can generate shorest path

PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py

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+"""
+
+Probablistic Road Map (PRM) Planner
+
+author: Atsushi Sakai (@Atsushi_twi)
+
+"""
+
+import random
+import math
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotrecorder import matplotrecorder
+from pyfastnns import pyfastnns
+matplotrecorder.donothing = True
+
+# parameter
+N_SAMPLE = 500
+N_KNN = 10
+MAX_EDGE_LEN = 30.0  # [m] Maximum edge length
+
+
+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 PRM_planning(sx, sy, gx, gy, ox, oy, rr):
+
+    sample_x, sample_y = sample_points(sx, sy, gx, gy, rr, ox, oy)
+    plt.plot(sample_x, sample_y, ".r")
+
+    road_map = generate_roadmap(sample_x, sample_y, rr)
+
+    rx, ry = dijkstra_planning(
+        sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y)
+
+    return rx, ry
+
+
+def generate_roadmap(sample_x, sample_y, rr):
+
+    road_map = []
+    nsample = len(sample_x)
+    skdtree = pyfastnns.NNS(np.vstack((sample_x, sample_y)).T)
+
+    for (i, ix, iy) in zip(range(nsample), sample_x, sample_y):
+
+        index = skdtree.search(
+            np.matrix([ix, iy]).T, k=nsample)
+        edge_id = []
+
+        for ii in range(1, len(index[0][0][0])):
+            #  nx = sample_x[index[i]]
+            #  ny = sample_y[index[i]]
+
+            #  if !is_collision(ix, iy, nx, ny, rr, okdtree)
+            edge_id.append(index[0][0][0][ii])
+            if len(edge_id) >= N_KNN:
+                break
+
+        road_map.append(edge_id)
+
+    #  plot_road_map(road_map, sample_x, sample_y)
+
+    return road_map
+
+
+def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y):
+    """
+    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(sx, sy, 0.0, -1)
+    ngoal = Node(gx, gy, 0.0, -1)
+
+    openset, closedset = dict(), dict()
+    openset[len(road_map) - 2] = nstart
+
+    while True:
+        if len(openset) == 0:
+            print("Cannot find path")
+            break
+
+        print(len(openset), len(closedset))
+
+        c_id = min(openset, key=lambda o: openset[o].cost)
+        current = openset[c_id]
+        print("current", current, c_id)
+        #  input()
+
+        # show graph
+        plt.plot(current.x, current.y, "xc")
+        if len(closedset.keys()) % 10 == 0:
+            plt.pause(0.001)
+            matplotrecorder.save_frame()
+
+        if c_id == (len(road_map) - 1):
+            print("Find goal")
+            ngoal.pind = current.pind
+            ngoal.cost = current.cost
+            break
+
+        # Remove the item from the open set
+        del openset[c_id]
+        # Add it to the closed set
+        closedset[c_id] = current
+
+        # expand search grid based on motion model
+        for i in range(len(road_map[c_id])):
+            n_id = road_map[c_id][i]
+            print(i, n_id)
+            dx = sample_x[n_id] - current.x
+            dy = sample_y[n_id] - current.y
+            d = math.sqrt(dx**2 + dy**2)
+            node = Node(sample_x[n_id], sample_y[n_id],
+                        current.cost + d, c_id)
+
+            #  if not verify_node(node, obmap, minx, miny, maxx, maxy):
+            #  continue
+
+            if n_id in closedset:
+                continue
+            # Otherwise if it is already in the open set
+            if n_id in openset:
+                if openset[n_id].cost > node.cost:
+                    openset[n_id].cost = node.cost
+                    openset[n_id].pind = c_id
+            else:
+                openset[n_id] = node
+
+    # generate final course
+    rx, ry = [ngoal.x], [ngoal.y]
+    pind = ngoal.pind
+    while pind != -1:
+        n = closedset[pind]
+        rx.append(n.x)
+        ry.append(n.y)
+        pind = n.pind
+
+    return rx, ry
+
+
+def plot_road_map(road_map, sample_x, sample_y):
+
+    for i in range(len(road_map)):
+        for ii in range(len(road_map[i])):
+            ind = road_map[i][ii]
+
+            plt.plot([sample_x[i], sample_x[ind]],
+                     [sample_y[i], sample_y[ind]], "-k")
+
+
+def sample_points(sx, sy, gx, gy, rr, ox, oy):
+    maxx = max(ox)
+    maxy = max(oy)
+    minx = min(ox)
+    miny = min(oy)
+
+    sample_x, sample_y = [], []
+
+    nns = pyfastnns.NNS(np.vstack((ox, oy)).T)
+
+    while len(sample_x) <= N_SAMPLE:
+        tx = (random.random() - minx) * (maxx - minx)
+        ty = (random.random() - miny) * (maxy - miny)
+
+        index, dist = nns.search(np.matrix([tx, ty]).T)
+
+        if dist[0] >= rr:
+            sample_x.append(tx)
+            sample_y.append(ty)
+
+    sample_x.append(sx)
+    sample_y.append(sy)
+    sample_x.append(gx)
+    sample_y.append(gy)
+
+    return sample_x, sample_y
+
+
+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]
+    robot_size = 5.0  # [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)
+
+    plt.plot(ox, oy, ".k")
+    plt.plot(sx, sy, "xr")
+    plt.plot(gx, gy, "xb")
+    plt.grid(True)
+    plt.axis("equal")
+
+    rx, ry = PRM_planning(sx, sy, gx, gy, ox, oy, robot_size)
+
+    plt.plot(rx, ry, "-r")
+
+    for i in range(20):
+        matplotrecorder.save_frame()
+    plt.show()
+
+    matplotrecorder.save_movie("animation.gif", 0.1)
+
+
+if __name__ == '__main__':
+    main()