## 2 Hardware and Software Solutions
### Path Planning  
Path planning is a term known in robotics as part of the navigation of an autonomous robot. The result of planning is the process by which we go from the initial position to the target position. The main condition is that the road does not cross with objects in the area. There are two approaches to path planning, local or global planning. Global trip planning is based on a familiar environment and is suitable for finding the optimal route. In local planning, the robot responds appropriately to unknown or dynamic obstacles. In this case, the robot makes decisions while moving. By combining both methods, better results can be achieved. The planning can be divided into two parts, while each part can be solved separately. The first deals with the pre-processing of the workspace, where this space is discretized and then converted into a configuration space. The configuration space consists of all robot configurations in the workspace where it performs the given tasks. The robot configuration is determined by a vector that contains the position and orientation of the robot. After discretizing the workspace, a configuration space is created, represented by a graph, grid, or function. The second part deals with the use of a specific algorithm, to find the path between the start and end position of the robot, already in the created configuration space. If we wanted to categorize the individual approaches, we would again come across different points of view of the authors in their division. However, the opinions do not differ so radically. In the article [17], the author divided the issue into three basic approaches, namely roadmaps, cell decomposition, and potential fields. He considered A*, probabilistic road maps (from the Probabilistic Roadmap), and genetic algorithms (from the Genetic algorithm) to be the most frequently used algorithms. In [18], the author added a group of approaches called heuristic approaches compared to the previous author. He included artificial intelligence methods in this subset, such as neural network planning, fuzzy logic, genetic algorithms, and particle swarm optimization. As a result, he evaluated that heuristic methods are more suitable for dynamic environments. The author dealt with this area in a more complex way in [19], where his view of this area is captured in Figure 2. Here, the author mainly focused on global travel planning methods. His approach divided the environment modeling, optimization criteria, and path search method into three steps. The step, which he called the optimization criteria, can be considered as inserting inputs into the third step for optimal path search. He used methods such as the framework space approach, the free space approach, cellular decomposition, topological methods, and  
Figure 1: A robotic system with omnidirectional chassis [16].  
probabilistic road maps. He divided the search for a path into two areas, the heuristic and the field of artificial intelligence.  
As we have noticed, in [18,19], there was a conflict in the division of methods under the designation heuristic. If we looked further in the area of categorization of this issue, we would find further discrepancies. Based on these discrepancies, the author in [20] chose a different approach to solve this issue. He divided all the used methods into four areas, namely Reactive computing, Soft Computing, C-Space Search, and Optimal Control. This distribution can be seen in Figure 3. In addition, each subcategory may have something in common with another subcategory.  
We decide to use the A* algorithm to plan the route. Algorithm A* is used to search a discrete state space. The output should be the optimal trajectory from the initial state to the target state.  
Figure 2: Methods of path planning.