958 resultados para 290301 Robotics and Mechatronics
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The design of an Autonomous Surface Vehicle for operation in river and estuarine scenarios is presented. Multiple operations with autonomous underwater vehicles and support to AUV missions are one of the main design goals in the ROAZ system. The mechanical design issues are discussed. Hardware, software and implementation status are described along with the control and navigation system architecture. Some preliminary test results concerning a custom developed thruster are presented along with hydrodynamic drag calculations by the use of computer fluid dynamic methods.
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Proceedings of the 10th Mediterranean Conference on Control and Automation - MED2002 Lisbon, Portugal, July 9-12, 2002
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International Conference on Advanced Robotics, Coimbra, Portugal, Julho 2003
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Teaching robotics to students at the beginning of their studies has become a huge challenge. Simulation environments can be an effective solution to that challenge where students can interact with simulated robots and have the first contact with robotic constraints. From our previous experience with simulation environments it was possible to observe that students with lower background knowledge in robotics where able to deal with a limited number of constraints, implement a simulated robotic platform and study several sensors. The question is: after this first phase what should be the best approach? Should the student start developing their own hardware? Hardware development is a very important part of an engineer's education but it can also be a difficult phase that could lead to discouragement and loss of motivation in some students. Considering the previous constraints and first year engineering students’ high abandonment rate it is important to develop teaching strategies to deal with this problem in a feasible way. The solution that we propose is the integration of a low-cost standard robotic platform WowWee Rovio as an intermediate solution between the simulation phase and the stage where the students can develop their own robots. This approach will allow the students to keep working in robotic areas such as: cooperative behaviour, perception, navigation and data fusion. The propose approach proved to be a motivation step not only for the students but also for the teachers. Students and teachers were able to reach an agreement between the level of demand imposed by the teachers and satisfaction/motivation of the students.
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This article presents a framework to an Industrial Engineering and Management Science course from School of Management and Industrial Studies using Autonomous Ground Vehicles (AGV) to supply materials to a production line as an experimental setup for the students to acquire knowledge in the production robotics area. The students must be capable to understand and put into good use several concepts that will be of utmost importance in their professional life such as critical decisions regarding the study, development and implementation of a production line. The main focus is a production line using AGVs, where the students are required to address several topics such as: sensors actuators, controllers and an high level management and optimization software. The presented framework brings to the robotics teaching community methodologies that allow students from different backgrounds, that normally don’t experiment with the robotics concepts in practice due to the big gap between theory and practice, to go straight to ”making” robotics. Our aim was to suppress the minimum start point level thus allowing any student to fully experience robotics with little background knowledge.
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Fractional Calculus (FC) goes back to the beginning of the theory of differential calculus. Nevertheless, the application of FC just emerged in the last two decades. In the field of dynamical systems theory some work has been carried out but the proposed models and algorithms are still in a preliminary stage of establishment. Having these ideas in mind, the paper discusses a FC perspective in the study of the dynamics and control of mechanical systems.
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This paper presents the TEC4SEA research infrastructure created in Portugal to support research, development, and validation of marine technologies. It is a multidisciplinary open platform, capable of supporting research, development, and test of marine robotics, telecommunications, and sensing technologies for monitoring and operating in the ocean environment. Due to the installed research facilities and its privileged geographic location, it allows fast access to deep sea, and can support multidisciplinary research, enabling full validation and evaluation of technological solutions designed for the ocean environment. It is a vertically integrated infrastructure, in the sense that it possesses a set of skills and resources which range from pure conceptual research to field deployment missions, with strong industrial and logistic capacities in the middle tier of prototype production. TEC4SEA is open to the entire scientific and enterprise community, with a free access policy for researchers affiliated with the research units that ensure its maintenance and sustainability. The paper describes the infrastructure in detail, and discusses associated research programs, providing a strategic vision for deep sea research initiatives, within the context of both the Portuguese National Ocean Strategy and European Strategy frameworks.
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Series: "Advances in intelligent systems and computing , ISSN 2194-5357, vol. 417"
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Many of our everyday tasks require the control of the serial order and the timing of component actions. Using the dynamic neural field (DNF) framework, we address the learning of representations that support the performance of precisely time action sequences. In continuation of previous modeling work and robotics implementations, we ask specifically the question how feedback about executed actions might be used by the learning system to fine tune a joint memory representation of the ordinal and the temporal structure which has been initially acquired by observation. The perceptual memory is represented by a self-stabilized, multi-bump activity pattern of neurons encoding instances of a sensory event (e.g., color, position or pitch) which guides sequence learning. The strength of the population representation of each event is a function of elapsed time since sequence onset. We propose and test in simulations a simple learning rule that detects a mismatch between the expected and realized timing of events and adapts the activation strengths in order to compensate for the movement time needed to achieve the desired effect. The simulation results show that the effector-specific memory representation can be robustly recalled. We discuss the impact of the fast, activation-based learning that the DNF framework provides for robotics applications.
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Cada cop més s’utilitzen robots en molts àmbits de la vida, els quals han de ser programats, i el fet que Microsoft s’impliqui aportant una eina de programació resulta interessant. Aprendre a programar robots i colònies de robots amb aquesta eina, la qual està tenint una bona acollida, és molt important, per això en aquest projecte s’ha fet una anàlisi crítica de MRS i del material docent associat a l’aplicació. Els resultats i conclusions obtinguts han estat, d’una banda, que és la millor eina de programació de robots, en termes generals, i d’altra, una guia iniciativa a l’entorn MRS, accessible a la direcció: http://shades.uab.cat/MSRS.
