Wavefront Coverage Path Planning (#351)

* First Commit of Wavefront Coverage Planner

* Update wavefront_coverage_path_planner.py

* fix CI / CodeFactor problem

* Fix mypy error

* update

* Fix PyCodeStyle

* Fix Code Scanning Warning and  code styling problem

* add simple unittest

* followed second code review suggestions

PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py

@@ -0,0 +1,219 @@
+"""
+Distance/Path Transform Wavefront Coverage Path Planner
+
+author: Todd Tang
+paper: Planning paths of complete coverage of an unstructured environment
+         by a mobile robot - Zelinsky et.al.
+link: http://pinkwink.kr/attachment/cfile3.uf@1354654A4E8945BD13FE77.pdf
+"""
+
+import os
+import sys
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+from scipy import ndimage
+
+
+do_animation = True
+
+
+def transform(
+    gridmap, src, distance_type='chessboard',
+    transform_type='path', alpha=0.01
+):
+    """transform
+
+    calculating transform of transform_type from src
+    in given distance_type
+
+    :param gridmap: 2d binary map
+    :param src: distance transform source
+    :param distance_type: type of distance used
+    :param transform_type: type of transform used
+    :param alpha: weight of Obstacle Transform usedwhen using path_transform
+    """
+
+    nrows, ncols = gridmap.shape
+
+    if nrows == 0 or ncols == 0:
+        sys.exit('Empty gridmap.')
+
+    inc_order = [[0, 1], [1, 1], [1, 0], [1, -1],
+                 [0, -1], [-1, -1], [-1, 0], [-1, 1]]
+    if distance_type == 'chessboard':
+        cost = [1, 1, 1, 1, 1, 1, 1, 1]
+    elif distance_type == 'eculidean':
+        cost = [1, np.sqrt(2), 1, np.sqrt(2), 1, np.sqrt(2), 1, np.sqrt(2)]
+    else:
+        sys.exit('Unsupported distance type.')
+
+    transform_matrix = float('inf') * np.ones_like(gridmap, dtype=np.float)
+    transform_matrix[src[0], src[1]] = 0
+    if transform_type == 'distance':
+        eT = np.zeros_like(gridmap)
+    elif transform_type == 'path':
+        eT = ndimage.distance_transform_cdt(1-gridmap, distance_type)
+    else:
+        sys.exit('Unsupported transform type.')
+
+    # set obstacle transform_matrix value to infinity
+    for i in range(nrows):
+        for j in range(ncols):
+            if gridmap[i][j] == 1.0:
+                transform_matrix[i][j] = float('inf')
+    is_visited = np.zeros_like(transform_matrix, dtype=bool)
+    is_visited[src[0], src[1]] = True
+    traversal_queue = [src]
+    calculated = [(src[0]-1)*ncols + src[1]]
+
+    def is_valid_neighbor(i, j):
+        return ni >= 0 and ni < nrows and nj >= 0 and nj < ncols \
+            and not gridmap[ni][nj]
+
+    while traversal_queue != []:
+        i, j = traversal_queue.pop(0)
+        for k, inc in enumerate(inc_order):
+            ni = i + inc[0]
+            nj = j + inc[1]
+            if is_valid_neighbor(ni, nj):
+                is_visited[i][j] = True
+
+                # update transform_matrix
+                transform_matrix[i][j] = min(
+                    transform_matrix[i][j],
+                    transform_matrix[ni][nj] + cost[k] + alpha*eT[ni][nj])
+
+                if not is_visited[ni][nj] \
+                        and ((ni-1)*ncols + nj) not in calculated:
+                    traversal_queue.append((ni, nj))
+                    calculated.append((ni-1)*ncols + nj)
+
+    return transform_matrix
+
+
+def wavefront(transform_matrix, start, goal):
+    """wavefront
+
+    performing wavefront coverage path planning
+
+    :param transform_matrix: the transform matrix
+    :param start: start point of planning
+    :param goal: goal point of planning
+    """
+
+    path = []
+    nrows, ncols = transform_matrix.shape
+
+    def get_search_order_increment(start, goal):
+        if start[0] >= goal[0] and start[1] >= goal[1]:
+            order = [[1, 0], [0, 1], [-1, 0], [0, -1],
+                     [1, 1], [1, -1], [-1, 1], [-1, -1]]
+        elif start[0] <= goal[0] and start[1] >= goal[1]:
+            order = [[-1, 0], [0, 1], [1, 0], [0, -1],
+                     [-1, 1], [-1, -1], [1, 1], [1, -1]]
+        elif start[0] >= goal[0] and start[1] <= goal[1]:
+            order = [[1, 0], [0, -1], [-1, 0], [0, 1],
+                     [1, -1], [-1, -1], [1, 1], [-1, 1]]
+        elif start[0] <= goal[0] and start[1] <= goal[1]:
+            order = [[-1, 0], [0, -1], [0, 1], [1, 0],
+                     [-1, -1], [-1, 1], [1, -1], [1, 1]]
+        else:
+            sys.exit('get_search_order_increment: cannot determine \
+                start=>goal increment order')
+        return order
+
+    def is_valid_neighbor(i, j):
+        is_i_valid_bounded = i >= 0 and i < nrows
+        is_j_valid_bounded = j >= 0 and j < ncols
+        if is_i_valid_bounded and is_j_valid_bounded:
+            return not is_visited[i][j] and \
+                transform_matrix[i][j] != float('inf')
+        return False
+
+    inc_order = get_search_order_increment(start, goal)
+
+    current_node = start
+    is_visited = np.zeros_like(transform_matrix, dtype=bool)
+
+    while current_node != goal:
+        i, j = current_node
+        path.append((i, j))
+        is_visited[i][j] = True
+
+        max_T = float('-inf')
+        i_max = (-1, -1)
+        i_last = 0
+        for i_last in range(len(path)):
+            current_node = path[-1 - i_last]  # get lastest node in path
+            for ci, cj in inc_order:
+                ni, nj = current_node[0] + ci, current_node[1] + cj
+                if is_valid_neighbor(ni, nj) and \
+                        transform_matrix[ni][nj] > max_T:
+                    i_max = (ni, nj)
+                    max_T = transform_matrix[ni][nj]
+
+            if i_max != (-1, -1):
+                break
+
+        if i_max == (-1, -1):
+            break
+        else:
+            current_node = i_max
+            if i_last != 0:
+                print('backtracing to', current_node)
+    path.append(goal)
+
+    return path
+
+
+def visualize_path(grid_map, start, goal, path, resolution):
+    oy, ox = start
+    gy, gx = goal
+    px, py = np.transpose(np.flipud(np.fliplr((path))))
+
+    if not do_animation:
+        plt.imshow(grid_map, cmap='Greys')
+        plt.plot(ox, oy, "-xy")
+        plt.plot(px, py, "-r")
+        plt.plot(gx, gy, "-pg")
+        plt.show()
+    else:
+        for ipx, ipy in zip(px, py):
+            plt.cla()
+            # for stopping simulation with the esc key.
+            plt.gcf().canvas.mpl_connect(
+                'key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
+            plt.imshow(grid_map, cmap='Greys')
+            plt.plot(ox, oy, "-xb")
+            plt.plot(px, py, "-r")
+            plt.plot(gx, gy, "-pg")
+            plt.plot(ipx, ipy, "or")
+            plt.axis("equal")
+            plt.grid(True)
+            plt.pause(0.1)
+
+
+def main():
+    dir_path = os.path.dirname(os.path.realpath(__file__))
+    img = plt.imread(os.path.join(dir_path, 'map', 'test.png'))
+    img = 1 - img  # revert pixel values
+
+    start = (43, 0)
+    goal = (0, 0)
+
+    # distance transform wavefront
+    DT = transform(img, goal, transform_type='distance')
+    DT_path = wavefront(DT, start, goal)
+    visualize_path(img, start, goal, DT_path, 1)
+
+    # path transform wavefront
+    PT = transform(img, goal, transform_type='path', alpha=0.01)
+    PT_path = wavefront(PT, start, goal)
+    visualize_path(img, start, goal, PT_path, 1)
+
+
+if __name__ == "__main__":
+    main()

