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Dive into the research topics where Napoleon H. Reyes is active.

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Featured researches published by Napoleon H. Reyes.


Archive | 2006

Synthesizing Adaptive Navigational Robot Behaviours Using a Hybrid Fuzzy A* Approach

Antony P. Gerdelan; Napoleon H. Reyes

Previously, we have devised a novel Hybrid Fuzzy A* algorithm that seamlessly integrates the forward planning feature of A* and the refined reactionary robot maneuvering capabilities of Fuzzy Logic in a real-time simulation environment. This paper further explores the uncharted domain of synthesizing three primary robot maneuvering behaviours, namely target pursuit, obstacle avoidance and opponent evasion in an adaptive compact Hybrid Fuzzy A* navigation system. In addition, this work sheds some light onto the dark pits of the previous Fuzzy A* architecture proposed, as the former Hybrid approach did not account for the necessity of evasive behavior, and so modifications to the forward planning layer are deemed to be necessary. In light of this, this chapter presents a new undesirability component that is injected into the A* algorithm, as well as optimisations to the cascade of fuzzy systems architecture that calculates the robot speed and angle adaptively. Empirical results are also presented that attest to the algorithm’s robustness when faced with a formidable army of moving obstacles while in pursuit of a target, as well as evading multiple opponents.


Fuzzy Cognitive Maps for Applied Sciences and Engineering | 2014

Use and Perspectives of Fuzzy Cognitive Maps in Robotics

Ján Vaščák; Napoleon H. Reyes

Fuzzy Cognitive Maps (FCM) started in the last decade to penetrate to areas as decision-making and control systems including robotics, which is characterized by its distributiveness, need for parallelism and heterogeneity of used means. This chapter deals with specification of needs for a robot control system and divides defined tasks into three basic decision levels dependent on their specification of use as well as applied means. Concretely, examples of several FCMs applications from the low and middle decision levels are described, mainly in the area of navigation, movement stabilization, action selection and path cost evaluation. Finally, some outlooks for future development of FCMs are outlined.


Memetic Computing | 2010

Finding near optimum colour classifiers: genetic algorithm-assisted fuzzy colour contrast fusion using variable colour depth

Heesang Shin; Napoleon H. Reyes

This paper presents a complete Fuzzy-Genetic-based self-calibrating illumination intensity-invariant colour classification system. Previously, we have developed a novel fuzzy colour processing technique called Fuzzy Colour Contrast Fusion (FCCF) that selectively and adaptively corrects colours depicting target colour objects. FCCF has been proven to compensate for the effects of spatially varying illumination intensities in the scene, in various colour spaces. However, FCCF requires a huge set of parameters that is extremely tedious to calibrate by hand. To address these problems, we present a system that combines FCCF with a Heuristic-Assisted Genetic Algorithm (HAGA). FCCF-HAGA fully automates the fine-tuning of all colour descriptors, with significantly improved colour classification accuracy. Furthermore, we have reduced FCCF’s storage space requirements by processing colour channels selectively at varying colour depths. This is accomplished by combining a Variable Colour Depth (VCD) algorithm with FCCF that searches for the most effective colour depth for each colour channel. Our results show that for all cases, the FCCF-HAGA-VCD combination improves pie-slice colour classification. For six different target colours, under varying illuminations, the hybrid algorithm was able to yield 17.63% higher overall colour classification accuracy as compared to the pure fuzzy approach. Furthermore, it was able to reduce LUT storage space requirements by 78.06%, as compared to the full-colour depth LUT.


international conference on neural information processing | 2008

Towards a generalised hybrid path-planning and motion control system with auto-calibration for animated characters in 3D environments

Antony P. Gerdelan; Napoleon H. Reyes

Intelligent navigation and path-finding for computer-animated characters in graphical 3D environments is a major design challenge facing programmers of simulations, games, and cinematic productions. Designing agents for computer-animated characters that are required to both move intelligently around obstacles in the environment, and do so in a psycho-visually realistic way with smooth motion is often a too-difficult challenge - designers generally sacrifice intelligent navigation for realistic movement or vice-versa. We present here a specially adapted hybrid fuzzy A* algorithm as a viable solution to meet both of these challenges simultaneously. We discuss the application of this algorithm to animated characters and outline our proposed architecture for automatic tuning of this system.


international conference on neural information processing | 2008

Variable colour depth look-up table based on fuzzy colour processing

Heesang Shin; Napoleon H. Reyes

This paper presents an application of a Fuzzy Colour Contrast Fusion (FFCF) algorithm in compensating for reduced colour depth representation of a colour image while maintaining efficient colour sensitivity that suffices for accurate real-time colour-based object recognition. We investigate the effects of applying fuzzy colour contrast rules to varying colour depth as we extract the optimal rule combination. The experiments were performed using the robot soccer game set-up with spatially varying illumination intensities on the scene. Interestingly, our results show that for most cases, colour depth reduction could actually improve colour classification via a pie-slice technique, in a modified rg-chromaticity colour space. For 6 different colours, the algorithm was able to yield 6.5% higher overall accuracy with only one-twelfth of LUT size than the full colour depth LUT.