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BACKGROUND: Both non-traumatic and traumatic spinal cord injuries have in common that a relatively minor structural lesion can cause profound sensorimotor and autonomous dysfunction. Besides treating the cause of the spinal cord injury the main goal is to restore lost function as far as possible. AIM: This article provides an overview of current innovative diagnostic (imaging) and therapeutic approaches (neurorehabilitation and neuroregeneration) aiming for recovery of function after non-traumatic and traumatic spinal cord injuries. MATERIAL AND METHODS: An analysis of the current scientific literature regarding imaging, rehabilitation and rehabilitation strategies in spinal cord disease was carried out. RESULTS: Novel magnetic resonance imaging (MRI) based techniques (e.g. diffusion-weighted MRI and functional MRI) allow visualization of structural reorganization and specific neural activity in the spinal cord. Robotics-driven rehabilitative measures provide training of sensorimotor function in a targeted fashion, which can even be continued in the homecare setting. From a preclinical point of view, defined stem cell transplantation approaches allow for the first time robust structural repair of the injured spinal cord. CONCLUSION: Besides well-established neurological and functional scores, MRI techniques offer the unique opportunity to provide robust and reliable "biomarkers" for restorative therapeutic interventions. Function-oriented robotics-based rehabilitative interventions alone or in combination with stem cell based therapies represent promising approaches to achieve substantial functional recovery, which go beyond current rehabilitative treatment efforts.
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Neuroimaging of the self has focused on high-level mechanisms such as language, memory or imagery of the self and implicated widely distributed brain networks. Yet recent evidence suggests that low-level mechanisms such as multisensory and sensorimotor integration may play a fundamental role in self-related processing. In the present study we used visuotactile multisensory conflict, robotics, virtual reality, and fMRI to study such low-level mechanisms by experimentally inducing changes in self-location. Participants saw a video of a person's back (body) or an empty room (no-body) being stroked while a MR-compatible robotic device stroked their back. The latter tactile input was synchronous or asynchronous with respect to the seen stroking. Self-location was estimated behaviorally confirming previous data that self-location only differed between the two body conditions. fMRI results showed a bilateral activation of the temporo-parietal cortex with a significantly higher BOLD signal increase in the synchronous/body condition with respect to the other conditions. Sensorimotor cortex and extrastriate-body-area were also activated. We argue that temporo-parietal activity reflects the experience of the conscious 'I' as embodied and localized within bodily space, compatible with clinical data in neurological patients with out-of-body experiences.
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In cooperative multiagent systems, agents interac to solve tasks. Global dynamics of multiagent teams result from local agent interactions, and are complex and difficult to predict. Evolutionary computation has proven a promising approach to the design of such teams. The majority of current studies use teams composed of agents with identical control rules ("geneti- cally homogeneous teams") and select behavior at the team level ("team-level selection"). Here we extend current approaches to include four combinations of genetic team composition and level of selection. We compare the performance of genetically homo- geneous teams evolved with individual-level selection, genetically homogeneous teams evolved with team-level selection, genetically heterogeneous teams evolved with individual-level selection, and genetically heterogeneous teams evolved with team-level selection. We use a simulated foraging task to show that the optimal combination depends on the amount of cooperation required by the task. Accordingly, we distinguish between three types of cooperative tasks and suggest guidelines for the optimal choice of genetic team composition and level of selection
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We present a georeferenced photomosaic of the Lucky Strike hydrothermal vent field (Mid-Atlantic Ridge, 37°18’N). The photomosaic was generated from digital photographs acquired using the ARGO II seafloor imaging system during the 1996 LUSTRE cruise, which surveyed a ~1 km2 zone and provided a coverage of ~20% of the seafloor. The photomosaic has a pixel resolution of 15 mm and encloses the areas with known active hydrothermal venting. The final mosaic is generated after an optimization that includes the automatic detection of the same benthic features across different images (feature-matching), followed by a global alignment of images based on the vehicle navigation. We also provide software to construct mosaics from large sets of images for which georeferencing information exists (location, attitude, and altitude per image), to visualize them, and to extract data. Georeferencing information can be provided by the raw navigation data (collected during the survey) or result from the optimization obtained from imatge matching. Mosaics based solely on navigation can be readily generated by any user but the optimization and global alignment of the mosaic requires a case-by-case approach for which no universally software is available. The Lucky Strike photomosaics (optimized and navigated-only) are publicly available through the Marine Geoscience Data System (MGDS, http://www.marine-geo.org). The mosaic-generating and viewing software is available through the Computer Vision and Robotics Group Web page at the University of Girona (http://eia.udg.es/_rafa/mosaicviewer.html)
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In swarm robotics, communication among the robots is essential. Inspired by biological swarms using pheromones, we propose the use of chemical compounds to realize group foraging behavior in robot swarms. We designed a fully autonomous robot, and then created a swarm using ethanol as the trail pheromone allowing the robots to communicate with one another indirectly via pheromone trails. Our group recruitment and cooperative transport algorithms provide the robots with the required swarm behavior. We conducted both simulations and experiments with real robot swarms, and analyzed the data statistically to investigate any changes caused by pheromone communication in the performance of the swarm in solving foraging recruitment and cooperative transport tasks. The results show that the robots can communicate using pheromone trails, and that the improvement due to pheromone communication may be non-linear, depending on the size of the robot swarm.