Capstone Project 135 JetBot

control_pkg

MPC Control algorithm
Takes state of the JetBot and the path
Outputs angular velocities on the two wheels

path_pkg

Generate desired path for the JetBot
Takes obstacle map, start position, end position
Outputs sequence of coordinates of the optimal path

path finder

Correspond to the pathfinding algorithm section in the report, include test code for different algorithms

vision_pkg

state (sub pkg)

    Video frame calibration, get JetBot state, get desired position
    Takes detected ArUco tag information and board_size
    
    state_estimation.py:
        get_state(calibrated corners and ids, board_size) (returns state of JetBot as np array [x, y, theta])
        get_desired_pos(calibrated corners and ids, board_size) (returns target position as np array [x, y])
    initialize.py:
        calib_frame(original frame, normal corners and ids, board_size) (returns calibrated img and homograph matrix)

detect_object (sub pkg)

    Generate obstacle map for A*
    Takes calibrated frame
    Outputs 2D np array of 0s and 1s, 0 being occupied space, 1 bing free space
    
    Camera.py:
        generate_map(calibrated frame) (returns the obstacle map)