tests/test_wavefront_coverage_path_planner.py

@@ -0,0 +1,64 @@
+import os
+import sys
+import matplotlib.pyplot as plt
+from unittest import TestCase
+
+sys.path.append(os.path.dirname(
+    os.path.abspath(__file__)) + "/../PathPlanning/WavefrontCPP")
+try:
+    import wavefront_coverage_path_planner
+except ImportError:
+    raise
+
+wavefront_coverage_path_planner.do_animation = False
+
+
+class TestPlanning(TestCase):
+    def wavefront_cpp(self, img, start, goal):
+        num_free = 0
+        for i in range(img.shape[0]):
+            for j in range(img.shape[1]):
+                num_free += 1 - img[i][j]
+
+        DT = wavefront_coverage_path_planner.transform(
+            img, goal, transform_type='distance')
+        DT_path = wavefront_coverage_path_planner.wavefront(DT, start, goal)
+        self.assertEqual(len(DT_path), num_free)  # assert complete coverage
+
+        PT = wavefront_coverage_path_planner.transform(
+            img, goal, transform_type='path', alpha=0.01)
+        PT_path = wavefront_coverage_path_planner.wavefront(PT, start, goal)
+        self.assertEqual(len(PT_path), num_free)  # assert complete coverage
+
+    def test_wavefront_CPP_1(self):
+        img_dir = os.path.dirname(
+            os.path.abspath(__file__)) + "/../PathPlanning/WavefrontCPP"
+        img = plt.imread(os.path.join(img_dir, 'map', 'test.png'))
+        img = 1 - img
+
+        start = (43, 0)
+        goal = (0, 0)
+
+        self.wavefront_cpp(img, start, goal)
+
+    def test_wavefront_CPP_2(self):
+        img_dir = os.path.dirname(
+            os.path.abspath(__file__)) + "/../PathPlanning/WavefrontCPP"
+        img = plt.imread(os.path.join(img_dir, 'map', 'test_2.png'))
+        img = 1 - img
+
+        start = (10, 0)
+        goal = (10, 40)
+
+        self.wavefront_cpp(img, start, goal)
+
+    def test_wavefront_CPP_3(self):
+        img_dir = os.path.dirname(
+            os.path.abspath(__file__)) + "/../PathPlanning/WavefrontCPP"
+        img = plt.imread(os.path.join(img_dir, 'map', 'test_3.png'))
+        img = 1 - img
+
+        start = (0, 0)
+        goal = (30, 30)
+
+        self.wavefront_cpp(img, start, goal)