification position, but also considers the motion prediction of the object. This avoidance framework proved to be capable to avoid non-cooperative obstacles, such as humans moving towards it.Due to the fact that the avoidance is starting when a future collision is predicted, the avoidance maneuver is started early enough to avoid obstacles with a higher velocity than a classic ``static obstacle'' radius approach can handle. An additional aim of this thesis is to showcase that the proposed contributions can be applied in full robotic missions/frameworks. Thus, this thesis presents a search and rescue mission with a quadruped-legged robot and a UAV on a partially known map to find the location of survivors and other objects of related interest. In this mission, the quadruped-legged robot carries the UAV to the edge of the known map from where it launches the UAV that then explores and detects any survival and other relevant objects.Also, an autonomy solution, based on Boston dynamics' quadruped-legged robot Spot, for enabling a map-based navigation in confined environments has been developed and tested. This Spot solution enables the robot to navigate to a user-selected point, rotate in the desired direction, and instruct the UAV, in the combined search and rescue mission, to take off.

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This thesis presents an innovative agricultural automation technique which focuses on addressing the significant perception challenges posed by occlusion within environments such as farms and greenhouses. Automated systems tasked with duties like pruning face considerable difficulties due to occlusion, complicating the accurate identification of plant features. To tackle these challenges, this work introduces a novel approach utilizing a LiDAR camera mounted on a robot arm, enhancing the system's ability to scan plants and dynamically adjust the arm's trajectory based on machine learning-derived segmentation. This adjustment significantly increases the detection area of plant features, improving identification accuracy and efficiency. Building on foreground isolation and instance segmentation, the thesis then presents an automated method for identifying optimal pruning points using best pose view images of indeterminate tomato plants. By integrating advanced image processing techniques, the proposed method ensures the pruning process by targeting branches with the highest leaf load. Experimental validation of the proposed method was conducted in a simulated environment, where it demonstrated substantially enhanced performance. In terms of pruning point identification, the method achieved impressive results with 94% precision, 90% recall, and 92% F1 score for foreground isolation. Furthermore, the segmentation of isolated images significantly outperformed non-isolated ones, with improvements exceeding 30%, 27%, and 30% in precision, recall, and F1 metrics, respectively. This validation also confirmed the method's effectiveness in accurately identifying pruning points, achieving a 67% accuracy rate when compared against manually identified pruning points. These results underscore the robustness and reliability of the approach in automating pruning processes in agricultural settings. / Master of Science / This thesis explores new methods for improving automated farming systems, particularly focusing on enhancing tasks like pruning where visibility of plant features can be significantly obstructed by overlapping leaves and branches. Central to this study is the development of an innovative approach using a special camera mounted on a robotic arm, which scans plants to determine the best vantage points for precise interactions. This setup not only identifies the optimal positions for viewing but also adjusts the robot's movements in real-time to ensure it can accurately perform pruning task. The innovative approach employed here leverages advanced technology to dynamically adjust the trajectory of the robotic arm based on real-time imaging. This enables the robot to better detect essential features of plants, which is crucial to make informed decision of where to prune the plant. By improving the robot's ability to clearly see and interact with plants, the system facilitates more precise and efficient operations. Tests conducted in simulated environments have demonstrated that this method significantly enhances the robot's capability to isolate and identify plant features accurately. These improvements make it possible for the robot to subsequently identify pruning points, potentially reducing the time and labor typically required in traditional manual operations. Overall, this research indicates that integrating advanced sensing and machine learning technologies into agricultural automation can revolutionize farming practices, making them more efficient and less dependent on human labor, especially in environments where traditional methods are less effective.

