With an increased push for commercial autonomous cars, the demand of high speed systems capable of performing in unstructured driving environments is growing. In this thesis, the behavior of a bio-inspired predator prey model is considered to stimulate a more organic response to obstacles and a moving target than existing algorithms. However, the current predator prey model has a disconnect between the desired velocities commanded and the torque signals provided to the motors due the dynamics of the vehicle not accounted for. This causes the vehicle to derail from its intended trajectory at sharp turns. In this study, we start by adding dynamic behavior to the unicycle model to account for the varying dynamics of the vehicle. A backstepping algorithm is developed to connect the predator-prey model commanding desired velocities to an appropriate torque controller for the motors of the vehicle. To account for the unknown dynamic model parameters an adaptive control approach is utilized. Three different controllers are developed and evaluated. Out of the three, the indirect MRAC backstepping controller is deemed unsuitable due to its limitations with handling unknown parameter structure. The direct MRAC backstepping is deemed suitable and therefore simulated and implemented on the vehicle. The newly derived controller is able to overcome the disconnect and allow the vehicle to optimally track its trajectory for a velocity range of 1 m/s to 9 m/s despite varying dynamics. Lastly, the L1 adaptive backstepping controller is introduced and simulated to provide an alternative, more robust solution to the direct MRAC backstepping controller. / Master of Science

As already clear from the title, this dissertation deals with autonomous locomotive robot path planning, based on machine learning. Robot path planning task is to find a path from initial to target position without collision with obstacles so that the cost of the path is minimized. Autonomous robot is such a machine which is able to perform tasks completely independently even in environments with dynamic changes. Path planning in dynamic partially known environment is a difficult problem. Autonomous robot ability to adapt its behavior to changes in the environment can be ensured by using machine learning methods. In the field of path planning the mostly used methods of machine learning are case based reasoning, neural networks, reinforcement learning, swarm intelligence and genetic algorithms. The first part of this thesis introduces the current state of research in the field of path planning. Overview of methods is focused on basic omnidirectional robots and robots with differential constraints. In the thesis, several methods of path planning for omnidirectional robot and robot with differential constraints are proposed. These methods are mainly based on case-based reasoning and genetic algorithms. All proposed methods were implemented in simulation applications. Results of experiments carried out in these applications are part of this work. For each experiment, the results are analyzed. The experiments show that the proposed methods are able to compete with commonly used methods, because they perform better in most cases.

This Master's Thesis deals with the controlling of robotic car by Raspberry Pi and the ca- mera. Theoretical part describes individual steps of image processing and probabilistic plan- ning for searching path in the work space. In particular, algorithm RRT (Rapidly-exploring Random Tree) is discussed and the balanced bidirectional RRT is further introduced and used for nonholonomic planning in configuration space. Next chapter speaks about propo- sed solution and there is the accurate description of connection Raspberry Pi to the robotic car. Rest of the work provides look at implemetation details and evaluation. In the end, conclusion was given and some improvements were suggested.

This work describes 3D reconstruction using stereo vision. It presents methods for automatic localization of corresponding points in both images and their reprojection into 3D space. Application created can be used for navigation of a robot and object avoidance. Second part of the document describes chosen components of the robot. Path finding algorithms are also discussed, particulary Voronoi's diagram.

This work deals with the path finding for nonholonomic robots using probabilistic algorithms. The theoretical part analyzes the general problem of finding routes. Subsequently, the work will focus on probabilistic algorithms. The practical part describes design of the applet and web sites that demonstrate probabilistic algorithms to user-specified objects.