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Autonomous Formula Controlling
Harvan, Mário ; Janoušek, Vladimír (referee) ; Rozman, Jaroslav (advisor)
The aim of this paper is to analyze the problems of autonomous car system in the Formula Student competition. The paper focuses on design and implementation of a system that can identify race track, calculate the best route that car can follow, and control car to follow given route. The objective of the system is to control car so that it can go around the track in the shortest possible time. Part of the paper is a theoretical model of the vehicle, which allows an algorithm to calculate the maximum possible speed of the formula in each section of the track. Last section focuses on system testing inside simulator and comparison of different path finding algorithms.
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Mobile platform for testing autonomous vehicle sensors
Valla, Tomáš ; Ramík, Pavel (referee) ; Štětina, Josef (advisor)
The aim of this diploma thesis is to create a design of a mobile platform. The platform contains all the necessary electronics from conventional RC models and thus the RC control itself is guaranteed. It is also equipped with a light but solid platform, which is suitable for attaching the necessary sensors, lidars, cameras and other necessary accessories. Part of the work is also a brief overview of the division of RC models and evaluation of the components used for possible construction of the model. The final chapter is devoted to FEM analysis, where the load-bearing capacity of the platform under different load models is checked by means of structural analysis. Using modal analysis, the platform's own critical frequencies were determined and compared to the frequencies that are transmitted when driving on the road.
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Topics of the first and last mile in urban transport
Večerka, Marek ; Ramík, Pavel (referee) ; Štětina, Josef (advisor)
This bachelor thesis deals with the issue of the first and last mile in urban transport. It generally defines the issues of the first and last mile and provides an overview of available solutions. At the same time, it briefly looks at the future in the form of autonomous management. The individual solutions are described and subsequently related to the continent of Europe. The next step is to map and analyse the available options in the Statutory City of Brno. The result of this work is an evaluation of current available solutions and a proposal for further solving the problem of the first and last mile.
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Autonomous Slot-Car Driving with Adaptation to Unknown Track Shape
Nesvadba, Marek ; Bidlo, Michal (referee) ; Strnadel, Josef (advisor)
The goal of this bachelor thesis is design and implementation of slot car track mapping algorithm and algorithm that uses information about shape of the track to drive through it in the fastest time. Algorithm for mapping divides the track into sections (straights, corners, braking zones and corner exit zones) and algorithm for driving then uses information about driven distance and about shape of the track to set the speed of the car. The car is able to drive around the track with only the information about the track length without crashing, which is confirmed by experiments.
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Application of Reinforcement Learning in Autonomous Driving
Vosol, David ; Zbořil, František (referee) ; Janoušek, Vladimír (advisor)
This thesis is focused on the topic of reinforcement learning applied to a task of autonomous vehicle driving. First, the necessary fundamental theory is presented, including the state-of-the-art actor-critic methods. From them the Proximal policy optimization algorithm is chosen for the application to the mentioned task. For the same purpose, the racing simulator TORCS is used. Our goal is to learn a reinforcement learning agent in a simulated environment with the focus on a future real-world application to an RC scaled model car. To achieve this, we simulate the conditions of remote learning and control in the cloud. For that, simulation of network packet loss, noisy sensory and actuator data is done. We also experiment with the least number of vehicle's sensors required for the agent to successfully learn the task. Experiments regarding the vehicle's camera output are also carried out. Different system architectures are proposed, among others also with the aim to minimize hardware requirements. Finally, we explore the generalization properties of a learned agent in an unknown environment.
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Driver monitoring
Pieger, Matúš ; Bilík, Šimon (referee) ; Richter, Miloslav (advisor)
This master’s thesis deals with the design of systems for data collection which describe the driver’s behaviour in a car. This data is used to detect risky behaviour that the driver may commit due to inattention caused by the use of either lower or higher levels of driving automation. The thesis first describes the existing safety systems, especially in relation to the driver. Then it deals with the design of the necessary measuring scenes and the implementation of new systems based on the processing of input images which are obtained via the Intel RealSense D415 stereo camera. Every system is tested in a real vehicle environment. In the end the thesis contains an evaluation regarding the detection reliability of the created algorithms, it considers their shortcomings and possible improvements.
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Design and implementation of control program for mobile robot platform Turtlebot3 Burger
Filip, Jakub ; Lacko, Branislav (referee) ; Parák, Roman (advisor)
The aim of the bachelor thesis is the design and implementation of a control program for the mobile robotic platform TurtleBot3 Burger. The theoretical part of the bachelor's thesis defines the issue of mobile robotics, with a closer look at the various possibilities of locomotion, including examples from industrial practice. The TurtleBot3 mobile robot series belongs to the robotic platforms distributed by ROBOTIS, where their main feature is compatibility with the Robotic Operating System (ROS). The core of this system is licensed under the BSD, which guarantees open source code. The integration of ROS with the TurtleBot3 Burger model provides freely accessible robust libraries, which form the basis for understanding the control of a differentially controlled robot through ROS. In the practical part, the assembly and configuration of the robotic kit TurtleBot3 Burger is performed, including the introduction of key functionalities of this mobile platform, and the design of your own solution. The conclusion contains the justification of the mentioned proposal and the output after its implementation on a real robot.
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Neural Networks for Autonomous Car Driving
Dopita, Marek ; Hradiš, Michal (referee) ; Smrž, Pavel (advisor)
In this work, the principles of neural networks are introduced with a focus on autonomous vehicles. Based on this information, a proposal for the implementation of a system is created, which allows to drive a car without a driver. It builds on tools that allow easy creation and testing of autonomous vehicles. It is CARLA simulator and ranking.The proposal divides vehicle routes into three different situations. Each situation requires the use of different sensors, so a specific autonomous agent is created that is able to recognize the situation and switch between different neural network designs. Each such network is specific in its inputs and is taught in a specific situation.Programs are created that are able to easily collect a data set using the CARLA Leaderboard. Then, a way is introduced to how the collected data can be divided into categories so that each category can be used to learn its neural network.
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