Greedy Best-First Search (#315)

PathPlanning/AStar/a_star.py

@@ -237,7 +237,7 @@ def main():
     grid_size = 2.0  # [m]
     robot_radius = 1.0  # [m]
 
-    # set obstable positions
+    # set obstacle positions
     ox, oy = [], []
     for i in range(-10, 60):
         ox.append(i)

PathPlanning/BidirectionalAStar/bidirectional_a_star.py

@@ -100,8 +100,9 @@ class BidirectionalAStarPlanner:
                          self.calc_grid_position(current_B.y, self.miny), "xc")
                 # for stopping simulation with the esc key.
                 plt.gcf().canvas.mpl_connect('key_release_event',
-                                             lambda event: [exit(
+                                             lambda event:
-                                                 0) if event.key == 'escape' else None])
+                                             [exit(0) if event.key == 'escape'
+                                              else None])
                 if len(closed_set_A.keys()) % 10 == 0:
                     plt.pause(0.001)
 
@@ -121,61 +122,50 @@ class BidirectionalAStarPlanner:
 
             # expand_grid search grid based on motion model
             for i, _ in enumerate(self.motion):
-                continue_A = False
-                continue_B = False
 
-                child_node_A = self.Node(current_A.x + self.motion[i][0],
+                c_nodes = [self.Node(current_A.x + self.motion[i][0],
-                                         current_A.y + self.motion[i][1],
+                                     current_A.y + self.motion[i][1],
-                                         current_A.cost + self.motion[i][2], 
+                                     current_A.cost + self.motion[i][2],
-                                         c_id_A)
+                                     c_id_A),
+                           self.Node(current_B.x + self.motion[i][0],
+                                     current_B.y + self.motion[i][1],
+                                     current_B.cost + self.motion[i][2],
+                                     c_id_B)]
 
-                child_node_B = self.Node(current_B.x + self.motion[i][0],
+                n_ids = [self.calc_grid_index(c_nodes[0]),
-                                         current_B.y + self.motion[i][1],
+                         self.calc_grid_index(c_nodes[1])]
-                                         current_B.cost + self.motion[i][2], 
-                                         c_id_B)
-
-                n_id_A = self.calc_grid_index(child_node_A)
-                n_id_B = self.calc_grid_index(child_node_B)
 
                 # If the node is not safe, do nothing
-                if not self.verify_node(child_node_A):
+                continue_ = self.check_nodes_and_sets(c_nodes, closed_set_A,
-                    continue_A = True
+                                                      closed_set_B, n_ids)
 
-                if not self.verify_node(child_node_B):
+                if not continue_[0]:
-                    continue_B = True
+                    if n_ids[0] not in open_set_A:
-
-                if n_id_A in closed_set_A:
-                    continue_A = True
-
-                if n_id_B in closed_set_B:
-                    continue_B = True
-
-                if not continue_A:
-                    if n_id_A not in open_set_A:
                         # discovered a new node
-                        open_set_A[n_id_A] = child_node_A
+                        open_set_A[n_ids[0]] = c_nodes[0]
                     else:
-                        if open_set_A[n_id_A].cost > child_node_A.cost:
+                        if open_set_A[n_ids[0]].cost > c_nodes[0].cost:
                             # This path is the best until now. record it
-                            open_set_A[n_id_A] = child_node_A
+                            open_set_A[n_ids[0]] = c_nodes[0]
 
-                if not continue_B:
+                if not continue_[1]:
-                    if n_id_B not in open_set_B:
+                    if n_ids[1] not in open_set_B:
                         # discovered a new node
-                        open_set_B[n_id_B] = child_node_B
+                        open_set_B[n_ids[1]] = c_nodes[1]
                     else:
-                        if open_set_B[n_id_B].cost > child_node_B.cost:
+                        if open_set_B[n_ids[1]].cost > c_nodes[1].cost:
                             # This path is the best until now. record it
-                            open_set_B[n_id_B] = child_node_B
+                            open_set_B[n_ids[1]] = c_nodes[1]
 
         rx, ry = self.calc_final_bidirectional_path(
             meetpointA, meetpointB, closed_set_A, closed_set_B)
 
