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2009 | z. 227 | 3-144
Tytuł artykułu

Uniwersalna metoda modelowania zachowań robota mobilnego wykorzystująca architekturę uogólnionych sieci komórkowych

Warianty tytułu
EN
Generalized cellular network architecture for modelling the behavior of a mobile robot
Języki publikacji
PL
Abstrakty
PL
Celem pracy jest przedstawienie systemu nawigacyjnego robota mobilnego, w którym wykorzystano, wzorowane na strukturze układu nerwowego organizmów żywych, neuronowe sieci komórkowe (CNN). Chua zaprojektował CNN w 1988 r. Są to sieci jednowarstwowe, w których neurony są rozłożone w formie regularnej siatki. Chua rozszerzył definicje CNN w 1997 r. Przyjęto, że sieć składa się z lokalnie połączonych komórek. Taki model może być traktowany jako uogólnienie automatów skończonych. Każda komórka sieci jest pewną samodzielną jednostką obliczeniową. Stan komórki zależy od stanu komórek sąsiednich, wartości sygnałów wejściowych oraz przyjętego szablonu oddziaływań. W wyniku przetwarzania informacji w sposób równoległy przez dużą liczbę bardzo prostych powtarzalnych układów, jest możliwe rozwiązanie skomplikowanych zagadnień w czasie rzeczywistym. Praca składa się z sześciu rozdziałów. Rozdział 1 zawiera informacje wstępne, precyzuje cel pracy i podaje algorytm systemu nawigacyjnego. W rozdziale 2 opisano ogólny model sieci komórkowych. Jako ilustrację zastosowań klasycznej architektury sieci przedstawiono wyniki badań dotyczące przetwarzania obrazów. Eksperymenty opisywanego systemu przeprowadzono dla systemów bezpieczeństwa pracy, ale zaproponowane metody mogą być wykorzystywane w systemach nawigacyjnych robotów mobilnych. Badania były prowadzone we współpracy z Centralnym Instytutem Ochrony Pracy. W rozdziale 3 przedstawiono oryginalną rastrowo-obiektywną reprezentację sceny. Opisano algorytmy tworzenia dwu- i trójwymiarowych map otoczenia na podstawie wskazań dalmierza laserowego LMS 200 oraz kamery dookólnej. Zaprezentowano hierarchiczną strukturę sieci komórkowych, które umożliwiają wykrywanie obiektów charakterystycznych i agregację danych, pochodzących z różnego typu urządzeń pomiarowych. W kolejnej części pracy (rozdz. 4) opisano moduł określenia przemieszczenia robota na podstawie obserwacji zmian położenia znaczników naturalnych. Wykorzystano znaną metodę filtrów cząsteczkowych. Oryginalnym wkładem autorki jest uwzględnienie w procesie generowania cząsteczek globalnej informacji o środowisku. Ta cecha algorytmu umożliwia skrócenie czasu obliczeń. Charakterystyczne cechy otoczenia są wykrywane za pomocą CNN. Istotnym fragmentem (rozdz. 5) jest przedstawienie zastosowania uogólnionych sieci komórkowych w zagadnieniu planowania działań. Zaprezentowano schemat systemu, który umożliwia rozwiązanie szerokiej klasy zagadnień w sposób efektywny. Opracowano algorytmy, które umożliwiają: generowanie trasy dla robota o dowolnym kształcie, uwzględniające promień skrętu i dynamikę pojazdu oraz kryterium jakości obszarów (np. nasłonecznienia), rozwiązanie problemu planowania trasy w sytuacji, gdy pojazd powinien przejechać przez określone punkty (np. aby uzupełnić zapasy energii), przeszukiwanie pomieszczeń, planowanie trasy do obiektów opisywanych w sposób symboliczny. Przedstawiono też metodę planowania trasy z uwzględnieniem dopuszczalnych prędkości pojazdu. Rozdział 6 poświęcono modelowaniu zachowań grupowych - planowanie trajektorii dla zespołu robotów dążących do wspólnego celu niezależnie, w stadzie oraz określonym szyku. Na końcu pracy umieszczono podsumowanie wyników prowadzonych badań.
EN
The book provides an overview of applications of Cellular Neural Networks CNN for mobile robot navigation. CNN, a single-layer network defined on regular lattices, was introduced by Chua in 1988. This definition was extended in 1997 by the author. It is assumed that CNN consists of cell that interact locally. This tape of CNN can be viewed as a generalization of cellular automata. Neurons can be modeled as locally connected finite state machines. The state of a cell depends on the states of neighboring cells, values of input signals and values of templates. The present work consists of six chapter. After the introduction in chapter 1, chapter 2 described the CNN paradigm, In this section, the application of CNN for pattern recognition is presented. The research was conducted with cooperation with the Central Institute for Labour Protection - National Research Institute. The methods described in this chapter can be adapted to mobile robot navigation, in the case when the robot is equipped with a CCD camera. In chapter 3, the dual grid-based and feature-based method of map building is introduced. In this section, the algorithms of 2D and 3D map building, based on a laser range finder and an omnicamera sensor, are described. The natural landmarks of the environment where detected using CNN. In chapter 4, the method of mobile robot localization is described. Particle filters are used to solve the problem. Information about some global features of the environment is used for particle generation. This approach allows a reduction in the number of particles and makes the method more effective. In the main part of this book (chapter 5), the applications of CNN for task planning are presented. The algorithms solve the following problem: - path planning for robots of different shapes and holonomic constraints, - exploration of an environment, - path planning to goals which are described in symbolic way, - path planning in the case when the cost of traveling varies for different parts of the environment, - computation of optimal controls (linear and angular velocity) for the robot. A modified dynamic window approach is used to determine optimal controls (linear and angular velocities of the robot). In this method, the kinematic and dynamic constraints of the vehicle are taken into account. Optimal velocities are computed based on information stored in the CNN. In chapter 6, the method for path planning for a team cooperating robots is presented. The book ends with conclusions.
Wydawca

Rocznik
Tom
Strony
3-144
Opis fizyczny
Bibliogr. 294 poz., rys., tab.
Twórcy
  • Instytut Podstawowych Problemów Techniki Polskiej Akademii Nauk
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