Fast Radial Basis Functions for Engineering Applications

Biancolini, Marco Evangelos

doi:10.1007/978-3-319-75011-8

Cited by 91 publications

(73 citation statements)

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“…The implementation of the adaptive NNs eliminates the phase of offline training, and it also eliminates the requirement of the exact structure of disturbances or robot dynamics. In particular, the radialbased-function (RBF) [28], NNs is employed in this paper as M M M(q q q)q q q r +C C C(q q q,q q q)q q q r +g g g(q q q) = W W W θ θ θ (q q q,q q q,q q q r ,q q q r )+E E E, (17) where W W W is the ideal weight matrix, θ θ θ (·) is the activation function, and E E E is the vector of approximation error.…”

Section: B Modeling and Planningmentioning

confidence: 99%

Development of an Autonomous Sanding Robot with Structured-Light Technology

Huo

Chen

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Large demand for robotics and automation has been reflected in the sanding works, as current manual operations are labor-intensive, without consistent quality, and also subject to safety and health issues. While several machines have been developed to automate one or two steps in the sanding works, the autonomous capability of existing solutions is relatively low, and the human assistance or supervision is still heavily required in the calibration of target objects or the planning of robot motion and tasks. This paper presents the development of an autonomous sanding robot, which is able to perform the sanding works on an unknown object automatically, without any prior calibration or human intervention. The developed robot works as follows. First, the target object is scanned then modeled with the structured-light camera. Second, the robot motion is planned to cover all the surfaces of the object with an optimized transition sequence. Third, the robot is controlled to perform the sanding on the object under the desired impedance model. A prototype of the sanding robot is fabricated and its performance is validated in the task of sanding a batch of wooden boxes. With sufficient degrees of freedom (DOFs) and the module design for the end effector, the developed robot is able to provide a general solution to the autonomous sanding on many other different objects.

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Section: B Modeling and Planningmentioning

confidence: 99%

Development of an Autonomous Sanding Robot with Structured-Light Technology

Huo

Chen

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…Extensive literature dealing with RBFs can be retrieved in dedicated textbooks or online . In this section, a brief excursus about RBF basics is reported in order to facilitate the understanding of the proposed mapping algorithm.…”

Section: Mathematical Backgroundmentioning

confidence: 99%

A balanced load mapping method based on radial basis functions and fuzzy sets

Biancolini

Chiappa

Giorgetti

et al. 2018

Numerical Meth Engineering

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A common issue in multiphysics analysis regards a reliable way for loose couplings, because the same object is modeled using different mesh refinements, each one suited for a proper field of physics. Output data originating from a simulation environment are transferred as input data to a different model to run a new analysis. It is strongly desirable that such information transfers in a conservative way in terms of general balance. This paper faces the problem of pressure mapping between widely dissimilar meshes. The proposed procedure yields two steps: pressure interpolation by means of radial basis functions and fuzzy subset correction. The first step is pointwise interpolation that exploits a series of basis functions. The second step applies to the outcome of the first one to reestablish load balance between the two models through the introduction of a smooth correction field. Practical tests from the aeronautical field allow validating the method.

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“…A parametric C-type structured mesh is built near the airfoil, surrounded by an unstructured triangular mesh (figure 3a). The mesh is adapted onto the new shape using a radial basis function mesh morphing software (RBF Morph Ansys Fluent Add On) for every tested geometry according to the approach presented in (Biancolini, Viola, and Riotte 2014;Biancolini, Costa, et al 2016;Biancolini 2018) . For each Re, the grid is uniformly scaled in order to achieve a non-dimensional wall-coordinate y + ≤ 0.5.…”

Section: D Urans Solver Setupmentioning

confidence: 99%

Optimal airfoil’s shapes by high fidelity CFD

Viola

Chapin

Speranza

et al. 2018

Aircraft Eng & Aerospace Tech

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Purpose-There is an increasing interest in airfoils that modify their shape to adapt at the flow conditions. As an example of application, we search the optimal 4-digit NACA airfoil that maximises the lift-over-drag ratio for a constant lift coefficient of 0.6, from Re = 10 4 to 3 × 10 6. Design/methodology/approach-We consider a γ − Re θt transition model and a κ − ω SST turbulence model with a covariance matrix adaptation evolutionary optimisation algorithm. The shape is adapted by radial basis functions mesh morphing using four parameters (angle of attack, thickness, camber, maximum camber position). The objective of the optimisation is to find the airfoil that enables a maximum lift-overdrag ratio for a target lift coefficient of 0.6. Findings-The computation of the optimal airfoils confirmed the expected increase with Re of the lift-overdrag ratio. However, while the observation of efficient biological fliers suggests that the thickness increases monotonically with Re, we find that it is constant but for a 1.5% step increase at Re = 3 × 10 5. Practical implications-We propose and validate an efficient high fidelity method for the shape optimisation of airfoils that can be adopted to define robust and reliable industrial design procedures. Originality/value-We show that the difference in the numerical error between 2D and 3D simulations is negligible, and that the numerical uncertainty of the 2D simulations is sufficiently small to confidently predict the aerodynamic forces across the investigated range of Re.

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Fast Radial Basis Functions for Engineering Applications

Cited by 91 publications

References 0 publications

Development of an Autonomous Sanding Robot with Structured-Light Technology

Development of an Autonomous Sanding Robot with Structured-Light Technology

A balanced load mapping method based on radial basis functions and fuzzy sets

Optimal airfoil’s shapes by high fidelity CFD

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