11 research outputs found

    Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions

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    Welcome to ROBOTICA 2009. This is the 9th edition of the conference on Autonomous Robot Systems and Competitions, the third time with IEEE‐Robotics and Automation Society Technical Co‐Sponsorship. Previous editions were held since 2001 in Guimarães, Aveiro, Porto, Lisboa, Coimbra and Algarve. ROBOTICA 2009 is held on the 7th May, 2009, in Castelo Branco , Portugal. ROBOTICA has received 32 paper submissions, from 10 countries, in South America, Asia and Europe. To evaluate each submission, three reviews by paper were performed by the international program committee. 23 papers were published in the proceedings and presented at the conference. Of these, 14 papers were selected for oral presentation and 9 papers were selected for poster presentation. The global acceptance ratio was 72%. After the conference, eighth papers will be published in the Portuguese journal Robótica, and the best student paper will be published in IEEE Multidisciplinary Engineering Education Magazine. Three prizes will be awarded in the conference for: the best conference paper, the best student paper and the best presentation. The last two, sponsored by the IEEE Education Society ‐ Student Activities Committee. We would like to express our thanks to all participants. First of all to the authors, whose quality work is the essence of this conference. Next, to all the members of the international program committee and reviewers, who helped us with their expertise and valuable time. We would also like to deeply thank the invited speaker, Jean Paul Laumond, LAAS‐CNRS France, for their excellent contribution in the field of humanoid robots. Finally, a word of appreciation for the hard work of the secretariat and volunteers. Our deep gratitude goes to the Scientific Organisations that kindly agreed to sponsor the Conference, and made it come true. We look forward to seeing more results of R&D work on Robotics at ROBOTICA 2010, somewhere in Portugal

    Behavior-based Control for Service Robots inspired by Human Motion Patterns : a Robotic Shopping Assistant

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    Es wurde, unter Verwendung menschenĂ€hnlicher Bewegungsmuster und eines verhaltensbasierten Ansatzes, eine Steuerung fĂŒr mobile Serviceroboter entwickelt, die Aufgabenplanung, globale und lokale Navigation in dynamischen Umgebungen, sowie die gemeinsame AufgabenausfĂŒhrung mit einem Benutzer umfasst. Das Verhaltensnetzwerk besteht aus Modulen mit voneinander unabhĂ€ngigen Aufgaben. Das komplexe Gesamtverhalten des Systems ergibt sich durch die Vereinigung der Einzelverhalten (\u27Emergenz\u27)

    Medical SLAM in an autonomous robotic system

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    One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilities by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This thesis addresses the ambitious goal of achieving surgical autonomy, through the study of the anatomical environment by Initially studying the technology present and what is needed to analyze the scene: vision sensors. A novel endoscope for autonomous surgical task execution is presented in the first part of this thesis. Which combines a standard stereo camera with a depth sensor. This solution introduces several key advantages, such as the possibility of reconstructing the 3D at a greater distance than traditional endoscopes. Then the problem of hand-eye calibration is tackled, which unites the vision system and the robot in a single reference system. Increasing the accuracy in the surgical work plan. In the second part of the thesis the problem of the 3D reconstruction and the algorithms currently in use were addressed. In MIS, simultaneous localization and mapping (SLAM) can be used to localize the pose of the endoscopic camera and build ta 3D model of the tissue surface. Another key element for MIS is to have real-time knowledge of the pose of surgical tools with respect to the surgical camera and underlying anatomy. Starting from the ORB-SLAM algorithm we have modified the architecture to make it usable in an anatomical environment by adding the registration of the pre-operative information of the intervention to the map obtained from the SLAM. Once it has been proven that the slam algorithm is usable in an anatomical environment, it has been improved by adding semantic segmentation to be able to distinguish dynamic features from static ones. All the results in this thesis are validated on training setups, which mimics some of the challenges of real surgery and on setups that simulate the human body within Autonomous Robotic Surgery (ARS) and Smart Autonomous Robotic Assistant Surgeon (SARAS) projects

    Medical SLAM in an autonomous robotic system

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    One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilities by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This thesis addresses the ambitious goal of achieving surgical autonomy, through the study of the anatomical environment by Initially studying the technology present and what is needed to analyze the scene: vision sensors. A novel endoscope for autonomous surgical task execution is presented in the first part of this thesis. Which combines a standard stereo camera with a depth sensor. This solution introduces several key advantages, such as the possibility of reconstructing the 3D at a greater distance than traditional endoscopes. Then the problem of hand-eye calibration is tackled, which unites the vision system and the robot in a single reference system. Increasing the accuracy in the surgical work plan. In the second part of the thesis the problem of the 3D reconstruction and the algorithms currently in use were addressed. In MIS, simultaneous localization and mapping (SLAM) can be used to localize the pose of the endoscopic camera and build ta 3D model of the tissue surface. Another key element for MIS is to have real-time knowledge of the pose of surgical tools with respect to the surgical camera and underlying anatomy. Starting from the ORB-SLAM algorithm we have modified the architecture to make it usable in an anatomical environment by adding the registration of the pre-operative information of the intervention to the map obtained from the SLAM. Once it has been proven that the slam algorithm is usable in an anatomical environment, it has been improved by adding semantic segmentation to be able to distinguish dynamic features from static ones. All the results in this thesis are validated on training setups, which mimics some of the challenges of real surgery and on setups that simulate the human body within Autonomous Robotic Surgery (ARS) and Smart Autonomous Robotic Assistant Surgeon (SARAS) projects

