1,721,031 research outputs found
Relatively optimal control with characteristic polynomial assignment and output feedback
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APPARATO E PROCEDIMENTO PER LA PROGRAMMAZIONE DI ROBOT PER MEZZO DI DIMOSTRAZIONE
No full textThe present invention relates to an apparatus for programming robots based on a novel passive pointing device including fiducial markers which enables interaction with one or more cameras through a computer implemented method. By taking advantage of a camera integral to the robot, the computer implemented method and the pointing device enable a tracking mechanism that during robot programming makes possible to change the position of the robot in real time in order to maintain substantially constant the relative pose between the on-board camera and the pointing device
In this way, the human operator can advantageously set the poses of the robot tool by means of demonstration i.e., by placing the pointer in the desired position and orientation during execution of the computer implemented method.
In the preferred embodiment, the apparatus includes a passive pointer (i.e. without electronic unit to detect the pose) and a camera mounted on the robot’s wrist.
Thanks to the tracking mechanism, the programming apparatus and method achieves higher precision than known systems and larger working-space. The precision is advantageously adjustable according to needs
Relatively optimal control: a static piecewise-affine solution
No full textA relatively optimal control is a stabilizing controller that, without initialization nor feedforwarding and tracking the optimal trajectory, produces the optimal (constrained) behavior for the nominal initial condition of the plant. In a previous work, for discrete–time linear systems, we presented a linear dynamic relatively optimal control. Here we provide a static solution, namely a dead–beat piecewise affine state–feedback controller based on a suitable partition of the state space into polyhedral sets. The vertices of the polyhedra are the states of the optimal trajectory, hence a bound for the complexity of the controller is known in advance. We also show how to obtain a controller that is not deadbeat by removing the zero terminal constraint while guaranteeing stability. Finally, we compare the proposed static compensator with the dynamic one
Hamiltonian path planning in constrained workspace
Full text linkA methodology to plan the trajectories of robots that move in an n-dimensional Euclidean space, have to reach a target avoiding obstacles and are constrained to move in a region of the space is described. It is shown that if the positions of the obstacles are known then a Hamiltonian function can be constructed and used to define a collision-free trajectory. It is also shown that the method can be extended to the case in which the target or the obstacles (or both) move. Results of simulations for a pair of planar robots and a three degrees-of-freedom manipulator are finally reported
Simultaneous performance achievement via compensator blending
No full textIn this paper we consider the problem of designing a state-feedback controller that simultaneously achieves different optimality criteria defined on different input–output pairs. Precisely, if r “optimal” target transfer functions are given (as the result of local “optimal” controllers), it is shown that (under mild assumptions) there exists a unique controller capable of replicating these transfer functions in the closed-loop system, so simultaneously achieving the performances inherited by the chosen local transfer functions. An explicit and constructive procedure (we refer to such procedure as “compensator blending”) is provided. The possibility of designing a stable blending compensator or the generalization to dynamic local controllers or time varying systems are also discussed. We finally consider the dual version of the problem, precisely, we show how to achieve simultaneous optimality by filter blending
Learning-based automatic classification of lichens from images
Full text linkBiomonitoring plays a crucial role in the assessment of air quality, as it allows to estimate the presence of pollutants, by measuring deviations from normality of the components of an ecosystem. Lichens are among the organisms most commonly used as bioindicators. The present study deals with the classification of lichen taxa from images, by means of a machine learning process based on patch classification. A given image is divided in non-overlapping patches, and each of them undergoes feature extraction and classification, eventually being associated to a category. Three different methods for extracting patch descriptors are investigated: (i) handcrafted descriptors based on classical feature extractor algorithms, (ii) convolutional neural networks employed as feature extractors, and (iii) scattering networks, which combine wavelet convolutions and nonlinear operators. For each of these methods, the descriptors are used as inputs for a classification algorithm. The whole process is evaluated in terms of classification accuracy, empirically determining the most appropriate parameters for the different models implemented. By using the dataset of lichens of this study, best results (∼ 0.89 accuracy) are obtained with a specific handcrafted descriptor (dense SIFT), thus providing insights on the kind of representation which is most suitable for the task
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