|
|
Indoor Robot - Local Navigation
Matějka, Lukáš ; Šolc, František (referee) ; Žalud, Luděk (advisor)
This thesis deals with the problematic of design and realization of autonomous mobile robot, specifically with the subsystem for local navigation for a robot controlled by global navigation based on self-organizing neuron map, possible chassis design and control of these designs. Obstacle detection system is designed as well as optimum direction finding algorithm. Final section of this thesis concerns the possibilities of future improvements and development.
|
|
|
Autonomous mobile robot motion planning strategy design
Doseděl, Miroslav ; Králík, Jan (referee) ; Věchet, Stanislav (advisor)
This work is concerned with planning the movement of the robot in space and near plants. The main goal is to realize the movement of a robot designed for operation in indoor areas of buildings for watering indoor plants. In the first part of the thesis, existing algorithms for motion planning and obstacle avoidance are presented. In the next part, a search dealing with Framework ROS is written. The individual movements performed by the robot are then described and testing on the real robot is carried out at the end.
|
|
|
Self-Driving of a Model Car
Hazucha, Ivan ; Šimek, Václav (referee) ; Bidlo, Michal (advisor)
The aim of this thesis is to demonstrate options for self-driving model cars, focused on local path planning methods and obstacle avoidance. As a part of the project, the model was supplemented by a computing platform Raspberry Pi and appropriate sensors. Specifically, a 2D LiDAR sensor was used for detection and measuring the distance of surrounding objects, an incremental rotary encoder for measuring the distance travelled and current speed, and a gyroscope to keep track of the vehicle's relative orientation. Subsequently, a control system was implemented. This system is able to receive and process sensor data, use it to estimate vehicle's current location, compute an optimal trajectory in an uncharted environment, and control the vehicle's actuators accordingly. The result is a functional model car able to navigate in an unknown environment and reach specified goals by following a trajectory, dynamically generated depending on the surrounding obstacles.
|
|
|
BOIDS Method for Swarm Simulation
Burda, Radek ; Král, Jiří (referee) ; Zbořil, František (advisor)
This work primarily deals with C.Reynolds's model of flocking -BOIDS - and uses the model as a basis for creating a swarm simulation. It discusses methods for obstacle avoidance and principle of forces arbitration (flocking rules, obstacle avoidance and goal satisfying) to properly avoid conflicting of behaviours. Furhermore some of other approaches to flocking simulation are mentioned while their pros and cons are taken up. Last but not least proceeding of creating a graphic environment in Blender for demonstrating boids behavior in the final 3D application is described.
|
|
|
Controlling of Vehicles Formation
Revický, Peter ; Kapinus, Michal (referee) ; Rozman, Jaroslav (advisor)
The goal of this thesis is to create system for formation management of wheeled vehicles with kinematic constraints. The work presents the way to control vehicle and how to manage formation in presence of obstacles. Algorithms used for vehicle control are based on potential fields. Whole system is implemented in Unity game engine in 2D enviroment. The system is then tested on various scenarios such as passing through narrow passage, obstacle partially blocking formation, dynamic obstacle avoidance etc.
|
|
|
Obstacle Avoidance System
Dražil, Jan ; Raichl, Petr (referee) ; Novotný, Josef (advisor)
This thesis deals with methods for the problem of navigation and movement of autonomous vehicles between obstacles. The thesis first describes in general terms algorithms for motion planning and obstacle avoidance. Then, the thesis discusses the PX4 firmware for UAV control. The thesis proposes a custom obstacle avoidance algorithm designed for UAVs. The sensor used for obstacle detection is a stereo camera. This algorithm was implemented in Python using the ROS framework and tested in the Gazebo simulation environment. The results are discussed.
|
|
|
Autonomous mobile robot motion planning strategy design
Doseděl, Miroslav ; Králík, Jan (referee) ; Věchet, Stanislav (advisor)
This work is concerned with planning the movement of the robot in space and near plants. The main goal is to realize the movement of a robot designed for operation in indoor areas of buildings for watering indoor plants. In the first part of the thesis, existing algorithms for motion planning and obstacle avoidance are presented. In the next part, a search dealing with Framework ROS is written. The individual movements performed by the robot are then described and testing on the real robot is carried out at the end.
|
|
|
Obstacle Avoidance System
Dražil, Jan ; Raichl, Petr (referee) ; Novotný, Josef (advisor)
This thesis deals with methods for the problem of navigation and movement of autonomous vehicles between obstacles. The thesis first describes in general terms algorithms for motion planning and obstacle avoidance. Then, the thesis discusses the PX4 firmware for UAV control. The thesis proposes a custom obstacle avoidance algorithm designed for UAVs. The sensor used for obstacle detection is a stereo camera. This algorithm was implemented in Python using the ROS framework and tested in the Gazebo simulation environment. The results are discussed.
|
|
|
Self-Driving of a Model Car
Hazucha, Ivan ; Šimek, Václav (referee) ; Bidlo, Michal (advisor)
The aim of this thesis is to demonstrate options for self-driving model cars, focused on local path planning methods and obstacle avoidance. As a part of the project, the model was supplemented by a computing platform Raspberry Pi and appropriate sensors. Specifically, a 2D LiDAR sensor was used for detection and measuring the distance of surrounding objects, an incremental rotary encoder for measuring the distance travelled and current speed, and a gyroscope to keep track of the vehicle's relative orientation. Subsequently, a control system was implemented. This system is able to receive and process sensor data, use it to estimate vehicle's current location, compute an optimal trajectory in an uncharted environment, and control the vehicle's actuators accordingly. The result is a functional model car able to navigate in an unknown environment and reach specified goals by following a trajectory, dynamically generated depending on the surrounding obstacles.
|
|
|
Road Detection Using Data From Mobile Robot
Camera
Peška, Jaroslav
The paper is focused on developing a road detection algorithm that uses only data from a mobile robot’s camera. Key requirements are low latency and relatively low power requirements. Presented algorithm makes use of machine learning, where the neural network is fed not only image data, but also select additional inputs.
|
guest ::
