On the Relationship between Classical Grid Search and Probabilistic Roadmaps

LaValle, Steven M.; Branicky, M.S.; Lindemann, Stephen R.

doi:10.1007/978-3-540-45058-0_5

Cited by 110 publications

(40 citation statements)

References 34 publications

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“…In the discrete case, the S-Transform is the projection of the vector defined by the time seriesp½kT, onto a spanning set of vectors [11]. Each basis vector (of the Fourier transform) is divided into N localized vectors by an element-by-element product with the Nshifted Gaussians, such that the sum of these Nlocalized vectors is original basis vector.…”

Section: Feature Extraction Using S-transformmentioning

confidence: 99%

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Power Disturbances Classification Using S-Transform Based GA–PNN

Manimala¹,

Selvi²

2014

J. Inst. Eng. India Ser. B

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The significance of detection and classification of power quality events that disturb the voltage and/or current waveforms in the electrical power distribution networks is well known. Consequently, in spite of a large number of research reports in this area, a research on the selection of proper parameter for specific classifiers was so far not explored. The parameter selection is very important for successful modelling of input-output relationship in a function approximation model. In this study, probabilistic neural network (PNN) has been used as a function approximation tool for power disturbance classification and genetic algorithm (GA) is utilised for optimisation of the smoothing parameter of the PNN. The important features extracted from raw power disturbance signal using S-Transform are given to the PNN for effective classification. The choice of smoothing parameter for PNN classifier will significantly impact the classification accuracy. Hence, GA based parameter optimization is done to ensure good classification accuracy by selecting suitable parameter of the PNN classifier. Testing results show that the proposed S-Transform based GA-PNN model has better classification ability than classifiers based on conventional grid search method for parameter selection. The noisy and practical signals are considered for the classification process to show the effectiveness of the proposed method in comparison with existing methods.

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Section: Feature Extraction Using S-transformmentioning

confidence: 99%

“…A mesh three-dimensional (3-D) of the S -transform output yields frequency-time, amplitude-time, and frequency-amplitude plots. The original software code developed by Stockwell [11] in Matlab has been modified for power quality waveform studies. The time-frequency contours for sag and swell are shown in Fig.…”

Section: Feature Extraction Using S-transformmentioning

confidence: 99%

Power Disturbances Classification Using S-Transform Based GA–PNN

Manimala¹,

Selvi²

2014

J. Inst. Eng. India Ser. B

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“…The star discrepancy is an important notion in equidistribution theory as well as in quasi-Monte Carlo techniques (see for example [3]). Recently, it was also used in probabilistic motion planning to enhance the sampling uniformity [12].…”

Section: Star Discrepancymentioning

confidence: 99%

Coverage-guided test generation for continuous and hybrid systems

Dang

Nahhal

2009

Form Methods Syst Des

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In this paper, we describe a formal framework for conformance testing of continuous and hybrid systems, using the international standard 'Formal Methods in Conformance Testing' FMCT. We propose a novel test coverage measure for these systems, which is defined using the star discrepancy notion. This coverage measure is used to quantify the validation 'completeness'. It is also used to guide input stimulus generation by identifying the portions of the system behaviors that are not adequately examined. We then propose a test generation method, which is based on a robotic motion planning algorithm and is guided by the coverage measure. This method was implemented in a prototype tool that can handle high dimensional systems (up to 100 dimensions).

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“…Latombe [21] provides a good exposition of these aforementioned techniques applied to robotic motion. More recently, methods that employ sampling algorithms became popular, such as the Probabilistic RoadMaps (PRMs) [25] and the Rapidly-Exploring Random Trees (RRTs) [8,12].…”

Section: Path Planning and Obstacle Avoidancementioning

confidence: 99%

On the construction of a RoboCup small size league team

et al. 2011

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The Robot Soccer domain has become an important artificial intelligence test bench and a widely studied research area. It is a domain with real, dynamic, and uncertain environment, where teams of robots cooperate and face adversarial competition. To build a RoboCup Small Size League (SSL) team able to compete in the world championship requires multidisciplinary research in fields like robotic hardware development, machine learning, multi-robot systems, computer vision, control theory, and mechanics, among others.This paper intends to provide insights about the aspects involved on the development of the RoboFEI RoboCup SSL robot soccer team and to present the contributions produced over its course. Among these contributions, a computer vision system employing an artificial neural network (ANN) to recognize colors, a heuristic algorithm to recognize partially detected objects, an implementation of the known rapidlyexploring random trees (RRT) path planning algorithm with Murilo Fernandes Martins thanks the support of CAPES through the grant 5031/06-0. additional rules, enabling the angle of approach of the robot to be controlled, and a layered strategy software system.Experimental results on real robots demonstrate the high performance of the vision system and the efficiency of the RRT algorithm implementation. Some strategy functions are also experimented, with empirical results showing their effectiveness.

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On the Relationship between Classical Grid Search and Probabilistic Roadmaps

Cited by 110 publications

References 34 publications

Power Disturbances Classification Using S-Transform Based GA–PNN

Power Disturbances Classification Using S-Transform Based GA–PNN

Coverage-guided test generation for continuous and hybrid systems

On the construction of a RoboCup small size league team

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