A robótica social representa um ramo da interação humano-robô que visa desenvolver robôs para atuar em ambientes não estruturados em parceria direta com seres humanos. O relatório A Roadmap for U.S. Robotics From Internet to Robotics, de 2013, preconiza a obtenção de resultados promissores em 12 anos desde que condições apropriadas sejam disponibilizadas para a área. Uma das condições envolve a utilização de ambiente de referência para desenvolver, avaliar e comparar o desempenho de sistemas cognitivos. Este ambiente é denominado Robot City com atores, cenários (casas, ruas, cidade) e auditores. Até o momento esse complexo ambiente não se concretizou, possivelmente devido ao elevado custo de implantação e manutenção de uma instalação desse porte. Nesta dissertação é proposto um caminho alternativo através da definição e implementação do simulador de sistemas cognitivos denominado Robot House Simulator (RHS). O simulador RHS tem como objetivo disponibilizar um ambiente residencial composto por sala e cozinha, no qual convivem dois agentes, um robô humanoide e um avatar humano. O agente humano é controlado pelo usuário do sistema e o robô é controlado por uma arquitetura cognitiva que determina o comportamento do robô. A arquitetura cognitiva estabelece sua percepção do ambiente através de informações sensoriais supridas pelo RHS e modeladas por uma ontologia denominada OntSense. A utilização de uma ontologia garante rigidez formal aos dados sensoriais além de viabilizar um alto nivel de abstração. O RHS tem como base a ferramenta de desenvolvimento de jogos Unity sendo aderente ao conceito de código aberto com disponibilização pelo repositório online GitHub. A validação do sistema foi realizada através de experimentos que demonstraram a capacidade do simulador em prover um ambiente de validação para arquiteturas cognitivas voltadas à robótica social. O RHS é pioneiro na integração de um simulador e uma arquitetura cognitiva, além disto, é um dos poucos direcionados para robótica social provendo rica informação sensorial, destacando-se o modelamento inédito disponibilizado para os sentidos de olfato e paladar. / Social robotics represents a branch of human-robot interaction that aims to develop robots to work in unstructured environments in direct partnership with humans. The Roadmap for Robotics from the Internet to Robotics, 2013, predicts achieving promising results in 12 years as long as appropriate conditions are made available to the area. One of the conditions involves the use of a reference environment to develop, evaluate and compare the performance of cognitive systems. This environment is called Robot City with actors, scenarios (houses, streets, city) and auditors. To date, this complex environment has not been materialized, possibly due to its high cost of installing and maintaining. In this dissertation an alternative way is proposed through the definition and implementation of the simulator of cognitive systems called Robot House Simulator (RHS). The RHS simulator aims to provide a residential environment composed of living room and kitchen, in which two agents live together, a humanoid robot and a human avatar. The human avatar is controlled by the user of the system and the robot is controlled by a cognitive architecture that determines the behavior of the robot. The cognitive architecture establishes its perception of the environment through sensorial information supplied by the RHS and modeled by an ontology called OntSense. The use of an ontology guarantees formal rigidity to the sensory data in addition to enabling a high level of abstraction. The RHS simulator is based on the Unity game engine and is adheres to the open source concept, available on the GitHub online repository. The validation of the system was performed through experiments that demonstrated the simulators ability to provide a validation environment for cognitive architectures aimed at social robotics. The RHS simulator is a pioneer in the integration of a simulator and a cognitive architecture. In addition, it is one of the few for social robotics to provide rich sensory information where it is worth noting the unprecedented modeling available to the senses of smell and taste.

Le développement croissant de la chirurgie robotique pose le problème de la formation. Cette nouvelle technologie tend à suppléer dans les procédures complexes la coelioscopie. Elle nécessite une adaptation du chirurgien. Il est, en effet, nécessaire de maîtriser à la fois le télémanipulateur et les procédures chirurgicales, qui ne sont pas de simples transpositions des gestes coelioscopiques. Initialement, nous avons réalisé un historique du développement de la chirurgie mini-invasive coelioscopique et robotique, ainsi qu'un historique de l'apprentissage de la chirurgie. Puis, nous nous sommes intéressés à l'apprentissage de la robotique. Les simulateurs de chirurgie coelioscopique sont très couramment employés dans l'apprentissage. En robotiques, ils ont fait récemment leur apparition sur le marché. Nous avons étudié la validité du simulateur dV-Trainer dans l'apprentissage de la chirurgie robotique. Nous avons démontré l'intérêt de cet outil dans l'acquisition de la gestuelle et des automatismes propres au robot. Nous avons par ailleurs étudié l'impact d'une formation en micro-chirurgie sur les performances développées en chirurgie robotique car, au cours d'une étude préliminaire nous avions constaté que les micro-chirurgiens présentaient de meilleures aptitudes sur le simulateur de chirurgie robotique que ceux sans expérience en micro-chirurgie. Dans un troisième temps, nous nous sommes intéressés à la téléchirurgie à longue distance qui est impactée par deux contraintes que sont la latence de transmission et le volume des informations à transmettre. Une première étude a étudié l'impact du délai de transmission sur les performances des chirurgiens. Une deuxième étude a consisté à réaliser une évaluation subjective par des chirurgiens de la qualité de vidéos de chirurgie robotique compressées afin de déterminer un seuil de compression maximal acceptable / The huge expansion of minimally invasive robotic devices for surgery ask the question of the training of this new technology. Progress of robotic-assisted surgical techniques allows today mini- invasive surgery to be more accurate, providing benefits to surgeons and patients for complex surgical procedures. But, it resulted from an increasing need for training and development of new pedagogical strategies. Indeed, the surgeon has to master the telemanipulator and the procedure, which is different from a simple transposition of a laparoscopic skill. The first part of this work treats about historical development of minimal invasive surgery from laparoscopy to robotic surgery. We also develop the evolution of training program in surgery. Virtual simulators provide efficient tools for laparoscopy training. The second part of this work, study some possible solutions for robotic training. We assess the validity of a new robotic virtual simulator (dV-Trainer). We demonstrate the usefulness of this tool for the acquisition of the basic gesture for robotic surgery. Then, we evaluate the impact of a previous experience in micro-surgery on robotic training. We propose a prospective study comparing the surgical performance of micro-surgeons to that of general surgeons on a robotic simulator. We want to determine if this experience in micro-surgery could significantly improve the abilities and surgeons performance in the field of basic gesture in robotic surgery. The last part of the study also looks to the future. Currently, telesurgery need sophisticated dedicated technical resources. We want to develop procedures for clinical routine used. Therefore, we evaluate the impact of the delay on the surgical procedure. Also, reducing data volume allow decreasing latency. An appropriate solution to reduce the amount of data could be found by introducing lossy compression for the transmission using the well-known MPEG-2 and H-264 standards

