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The thesis deals with the analysis, software design and the study of 3D kinematic model of robotic manipulator ROB 2-6. It creates the control software for this manipulator, containing user interface based on MFC applications. Moreover, the thesis is concentrated on the solution of various problems, e.g. non-linearity of servomotor or the limitation of manipulator´s attainability. Finally, it poses a documentation of the single project and offers new possibilities of further development in the control of this manipulator.

This diploma project is focused on possible applications of computational geometry methods for robot motion planning among static and dynamic obstacles, particularly based on global robot motion planning by means of generalised Voronoi diagrams. The main effort was to convert this complex geometric and analytic problem to graph theory environment where the tasks of planning and searching paths between pairs of the graph vertices are effeciently solvable. The Voronoi diagram is created considering the whole searching space, while edges of this diagram satisfy that the distance from the surrounding obstacles is maximised and the path found along the Voronoi diagram edges is optimised from the point of view of its security (and it is collision-free).

This work is dedicated to robot path planning with using principles of dynamic programing in discrete state space. Theoretical part is dedicated to actual situation in this field and to principle of applying Markov decission process to path planning. Practical part is dedicated to implementation of two algorithms based on MDP principles.

Diploma thesis deals with image processing for outdoor environment mobile robot. In first part, the problem is analyzed, general solution is proposed and suitable image processing methods are presented. In second part presented methods are tested and methods with best results are proposed. In third part is particular solution tested on real data.

This diploma´s thesis analyzes methods for robot path planning by means of algorithms AO*. The practical part focuses on the implementation of selected methods AO*, which are designed for planning under uncertainty environment. There was created the simulation program in this work. Simulation program enables testing the methods, that were implemented.

Work deal about self-organizing maps, especially about Kohonen self-organizing map. About creating of aplication, which realize creating and learning of self-organizing map. And about usage of self-organizing map for self-localization of robot.

The main focus of this diploma thesis is to design controlling system for robotic chassis moving on horizontal bolt nut of globular elements. This thesis includes analysis of movements of the chassis and definition of instructions for controlling level. Instructions for controlling level are compiled in constructed simulation programme. This thesis also contains design of electronic components and requirements for electronics of controlling subsystems.

This thesis deals with robot modelling and simulation of the robot behavior. At first we discuss a benefit of this kind of simulation. Next we describe the general scheme of robot. After that we select the environment where robot will be simulated in. There is a conception of this robot and explanation of the implementation in chosen environment. We continue with design and realization of the communication interface of this robot. The result of this procedure is robot with independent movement able to response to the obstructions and avoid them. Robot is able to create a map of the environment and use this map to find out the optimal way to the scheduled goal using the A* algorithm and achieve this goal. There is a possibility to use more robots in the scene. These communicate and the nearest one is able to get the goal.

Title: Evolution and Learning of Virtual Robots Author: RNDr. Peter Krčah Department: Department of Software and Computer Science Education Supervisor: RNDr. František Mráz, CSc. Abstract: Evolutionary robotics uses evolutionary algorithms to automatically design both body and controller of a robot. We describe two contributions to automated design of virtual robotic creatures. First, we introduce a nature-inspired method that allows virtual robots to modify their morphology through lifetime learning. We show that such morphological plasticity makes it possible to evolve robots that can dynamically adjust their morphology to the environment they are placed into. We also show that by reshaping the fitness landscape, learning reduces computation cost required to evolve a robot with a given target fitness even in a single environment. In the second contribution, we show that for certain problems in evolutionary robotics, premature convergence to local optima can be avoided by ignoring the original objective and searching for any novel behaviors instead (a technique known as Novelty Search). Keywords: Evolution of Virtual Creatures, Body-brain Coevolution, Morphological Plasticity, Neural Networks, Learning