    Robust hybrid central/self-organising multi-agent systems in intersections without traffic lights

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    [no abstract

    Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming

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    Animal production (e.g., milk, meat, and eggs) provides valuable protein production for human beings and animals. However, animal production is facing several challenges worldwide such as environmental impacts and animal welfare/health concerns. In animal farming operations, accurate and efficient monitoring of animal information and behavior can help analyze the health and welfare status of animals and identify sick or abnormal individuals at an early stage to reduce economic losses and protect animal welfare. In recent years, there has been growing interest in animal welfare. At present, sensors, big data, machine learning, and artificial intelligence are used to improve management efficiency, reduce production costs, and enhance animal welfare. Although these technologies still have challenges and limitations, the application and exploration of these technologies in animal farms will greatly promote the intelligent management of farms. Therefore, this Special Issue will collect original papers with novel contributions based on technologies such as sensors, big data, machine learning, and artificial intelligence to study animal behavior monitoring and recognition, environmental monitoring, health evaluation, etc., to promote intelligent and accurate animal farm management

    INTERACT 2015 Adjunct Proceedings. 15th IFIP TC.13 International Conference on Human-Computer Interaction 14-18 September 2015, Bamberg, Germany

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    INTERACT is among the world’s top conferences in Human-Computer Interaction. Starting with the first INTERACT conference in 1990, this conference series has been organised under the aegis of the Technical Committee 13 on Human-Computer Interaction of the UNESCO International Federation for Information Processing (IFIP). This committee aims at developing the science and technology of the interaction between humans and computing devices. The 15th IFIP TC.13 International Conference on Human-Computer Interaction - INTERACT 2015 took place from 14 to 18 September 2015 in Bamberg, Germany. The theme of INTERACT 2015 was "Connection.Tradition.Innovation". This volume presents the Adjunct Proceedings - it contains the position papers for the students of the Doctoral Consortium as well as the position papers of the participants of the various workshops

    Physiologie et génétique de la dynamique des réserves corporelles des ovins allaitants dans un milieu contraignant

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    Depuis quelques annĂ©es, l’élevage doit rĂ©pondre Ă  de nouvelles contraintes liĂ©es au changement climatique et adopter des principes proposĂ©s par l’agroĂ©cologie pour assurer sa durabilitĂ©. L’utilisation d’animaux plus robustes peut permettre de rĂ©pondre Ă  ces nouveaux dĂ©fis. Une des composantes de cette robustesse est la dynamique des rĂ©serves corporelles (DRC ; alternance de pĂ©riodes de mobilisation (MO) et de reconstitution (RE) des RC). Ce mĂ©canisme biologique permet aux ruminants de faire face aux pĂ©riodes avec un bilan Ă©nergĂ©tique nĂ©gatif (BEN). L’objectif de ma thĂšse est d’approfondir les connaissances sur ce sujet en caractĂ©risant la DRC au cours de plusieurs cycles de production et en Ă©tudiant le dĂ©terminisme gĂ©nĂ©tique de cette dynamique chez les ovins allaitants. Les donnĂ©es utilisĂ©es proviennent de suivis longitudinaux d’état corporel et de poids vif de brebis allaitantes Romane, Ă©levĂ©es en plein air intĂ©gral Ă  La Fage (Causses du Larzac). La caractĂ©risation de la DRC indique qu’il existe plusieurs grands types de trajectoires de variation d’état corporel au cours des stades physiologiques des brebis. Les diffĂ©rences de niveau et de forme de ces trajectoires s’expliquent principalement par de la variabilitĂ© interindividuelle. La modĂ©lisation mathĂ©matique de la DRC a permis de dĂ©finir des critĂšres synthĂ©tiques des capacitĂ©s de MO et RE des RC. Par ailleurs, de faibles corrĂ©lations, mais favorables, entre la DRC et d’autres performances de production indiquent que plus la brebis mobilise ses RC durant les pĂ©riodes de BEN, meilleures sont les performances de production. L’étude du dĂ©terminisme gĂ©nĂ©tique de la DRC a permis de mettre en Ă©vidence qu’il s’agit d’un caractĂšre hĂ©ritable et que les mĂ©canismes de MO et RE des RC sont fortement liĂ©s gĂ©nĂ©tiquement. Le nombre Ă©levĂ© et les faibles effets des zones du gĂ©nome associĂ©es Ă  la DRC suggĂšrent un dĂ©terminisme polygĂ©nique. Enfin, de nombreux gĂšnes candidats ont Ă©tĂ© identifiĂ©s dans ces rĂ©gions gĂ©nomiques dĂ©jĂ  dĂ©crits pour leurs liens avec des caractĂšres d’adipositĂ©, de croissance et les mĂ©tabolismes Ă©nergĂ©tique. Ainsi, l’utilisation de la DRC en sĂ©lection gĂ©nĂ©tique peut ĂȘtre envisagĂ©e chez les ruminants, mĂȘme si des travaux complĂ©mentaires doivent ĂȘtre poursuivis notamment par l’étude des interactions gĂ©notype x milieu sur ce caractĂšre
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