Important Note: In this project, the use of any simulation software within the VMWare virtual machine is not desired. It is specifically prepared for simple matrix operations in the MATLAB environment. The acquisition and processing of data from the simulator will be requested later. All of the following requirements should be implemented in the MATLAB environment. Hints about commands that can be used are provided, but other MATLAB commands can also be used.

Project Description:

In this project, students are expected to create a maze in the MATLAB environment, then use randomly generated LIDAR sensor data to detect the position and orientation of a robot inside the maze. Different algorithms can be used to create the maze in the project, and original approaches are also aimed to be developed in the analysis of LIDAR sensor data and location determination.

Maze Generation Algorithms:

• Recursive Backtracking
• Prim's Algorithm
• Kruskal's Algorithm
• Hunt-and-Kill Algorithm
• Wilson's Algorithm
• Depth-First Search (DFS)
• Binary Tree Algorithm
• Sidewinder Algorithm
• Eller's Algorithm
• Growing Tree Algorithm

Tasks:

Maze Creation:

Choose one of the above algorithms and create a maze of size 50x50 or 100x100.

Visualize the maze and save it in .png format.

Possible map image example: [Include an example image if available]

2. Simulation of LIDAR Sensor Data:

- Generate random LIDAR sensor data of size 2x2. These data should represent a specific area inside the maze.

3. Robot Position and Orientation Detection:

- Use LIDAR data to determine possible positions and orientations of the robot inside the maze.

- There may be multiple matches; list exact matches and possible matches based on a 90% threshold separately.

- Visualize each match and save them in `.png` format.

Recommended MATLAB Functions and Tips:

- Maze Creation: `zeros`, `randi`, `meshgrid`, `sub2ind`, etc.

- LIDAR Data Simulation: `rand`, `randn`, etc.

- Visualization: `imagesc`, `plot`, `scatter`, `colormap`, `axis`, `saveas`.

- Matching and Position Detection: Logical operations, indexing, `ismember`, `find`, etc.

- For thresholding, you can use the `abs` or `isequal` functions and logical operators.

Project Output:

- Visualized maze (.png)

- Visualization of LIDAR data on the maze (.png)

- Visualization of all possible positions of the robot inside the maze (.png)

- List of all positions in the case of an exact match

- List of all possible matches based on a 90% threshold

Project Evaluation:

This project aims to enhance students' algorithmic thinking, data analysis, and visualization skills. It also tests decision-making and problem-solving skills in different scenarios.