The prerequisite for the effective usage of the autonomous vehicles in the future is the ability to navigate independently from any human control in a structured environment. For an autonomous vehicle is a key issue to find an optimal path from the start tot he goal, which can be travelled as fast as possible.
During my work, I learned the basic concepts of robot control, which are essential for understanding motion planning methods. After introducing the principles, I describe some path planning algorithms, which are widely used in the industry nowadays. These methods are the Reed-Shepp, Hybrid-State A*m RTR, RRT and C*CS algorithms, that are described in the first part of my dissertation. After having a comprehensive picture of the state-of-the-art path planning algorithms, such a method is presented in more depth
The OSEHS (Orientation-Aware Space Exploration Guided Heuristic Search) planner can be used with high performance for planning tasks in narrow spaces, such as a parking scenario. I studied the operation of the algorithm to gain a deeper understanding of the advantages and disadvantages of this method. The essence of this method is to gain space and orientation information from free space before starting the depth-first search in the workspace after the possible states.
My most important task was to implement the planner to a C++ software library, which was developed in the university. To be convinced of the correct functioning and effectiveness of the program, as a final step, I tested my code in different environments. The test results can be found at the end of my thesis.