Identification of characteristic points of the radar image on the basis of "contour" invariants comparison

The article presents the one element of the autonomous maritime positioning system which task is the identification of characteristic points generated from the radar image. The system to fix position in automatic way requires extraction of characteristic points, their identification and subsequently in the last calculation step an application of classical methods of the radar navigation. The present article illustrates the characteristic point's identification algorithm based on the invariant representation of the radar image

Use genetic algorithms to construct testing tasks for the anti - collision system of an unmanned surface vehicle

Konstrukcja niezawodnego, automatycznego systemu antykolizyjnego dla Bezzałogowego Pojazdu Nawodnego wymaga intensywnego testowania. System musi być sprawdzony w wielu rożnych sytuacjach tak, aby można było stwierdzić, że jest bezpieczny i nie doprowadzi do kolizji. Tradycyjna metoda tworzenia zadań testowych polega na wykorzystaniu do tego celu człowieka. Projektant testow, bazując na swoim doświadczeniu, konstruuje kolejne testy starając się przy tym, aby utworzony przez niego zbior zadań testowych reprezentował wszystkie możliwe sytuacje, z ktorymi pojazd może mieć do czynienia na morzu. Problem jednak polega na tym, że człowiek nie jest w stanie przewidzieć wszystkich możliwych sytuacji, co może skutkować nieodpowiednim przygotowaniem systemu antykolizyjnego do pracy a w konsekwencji kolizją. W artykule zaproponowano inny sposob konstrukcji zadań testowych. Funkcję tą ma pełnić algorytm genetyczny, ktorego zadaniem jest poszukiwanie sytuacji stanowiących trudność dla systemu.To build a reliable automatic anti-collision system for an Unmanned Surface Vehicle it is necessary to implement an intensive testing procedure. In order for the system to guarantee safety at sea it has to be verified in many different situations. The traditional method for building such test tasks uses a test designer to create tests based on his or her experience; ideally the complete test set would represent all possible situations that the vehicle may face at sea. However, the problem is that a human cannot predict all possible situations, a flaw which may result in an inappropriate preparation of the anti-collision system and, in consequence, a collision. The following paper proposes another method for constructing testing tasks, a method that utilises a genetic algorithm. The algorithm's aim is to search for situations which may be difficult for the system or situations which are completely different from the ones tested so far

A quick algorithm for planning a path for a biomimetic autonomous underwater vehicle

Autonomous underwater vehicles are vehicles that are entirely or partly independent of human decisions. In order to obtain operational independence, the vehicles have to be equipped with specialized software. The task of the software is to move the vehicle along a trajectory while avoiding collisions. In its role of avoiding obstacles, the vehicle may sometimes encounter situations in which it is very difficult to determine what the next movement should be from an ad hoc perspective. When such a situation occurs, a planning component of the vehicle software should be run with the task of charting a safe trajectory between nearby obstacles. This paper presents a new path planning algorithm for a Biomimetic Autonomous Underwater Vehicle. The main distinguishing feature of the algorithm is its high speed compared with such classic planning algorithms as A*. In addition to presenting the algorithm, this paper also summarizes preliminary experiments intended to assess the effectiveness of the proposed algorithm

Selecting Generators for Creating Real Valued Detectors in Ship Immune System

The task of the ship immune system is to differentiate self objects, i.e. objects that are not dangerous to our ship, from other objects that can be a potential threat. To perform the task the system makes use of a set of detectors. The detectors imitate signatures of non-self objects and they are generated at random. In order for the detectors to be able to effectively perform their task they have to be constructed in appropriate way. Since, random generators are used to form detectors the problem is to select a generator producing the most effective detectors. In order to select an appropriate generator, experiments were carried out. In the experiments, the task of the ship immune system was to differentiate self and non-self ship radio stations. Results of the experiments are presented at the end of the paper

Organizacja procesu ewolucyjnego w kodowaniu asemblerowym

Assembler Encoding (AE) represents Artificial Neural Network (ANN) in the form of a simple program called Assembler Encoding Program (AEP). The task of AEP is to create the socalled Network Definition Matrix (NDM) including all the information necessary to construct ANN. AEPs and in consequence ANNs are formed by means of evolutionary techniques. To make AE an effective tool for creating ANNs it is necessary to appropriately organize all the evolutionary processes responsible for generating AEPs, i.e., it is necessary to properly select values of different parameters controlling the evolutionary process mentioned. To determine optimal conditions of the evolution in AE, experiments in a predator-prey problem were performed. The results of the experiments are presented at the end of the paper.Kodowanie asemblerowe jest metodą wykorzystującą metody ewolucyjne do tworzenia sieci neuronowych. W kodowaniu asemblerowym sieci neuronowe ewoluują w wielu oddzielnych populacjach. Stworzenie pojedynczej sieci neuronowej wymaga połączenia elementów pochodzących z różnych populacji. Aby sieci neuronowe tworzone w ten sposób były wysokiej jakości konieczne jest odpowiednie sterowanie ewolucją w każdej populacji. Artykuł prezentuje wyniki badań, których głównym celem było określenie zasad prowadzenia ewolucji w Kodowaniu Asemblerowym