quail/pathPlanner/theta_star.py at 559ac27a266a4612af787a21721f49bda538c2bf · Mineinjava/quail · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
import time

import point
from node import node
import numpy as np
from numba import njit
import bresenhams
from time import perf_counter
import traceback
from queue import PriorityQueue
from perlin_noise import PerlinNoise
HERUISTIC_WEIGHT = 1


def reconstruct_path(goal:node):
    path = []
    current = goal
    print("goal", goal)
    print("goal parent", goal.parent)
    print("goal parent parent", goal.parent.parent)
    while current.parent != current:
        path.append(current)
        current = current.parent
    path.append(current)
    path.reverse()
    return path

def update_vertex(s:node, neighbor:node, goal:node, grid):
    if neighbor.line_of_sight(s.parent, grid):
        if s.parent.shortestDist + s.parent.distance(neighbor) < neighbor.shortestDist:
            neighbor.parent = s.parent
            neighbor.set_shortestDist(s.parent.shortestDist + s.parent.distance(neighbor))
            neighbor.set_heuristic_distance(goal)
    else:
        if neighbor.line_of_sight(s, grid):
            if s.shortestDist + s.distance(neighbor) < neighbor.shortestDist:
                neighbor.parent = s
                neighbor.set_shortestDist(s.shortestDist + s.distance(neighbor))
                neighbor.set_heuristic_distance(goal)


def theta_star(start:node, goal:point, grid):
    start.shortestDist = 0
    start.parent = start
    start.set_heuristic_distance(goal)
    openSet = [start]
    closedSet = []
    while len(openSet) > 0:
        openSet.sort(key=lambda x: x.shortestDist + (HERUISTIC_WEIGHT * x.set_heuristic_distance(goal)))
        s = openSet.pop(0)
        if s == goal:
            return reconstruct_path(s)
        closedSet.append(s)
        for neighbor in s.get_neighbors(diagonal=True, grid=grid):
            if neighbor in closedSet:
                continue
            update_vertex(s, neighbor, goal, grid)
            if neighbor not in openSet and neighbor.parent != None:
                openSet.append(neighbor)
    return None


if __name__ == '__main__':
    """test case"""
    import matplotlib.pyplot as plt
    from cubicSpline import interpolate_xy
    WIDTH = 50
    HEIGHT = 50
    FILL_PCT = 0.35
    start = node(0, 0)
    goal = node(WIDTH-1, HEIGHT-1)
    grid = []
    SWAP_XY = True
    '''
    for x in range(WIDTH+1):
        arr = ([1] * round(HEIGHT*FILL_PCT) + ([0] * round(1+ HEIGHT - (HEIGHT*FILL_PCT))))
        np.random.shuffle(arr)
        grid.append(arr)

    '''
    noise = PerlinNoise(octaves=5)
    xpix, ypix = WIDTH, HEIGHT
    pic = [[noise([i/xpix, j/ypix]) for j in range(xpix)] for i in range(ypix)]

    grid = np.array(pic)
    grid = np.where(grid < FILL_PCT, 0, 1)


    grid = np.array(grid)
    plt.imshow(grid, cmap='gray', interpolation='none', origin='lower')
    plt.show()
    grid[0][0] = 0
    grid[HEIGHT-1][WIDTH-1] = 0
    theta_star(start, goal, grid)

    start_time = time.perf_counter()
    path = theta_star(start, goal, grid)
    end_time = time.perf_counter()
    print("Time taken: ", (end_time - start_time) * 1000)
    print(path)
    splinex = []
    spliney = []
    for i in range(len(path)):
        splinex.append(path[i].x)
        spliney.append(path[i].y)

    splinedx, splinedy = interpolate_xy(splinex, spliney, len(splinex) * 30)


    if SWAP_XY:
        splinedx, splinedy = splinedy, splinedx
        splinex, spliney = spliney, splinex

    plt.imshow(grid, cmap='gray', interpolation='none', origin='lower')
    plt.plot(splinex, spliney, marker='o')
    plt.plot(splinedx, splinedy, marker='')
    plt.show()