Path_Planning_Algo_with_Animations/Sampling-based Planning/env3D.py

# this is the three dimensional configuration space for rrt
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: yue qi
"""
import numpy as np

def getblocks(resolution):
    # AABBs 
    block = [[3.10e+00,0.00e+00,2.10e+00,3.90e+00,5.00e+00,6.00e+00],
            [9.10e+00,0.00e+00,2.10e+00,9.90e+00,5.00e+00,6.00e+00],
            [1.51e+01,0.00e+00,2.10e+00,1.59e+01,5.00e+00,6.00e+00],
            [1.00e-01,0.00e+00,0.00e+00,9.00e-01,5.00e+00,3.90e+00],
            [6.10e+00,0.00e+00,0.00e+00,6.90e+00,5.00e+00,3.90e+00],
            [1.21e+01,0.00e+00,0.00e+00,1.29e+01,5.00e+00,3.90e+00],
            [1.81e+01,0.00e+00,0.00e+00,1.89e+01,5.00e+00,3.90e+00]]
    Obstacles = []
    for i in block:
        i = np.array(i)
        Obstacles.append((i[0]/resolution,i[1]/resolution,i[2]/resolution,i[3]/resolution,i[4]/resolution,i[5]/resolution))
    return np.array(Obstacles)

class env():
    def __init__(self,xmin=0,ymin=0,zmin=0,xmax=20,ymax=5,zmax=6,resolution=1):
        self.resolution = resolution
        self.boundary = np.array([xmin,ymin,zmin,xmax,ymax,zmax])/resolution
        self.blocks = getblocks(resolution)
        self.start = np.array([0.5, 2.5, 5.5])
        self.goal = np.array([19.0, 2.5, 5.5])

    def visualize(self):
        # fig = plt.figure()
        # TODO: do visualizations
        return

if __name__ == '__main__': 
    newenv = env()
    X = StateSpace(newenv.boundary,newenv.resolution)
    print(X)