Discrete Map Generation using Fixed Cell Decomposition
This MATLAB script generates a 100x100 occupancy grid map of a static environment with obstacles from the HW3data.mat file.
Input:
HW3data.mat: A MATLAB file containing obstacle data (x-coordinates in map_x, y-coordinates in map_y, and obstacle IDs in the index).
Output:
occupancy_grid: A 100x100 binary image representing the map, where 0 indicates free space and 1 indicates occupied space.
occupancy_matrix: A 100x100 binary matrix corresponding to the occupancy_grid image.
Functionality:
Load Obstacle Data: Loads the map_x, map_y, and obstacle IDs from HW3data.mat.
Cell Decomposition: Creates a 100x100 grid representing the environment.
Occupancy Determination: Iterates through each cell:
If any obstacle point falls within a cell, the cell is marked as occupied (1) in both occupancy_grid and occupancy_matrix.
Otherwise, the cell is marked as free (0).
Simulating Lidar Data in Discrete Maps
This script simulates lidar data for a robot positioned within the generated map (from Question 2).
Input:
robot_x: The robot's x-coordinate.
robot_y: The robot's y-coordinate.
occupancy_grid: The 100x100 occupancy grid map.
max_range: The maximum range (in cells) that the lidar can detect.
Output:
lidar_data: A list of distances to obstacles encountered by the lidar along a 360-degree sweep.
Visualization (optional): A plot of the lidar data overlaid on the map.
Functionality:
Iterate Over Scan Angles: Steps through a range of angles representing the lidar sweep (e.g., 0 to 359 degrees).
Raycasting: For each angle:
Calculates the ray direction based on the angle and robot's orientation.
Casts a ray from the robot's position until it hits an obstacle or reaches the maximum range.
Records the distance to the obstacle in the lidar_data list.
Considers obstacles between the robot and the detected obstacle for occlusion (optional).
