mypy fix test

PathPlanning/HybridAStar/a_star_heuristic.py

@@ -0,0 +1,234 @@
+"""
+
+A* grid based planning
+
+author: Nikos Kanargias (nkana@tee.gr)
+
+See Wikipedia article (https://en.wikipedia.org/wiki/A*_search_algorithm)
+
+"""
+
+import math
+import heapq
+import matplotlib.pyplot as plt
+
+show_animation = False
+
+
+class Node:
+
+    def __init__(self, x, y, cost, pind):
+        self.x = x
+        self.y = y
+        self.cost = cost
+        self.pind = pind
+
+    def __str__(self):
+        return str(self.x) + "," + str(self.y) + "," + str(self.cost) + "," + str(self.pind)
+
+
+def calc_final_path(ngoal, closedset, reso):
+    # generate final course
+    rx, ry = [ngoal.x * reso], [ngoal.y * reso]
+    pind = ngoal.pind
+    while pind != -1:
+        n = closedset[pind]
+        rx.append(n.x * reso)
+        ry.append(n.y * reso)
+        pind = n.pind
+
+    return rx, ry
+
+
+def dp_planning(sx, sy, gx, gy, ox, oy, reso, rr):
+    """
+    gx: goal x position [m]
+    gx: goal x position [m]
+    ox: x position list of Obstacles [m]
+    oy: y position list of Obstacles [m]
+    reso: grid resolution [m]
+    rr: robot radius[m]
+    """
+
+    nstart = Node(round(sx / reso), round(sy / reso), 0.0, -1)
+    ngoal = Node(round(gx / reso), round(gy / reso), 0.0, -1)
+    ox = [iox / reso for iox in ox]
+    oy = [ioy / reso for ioy in oy]
+
+    obmap, minx, miny, maxx, maxy, xw, yw = calc_obstacle_map(ox, oy, reso, rr)
+
+    motion = get_motion_model()
+
+    openset, closedset = dict(), dict()
+    openset[calc_index(ngoal, xw, minx, miny)] = ngoal
+    pq = []
+    pq.append((0, calc_index(ngoal, xw, minx, miny)))
+
+    while 1:
+        if not pq:
+            break
+        cost, c_id = heapq.heappop(pq)
+        if c_id in openset:
+            current = openset[c_id]
+            closedset[c_id] = current
+            openset.pop(c_id)
+        else:
+            continue
+
+        # show graph
+        if show_animation:  # pragma: no cover
+            plt.plot(current.x * reso, current.y * reso, "xc")
+            # 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])
+            if len(closedset.keys()) % 10 == 0:
+                plt.pause(0.001)
+
+        # Remove the item from the open set
+
+        # expand search grid based on motion model
+        for i, _ in enumerate(motion):
+            node = Node(current.x + motion[i][0],
+                        current.y + motion[i][1],
+                        current.cost + motion[i][2], c_id)
+            n_id = calc_index(node, xw, minx, miny)
+
+            if n_id in closedset:
+                continue
+
+            if not verify_node(node, obmap, minx, miny, maxx, maxy):
+                continue
+
+            if n_id not in openset:
+                openset[n_id] = node  # Discover a new node
+                heapq.heappush(
+                    pq, (node.cost, calc_index(node, xw, minx, miny)))
+            else:
+                if openset[n_id].cost >= node.cost:
+                    # This path is the best until now. record it!
+                    openset[n_id] = node
+                    heapq.heappush(
+                        pq, (node.cost, calc_index(node, xw, minx, miny)))
+
+    rx, ry = calc_final_path(closedset[calc_index(
+        nstart, xw, minx, miny)], closedset, reso)
+
+    return rx, ry, closedset
+
+
+def calc_heuristic(n1, n2):
+    w = 1.0  # weight of heuristic
+    d = w * math.sqrt((n1.x - n2.x)**2 + (n1.y - n2.y)**2)
+    return d
+
+
+def verify_node(node, obmap, minx, miny, maxx, maxy):
+
+    if node.x < minx:
+        return False
+    elif node.y < miny:
+        return False
+    elif node.x >= maxx:
+        return False
+    elif node.y >= maxy:
+        return False
+
+    if obmap[node.x][node.y]:
+        return False
+
+    return True
+
+
+def calc_obstacle_map(ox, oy, reso, vr):
+
+    minx = round(min(ox))
+    miny = round(min(oy))
+    maxx = round(max(ox))
+    maxy = round(max(oy))
+
+    xwidth = round(maxx - minx)
+    ywidth = round(maxy - miny)
+
+    # obstacle map generation
+    obmap = [[False for i in range(ywidth)] for i in range(xwidth)]
+    for ix in range(xwidth):
+        x = ix + minx
+        for iy in range(ywidth):
+            y = iy + miny
+            #  print(x, y)
+            for iox, ioy in zip(ox, oy):
+                d = math.sqrt((iox - x)**2 + (ioy - y)**2)
+                if d <= vr / reso:
+                    obmap[ix][iy] = True
+                    break
+
+    return obmap, minx, miny, maxx, maxy, xwidth, ywidth
+
+
+def calc_index(node, xwidth, xmin, ymin):
+    return (node.y - ymin) * xwidth + (node.x - xmin)
+
+
+def get_motion_model():
+    # dx, dy, cost
+    motion = [[1, 0, 1],
+              [0, 1, 1],
+              [-1, 0, 1],
+              [0, -1, 1],
+              [-1, -1, math.sqrt(2)],
+              [-1, 1, math.sqrt(2)],
+              [1, -1, math.sqrt(2)],
+              [1, 1, math.sqrt(2)]]
+
+    return motion
+
+
+def main():
+    print(__file__ + " start!!")
+
+    # start and goal position
+    sx = 10.0  # [m]
+    sy = 10.0  # [m]
+    gx = 50.0  # [m]
+    gy = 50.0  # [m]
+    grid_size = 2.0  # [m]
+    robot_size = 1.0  # [m]
+
+    ox, oy = [], []
+
+    for i in range(60):
+        ox.append(i)
+        oy.append(0.0)
+    for i in range(60):
+        ox.append(60.0)
+        oy.append(i)
+    for i in range(61):
+        ox.append(i)
+        oy.append(60.0)
+    for i in range(61):
+        ox.append(0.0)
+        oy.append(i)
+    for i in range(40):
+        ox.append(20.0)
+        oy.append(i)
+    for i in range(40):
+        ox.append(40.0)
+        oy.append(60.0 - i)
+
+    if show_animation:  # pragma: no cover
+        plt.plot(ox, oy, ".k")
+        plt.plot(sx, sy, "xr")
+        plt.plot(gx, gy, "xb")
+        plt.grid(True)
+        plt.axis("equal")
+
+    rx, ry, _ = dp_planning(sx, sy, gx, gy, ox, oy, grid_size, robot_size)
+
+    if show_animation:  # pragma: no cover
+        plt.plot(rx, ry, "-r")
+        plt.show()
+
+
+if __name__ == '__main__':
+    show_animation = True
+    main()