Boustrophedon Cellular Decomposition的Python实现

算法简介

BCD(Boustrophedon Cellular Decomposition)是一种栅格地图的分解方法。完成该分解后，每个cell都可以通过一个牛耕式路径遍历。不同cell之间通过旅行商问题获得最优路径即可做到全地图的覆盖式清扫。详情可以参考论文

Choset, H. (2000). Coverage of Known Spaces: The Boustrophedon Cellular Decomposition. Autonomous Robots (Vol. 9).

算法原理非常简单。地图中的每一列称为一个slice。遍历地图中的slice，并计算区块的连通性。每当连通性增加，则触发一个IN事件，封闭当前cell，并开启两个新的cell。若遇到连通性降低，则触发一个OUT事件，当前的两个cell封闭，并开启一个新的cell。

Python代码

# This is the code of Boustrophedon Cellular Decomposition algorithm.
# I write it in python3.6.
# For more details, please read the paper:
# Choset, H. (2000). Coverage of Known Spaces: The Boustrophedon Cellular Decomposition. Autonomous Robots (Vol. 9).
#
#
#                                               - Dechao Meng
import numpy as np
import cv2
from PIL import Image
from matplotlib import pyplot as plt
import matplotlib
from typing import Tuple, List
import random

# matplotlib.rcParams['figure.figsize'] = [30, 20]

Slice = List[Tuple[int, int]]


def calc_connectivity(slice: np.ndarray) -> Tuple[int, Slice]:
    """
    计算一个slice的连通性，并且返回该slice的连通区域。

    Args:
        slice: rows. A slice of map.

    Returns:
        The connectivity number and connectivity parts.

    Examples:
        >>> data = np.array([0,0,0,0,1,1,1,0,1,0,0,0,1,1,0,1,1,0])
        >>> print(calc_connectivity(data))
        (4, [(4, 7), (8, 9), (12, 14), (15, 17)])
    """
    connectivity = 0
    last_data = 0
    open_part = False
    connective_parts = []
    for i, data in enumerate(slice):
        if last_data == 0 and data == 1:
            open_part = True
            start_point = i
        elif last_data == 1 and data == 0 and open_part:
            open_part = False
            connectivity += 1
            end_point = i
            connective_parts.append((start_point