international conference on neural information processing | 2008

Hybrid Fuzzy Colour Processing and Learning

Daniel P. Playne; Vrushank D. Mehta; Napoleon H. Reyes; Andre L. C. Barczak

We present a robust fuzzy colour processing system with automatic rule extraction and colour descriptors calibration for accurate colour object recognition and tracking in real-time. The system is anchored on the fusion of fuzzy colour contrast rules that operate on the red, green and blue channels independently and adaptively to compensate for the effects of glare, shadow, and illumination variations in an indoor environment. The system also utilises a pie-slice colour classification technique in a modified rg-chromaticity space. Now, colour operations can be defined linguistically to allow a vision system to discriminate between similarly coloured objects more effectively. The validity and generality of the proposed fuzzy colour processing system is analysed by examining the complete mapping of the fuzzy colour contrast rules for each target colour object under different illumination intensities with the presence of similarly coloured objects. The colour calibration algorithm is able to extract colour descriptors in a matter of seconds as compared to manual calibration usually taking hours to complete. Using the robot soccer environment as a test bed, the algorithm is able to calibrate colours with excellent accuracy.


pacific rim international conference on artificial intelligence | 2010

Colour object classification using the fusion of visible and near-infrared spectra

Heesang Shin; Napoleon H. Reyes; Andre L. C. Barczak; Chee Seng Chan

Under extreme light conditions, a conventional colour CCD camera would fail to render the colours of an object properly as the visible spectrum is either faintly observable in the scene or the presence of glare corrupts the colours sensed. On the other hand, for darkly-illuminated areas, a near-infrared (NIR) camera would sense stronger more discriminable signals, but could only render the scene monochromatically. The underlying challenge in this research is how to adaptively integrate a monochromatic NIR image with a faintly rendered colour image of the same darkly or very brightly lit scene to give rise to improved colour classification results that discriminate colours more effectively. This research proposes a Fuzzy-Genetic colour processing algorithm that adaptively marries together the visible and near-infrared spectra signals for the purpose of colour object recognition. The experiments were done on a scene with spatially varying illumination intensities, using Fujifilms UV/IR Super CCD camera with a sensitivity range between 380nm to 1000nm in conjunction with NIR filters. Results prove that the proposed multi-spectrum technique yields better colour classification results than utilizing the pure visible spectrum alone.


Revista De Informática Teórica E Aplicada | 2013

Tuning Fuzzy-Based Hybrid Navigation Systems Using Calibration Maps

Napoleon H. Reyes; Andre L. C. Barczak; Teo Susnjak

We present a novel approach for the tuning and assessment of a cascade of fuzzy logic systems, working cohesively for robot soccer navigation. We generate calibration maps to comprehensively examine the performance of the cascades, allowing for both the visualisation and quantification of the overall system performance. The experiments demonstrate how the proposed method captures the aggregate effect on system’s efficiency of even the slightest changes to the fuzzy rules. It also provides feedback on the mechanics of the fuzzy systems that could be held responsible for any shortcomings. Interestingly, without the aid of the proposed techniques, these minute changes are very difficult, if not impossible to identify through human visual inspection per se. Although the example provided in the paper reflects navigation in the Mirosot league robot soccer scope, the proposed calibration method lends itself amenable to other problem domains where target pursuit and obstacle avoidance behaviours are a necessity. It is also worth-noting that the calibration method can be utilised as a fitness function to a Genetic Algorithm or other optimisation techniques, for a fully-automated calibration. Lastly, we discuss how the calibrated cascade of fuzzy systems neatly integrate with the A* algorithm to produce a hybrid system for near-optimal navigation.


international symposium on neural networks | 2011

A Hybrid Fuzzy Q-learning algorithm for robot navigation

Sean W. Gordon; Napoleon H. Reyes; Andre L. C. Barczak

In the field of robot navigation, a number of different approaches have been proposed. One of these is Hybrid Fuzzy A* (HFA), which uses the A* algorithm to determine the long term path from the robot to some target, and fuzzy logic to move the robot to each waypoint along the path. This algorithm has been shown to be fast and effective in simulation, however A* is limited in the variables it can consider and the challenges it can be applied to. We propose replacing A* with Q-learning, which does not suffer from these limitations. We demonstrate the ability of Hybrid Fuzzy Q-Learning (HFQL) to navigate a robot to a given target and then apply the algorithm to a different challenge where the robot needs to balance reaching the target quickly against picking up as many subgoals as possible.


computer analysis of images and patterns | 2011

A new ensemble-based cascaded framework for multiclass training with simple weak learners

Teo Susnjak; Andre L. C. Barczak; Napoleon H. Reyes; Kenneth A. Hawick

We present a novel approach to multiclass learning using an ensemblebased cascaded learning framework. By implementing a multiclass cascaded classifier with AdaBoost, we show how detection runtimes are accelerated since only a subset of the ensemble is executed, thus making the classifiers suitable for computer vision applications. We also propose a new multiclass weak learner and demonstrate the frameworks ability to achieve arbitrarily low training errors in conjunction with it. We tested our algorithm against AdaBoost.OC, ECC and M2 multiclass learning methods, on seven benchmark UCI datasets. In our experiments, we found that our framework achieves higher accuracy on five out of seven datasets and displays faster runtime efficiency in all cases.

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Ján Vaščák

Technical University of Košice

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Peter Sincak

Technical University of Košice

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