Petr Dolezel

PID controller design for nonlinear oscillative plants using piecewise linear neural network

The paper presents recently introduced algorithm for PID controller tuning using pole assignment technique and piecewise-linear neural network. After the technique introduction, there is performed control simulation which proves, that this technique can be used to control of oscillative nonlinear systems.

The time optimal path-planning of mobile robots motion respecting the time cost of rotation

The paper describes the time optimal path-planning method designed for differential wheeled mobile robots operating on flat ground. The robots are used as support teaching tool by the path-planning problematic exercising. Whereas the exercise is designed for students without any prior knowledge about the path-planning, the graph version of the A* algorithm was chosen as the appropriate algorithm for the problematic introduction. The students are supposed to exercise the path-planning using the evaluation functions of various difficulties. The most complex of them is the evaluation function reflecting both the transportation time and the time required for a robot rotation. Its mathematical formulation is described in the paper and its functionality is shown in three case studies where the shortest time-path between two locations in a labyrinth is required to be found.

Multi Agent Environment for Modelling and Testing of Cooperative Behaviour of Agents

The article introduces a new universal software environment for multi agent simulation. The environment presents the model of reality and it is suitable for testing of the existing or new methods, especially in the field of agent coordination, cooperation or collaboration. The software is aimed to be used in any Java based simulation tool and to provide a consistent approach for gathering comparable results among these tools. Moreover, it can be applied in problems such as coordinated decision making, attitude alignment problem, robot position synchronization, dynamic obstacle avoidance and more others. The concept of this tool helps to create programs which are easily transferable and applicable to real robotic platforms.

Artificial neural network promotion

The paper presents the possibility of introducing artificial intelligence and especially artificial neural network methodology to students in interesting way. To be more specific, the artificial neural network is described through the design of NPCs artificial intelligence in simple computer game.

Detection of grapes in natural environment using feedforward neural network as a classifier

The recognition of wine grapes in images acquired in natural environment is a serious issue solved by researches dealing with precision viticulture. The detection of wine grapes of red kinds is a well managed problem. On the other hand, the detection of white grapes is still a challenging task. In this contribution, the classifier for white wine grapes recognition is introduced and evaluated. The classifier is based on an artificial neural network and is used in two ways which differ in image representation. Namely, the pixel intensities and histogram of oriented gradients are used for the representation of images. Then, feedforward multilayer neural network is applied as a classifier. The classifiers based on the histograms of oriented gradients seemed to be very effective - they were almost error free from the cross validation point of view and they performed well with the independent testing data sets, too. On the other hand, the representation using pixel intensities was stated as insufficient for classification using our approach.

An IoT Approach to Positioning of a Robotic Vehicle

This paper presents and evaluates one approach to the problems of automatic control of a vehicle movement in a large outdoor area. The positioning of the vehicle in the area is provided by iBeacons, located at the edges of the given surface. The iBeacon is a small and low-power device which periodically transmits its UUID (Universally Unique Identifier) number through the interface of a Bluetooth 4.x. The vehicle should be able to calculate its position according to the power of the signal, considering the location of the iBeacons. To be more specific, the triangulation method is applied to determine the position. According to the set of experiments presented at the end of the paper, the position error of a robotic vehicle is mostly less then 1 m.

Computer game as a tool for machine learning education

The effective education is one of very important and everlasting challenges of human society. With each generation of students, new approaches have to be implemented to keep the process of education prosperous. This paper introduces a small piece to a set of modern tools for education of Informatics and Electrical Engineering. To be more specific, an interactive software for machine learning testing and demonstration is presented in this paper. The software is designed especially to be used as a motivation and a first encounter to these areas of technical studies, while it supports individual efforts of the students. In the paper, the software architecture is described and, in the second half of the paper, some possibilities of software usage in education process are suggested.

Pattern recognition neural network as a tool for pest birds detection

Various kinds of vermin have been considered as a huge problem since primeval times. Over this period, means of protection against vermin have developed to be very quick and efficient. However, new goals in protection have appeared recently which reflects legislative changes in most countries. Public opinion has shifted towards greater environment protection. Nowadays, vermin control systems have turned from being used globally into local applications and from being applied preventively into casual usage. Thus, accurate vermin detection units are becoming very important parts of vermin control systems. This situation is valid in agricultural areas (e.g. vineyards) which are protected against pest birds, too. Reflecting on the current situation, a feedforward multilayer artificial neural network, aimed on detection of European starling in vineyards, is presented in this paper. Except a description and validation of the detection method, the idea of the comprehensive protection system is also outlined in this paper.

Neural Network as a Tool for Detection of Wine Grapes

The recognition of wine grapes in real-life images is a serious issue solved by researches dealing with precision viticulture. The detection of wine grapes of red varieties is a well mastered problem. On the other hand, the detection of white varieties is still a challenging task. In this contribution, detectors designed for recognition of white wine grapes in real-life images are introduced and evaluated. Two representations of object images are considered in this paper; namely, vector of normalized pixel intensities and histograms of oriented gradients. In both cases, classifiers are realized using feedforward multilayer neural networks. The detector based on the histograms of oriented gradients has proven to be very effective by cross-validation. The results obtained by its evaluation on independent testing data are slightly worse; however, still very good. On the other hand, the representation using the vector of normalized pixel intensities was stated as insufficient.

European starling detection in agricultural areas using multilayer artificial neural network

The use of neural network classifier to recognize a pest bird in agricultural areas is presented in this contribution. Firstly, the idea of comprehensive system of protection against pest birds is outlined. Then, the method of detection is described, the process of neural network design is illustrated and, in the end, the neural network is validated using data gathered in fields.