A robótica social representa um ramo da interação humano-robô que visa desenvolver robôs para atuar em ambientes não estruturados em parceria direta com seres humanos. O relatório A Roadmap for U.S. Robotics From Internet to Robotics, de 2013, preconiza a obtenção de resultados promissores em 12 anos desde que condições apropriadas sejam disponibilizadas para a área. Uma das condições envolve a utilização de ambiente de referência para desenvolver, avaliar e comparar o desempenho de sistemas cognitivos. Este ambiente é denominado Robot City com atores, cenários (casas, ruas, cidade) e auditores. Até o momento esse complexo ambiente não se concretizou, possivelmente devido ao elevado custo de implantação e manutenção de uma instalação desse porte. Nesta dissertação é proposto um caminho alternativo através da definição e implementação do simulador de sistemas cognitivos denominado Robot House Simulator (RHS). O simulador RHS tem como objetivo disponibilizar um ambiente residencial composto por sala e cozinha, no qual convivem dois agentes, um robô humanoide e um avatar humano. O agente humano é controlado pelo usuário do sistema e o robô é controlado por uma arquitetura cognitiva que determina o comportamento do robô. A arquitetura cognitiva estabelece sua percepção do ambiente através de informações sensoriais supridas pelo RHS e modeladas por uma ontologia denominada OntSense. A utilização de uma ontologia garante rigidez formal aos dados sensoriais além de viabilizar um alto nivel de abstração. O RHS tem como base a ferramenta de desenvolvimento de jogos Unity sendo aderente ao conceito de código aberto com disponibilização pelo repositório online GitHub. A validação do sistema foi realizada através de experimentos que demonstraram a capacidade do simulador em prover um ambiente de validação para arquiteturas cognitivas voltadas à robótica social. O RHS é pioneiro na integração de um simulador e uma arquitetura cognitiva, além disto, é um dos poucos direcionados para robótica social provendo rica informação sensorial, destacando-se o modelamento inédito disponibilizado para os sentidos de olfato e paladar. / Social robotics represents a branch of human-robot interaction that aims to develop robots to work in unstructured environments in direct partnership with humans. The Roadmap for Robotics from the Internet to Robotics, 2013, predicts achieving promising results in 12 years as long as appropriate conditions are made available to the area. One of the conditions involves the use of a reference environment to develop, evaluate and compare the performance of cognitive systems. This environment is called Robot City with actors, scenarios (houses, streets, city) and auditors. To date, this complex environment has not been materialized, possibly due to its high cost of installing and maintaining. In this dissertation an alternative way is proposed through the definition and implementation of the simulator of cognitive systems called Robot House Simulator (RHS). The RHS simulator aims to provide a residential environment composed of living room and kitchen, in which two agents live together, a humanoid robot and a human avatar. The human avatar is controlled by the user of the system and the robot is controlled by a cognitive architecture that determines the behavior of the robot. The cognitive architecture establishes its perception of the environment through sensorial information supplied by the RHS and modeled by an ontology called OntSense. The use of an ontology guarantees formal rigidity to the sensory data in addition to enabling a high level of abstraction. The RHS simulator is based on the Unity game engine and is adheres to the open source concept, available on the GitHub online repository. The validation of the system was performed through experiments that demonstrated the simulators ability to provide a validation environment for cognitive architectures aimed at social robotics. The RHS simulator is a pioneer in the integration of a simulator and a cognitive architecture. In addition, it is one of the few for social robotics to provide rich sensory information where it is worth noting the unprecedented modeling available to the senses of smell and taste.

This diploma thesis deals with the design of a robotic workplace for deburring of a given part. The robot's task is to remove the machined part from the production machine, create a blank workpiece ready for machining, and finally deburr the the machined part. There are several proposals for the layout of the robotic cell, as well as the design of the end effector, the input tray for semi-finished products and a stand with tools for deburring. Subsequently, a simulation model of the designed robotic cell is created in the Siemens Process Simulate software. Using RSC modules, the exact resulting cell clock is determined. The whole process of creating a simulation model is detaily described. At the end of this thesis is an economic evaluation of the proposed solution.

This diploma thesis deals with the design and virtual commissioning of a robotic workplace designed for glueing and assembling a car headlight. The thesis contains a summary of available information on the matter of virtual commissioning and industrial robotics. It also contains a system analysis of the necessary equipment, followed by a design of several workplace variants. The final solution for which the 3D model is made is selected from them. With it, in the RobotStudio software, a simulation of all processes taking place at the workplace is created. On its base, a control program is created and debugged, and virtual commissioning of the workplace is performed.