         return rx, ry
 
-    def calc_final_bidirectional_path(self, meetnode_A, meetnode_B, closed_set_A, closed_set_B):
+    # takes two sets and two meeting nodes and return the optimal path
-        rx_A, ry_A = self.calc_final_path(meetnode_A, closed_set_A)
+    def calc_final_bidirectional_path(self, n1, n2, setA, setB):
-        rx_B, ry_B = self.calc_final_path(meetnode_B, closed_set_B)
+        rx_A, ry_A = self.calc_final_path(n1, setA)
+        rx_B, ry_B = self.calc_final_path(n2, setB)
 
         rx_A.reverse()
         ry_A.reverse()
@@ -198,6 +188,16 @@ class BidirectionalAStarPlanner:
 
         return rx, ry
 
+    def check_nodes_and_sets(self, c_nodes, closedSet_A, closedSet_B, n_ids):
+        continue_ = [False, False]
+        if not self.verify_node(c_nodes[0]) or n_ids[0] in closedSet_A:
+            continue_[0] = True
+
+        if not self.verify_node(c_nodes[1]) or n_ids[1] in closedSet_B:
+            continue_[1] = True
+
+        return continue_
+
     @staticmethod
     def calc_heuristic(n1, n2):
         w = 1.0  # weight of heuristic

PathPlanning/BreadthFirstSearch/breadth_first_search.py

@@ -84,8 +84,9 @@ class BreadthFirstSearchPlanner:
                          self.calc_grid_position(current.y, self.miny), "xc")
                 # for stopping simulation with the esc key.
                 plt.gcf().canvas.mpl_connect('key_release_event',
-                                             lambda event: [exit(
+                                             lambda event:
-                                                 0) if event.key == 'escape' else None])
+                                             [exit(0) if event.key == 'escape'
+                                              else None])
                 if len(closed_set.keys()) % 10 == 0:
                     plt.pause(0.001)
 
@@ -216,7 +217,7 @@ def main():
     grid_size = 2.0  # [m]
     robot_radius = 1.0  # [m]
 
-    # set obstable positions
+    # set obstacle positions
     ox, oy = [], []
     for i in range(-10, 60):
         ox.append(i)

PathPlanning/DepthFirstSearch/depth_first_search.py

@@ -81,8 +81,9 @@ class DepthFirstSearchPlanner:
                          self.calc_grid_position(current.y, self.miny), "xc")
                 # for stopping simulation with the esc key.
                 plt.gcf().canvas.mpl_connect('key_release_event',
-                                             lambda event: [exit(
+                                             lambda event:
-                                                 0) if event.key == 'escape' else None])
+                                             [exit(0) if event.key == 'escape'
+                                              else None])
                 plt.pause(0.01)
 
             if current.x == ngoal.x and current.y == ngoal.y:
@@ -213,7 +214,7 @@ def main():
     grid_size = 2.0  # [m]
     robot_radius = 1.0  # [m]
 
-    # set obstable positions
+    # set obstacle positions
     ox, oy = [], []
     for i in range(-10, 60):
         ox.append(i)

PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py

@@ -0,0 +1,278 @@
+"""
+
+Greedy Best-First grid planning
+
+author: Erwin Lejeune (@spida_rwin)
+
+See Wikipedia article (https://en.wikipedia.org/wiki/Best-first_search)
+
+"""
+
+import math
+
+import matplotlib.pyplot as plt
+
+show_animation = True
+
+
+class BestFirstSearchPlanner:
+
+    def __init__(self, ox, oy, reso, rr):
+        """
+        Initialize grid map for greedy best-first planning
+
+        ox: x position list of Obstacles [m]
+        oy: y position list of Obstacles [m]
+        reso: grid resolution [m]
+        rr: robot radius[m]
+        """
+
+        self.reso = reso
+        self.rr = rr
+        self.calc_obstacle_map(ox, oy)
+        self.motion = self.get_motion_model()
+
+    class Node:
+        def __init__(self, x, y, cost, pind, parent):
+            self.x = x  # index of grid
+            self.y = y  # index of grid
+            self.cost = cost
+            self.pind = pind
+            self.parent = parent
+
+        def __str__(self):
+            return str(self.x) + "," + str(self.y) + "," + str(
+                self.cost) + "," + str(self.pind)
+
+    def planning(self, sx, sy, gx, gy):
+        """
+        Greedy Best-First search
+
+        input:
+            sx: start x position [m]
+            sy: start y position [m]
+            gx: goal x position [m]
+            gy: goal y position [m]
+
+        output:
+            rx: x position list of the final path
+            ry: y position list of the final path
+        """
+
+        nstart = self.Node(self.calc_xyindex(sx, self.minx),
+                           self.calc_xyindex(sy, self.miny), 0.0, -1, None)
+        ngoal = self.Node(self.calc_xyindex(gx, self.minx),
+                          self.calc_xyindex(gy, self.miny), 0.0, -1, None)
+
+        open_set, closed_set = dict(), dict()
+        open_set[self.calc_grid_index(nstart)] = nstart
+
+        while 1:
+            if len(open_set) == 0:
+                print("Open set is empty..")
+                break
+
+            c_id = min(
+                open_set,
+                key=lambda o: self.calc_heuristic(ngoal, open_set[o]))
+
+            current = open_set[c_id]
+
+            # show graph
+            if show_animation:  # pragma: no cover
+                plt.plot(self.calc_grid_position(current.x, self.minx),
+                         self.calc_grid_position(current.y, self.miny), "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(closed_set.keys()) % 10 == 0:
+                    plt.pause(0.001)
+
+            # Remove the item from the open set
+            del open_set[c_id]
+
+            # Add it to the closed set
+            closed_set[c_id] = current
+
+            if current.x == ngoal.x and current.y == ngoal.y:
+                print("Found goal")
+                ngoal.pind = current.pind
+                ngoal.cost = current.cost
+                break
+
+            # expand_grid search grid based on motion model
+            for i, _ in enumerate(self.motion):
+                node = self.Node(current.x + self.motion[i][0],
+                                 current.y + self.motion[i][1],
+                                 current.cost + self.motion[i][2],
+                                 c_id, current)
+
+                n_id = self.calc_grid_index(node)
+
+                # If the node is not safe, do nothing
+                if not self.verify_node(node):
+                    continue
+
+                if n_id in closed_set:
+                    continue
+
+                if n_id not in open_set:
+                    open_set[n_id] = node
+                else:
+                    if open_set[n_id].cost > node.cost:
+                        open_set[n_id] = node
+        closed_set[ngoal.pind] = current
+        rx, ry = self.calc_final_path(ngoal, closed_set)
+        return rx, ry
+
+    def calc_final_path(self, ngoal, closedset):
+        # generate final course
+        rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [
+            self.calc_grid_position(ngoal.y, self.miny)]
+        n = closedset[ngoal.pind]
+        while n is not None:
+            rx.append(self.calc_grid_position(n.x, self.minx))
+            ry.append(self.calc_grid_position(n.y, self.miny))
+            n = n.parent
+
+        return rx, ry
+
+    @staticmethod
+    def calc_heuristic(n1, n2):
+        w = 1.0  # weight of heuristic
+        d = w * math.hypot(n1.x - n2.x, n1.y - n2.y)
+        return d
+
+    def calc_grid_position(self, index, minp):
+        """
+        calc grid position
+
+        :param index:
+        :param minp:
+        :return:
+        """
+        pos = index * self.reso + minp
+        return pos
+
+    def calc_xyindex(self, position, min_pos):
+        return round((position - min_pos) / self.reso)
+
+    def calc_grid_index(self, node):
+        return (node.y - self.miny) * self.xwidth + (node.x - self.minx)
+
+    def verify_node(self, node):
+        px = self.calc_grid_position(node.x, self.minx)
+        py = self.calc_grid_position(node.y, self.miny)
+
+        if px < self.minx:
+            return False
+        elif py < self.miny:
+            return False
+        elif px >= self.maxx:
+            return False
+        elif py >= self.maxy:
+            return False
+
+        # collision check
+        if self.obmap[node.x][node.y]:
+            return False
+
+        return True
+
+    def calc_obstacle_map(self, ox, oy):
+
+        self.minx = round(min(ox))
+        self.miny = round(min(oy))
+        self.maxx = round(max(ox))
+        self.maxy = round(max(oy))
+        print("minx:", self.minx)
+        print("miny:", self.miny)
+        print("maxx:", self.maxx)
+        print("maxy:", self.maxy)
+
+        self.xwidth = round((self.maxx - self.minx) / self.reso)
+        self.ywidth = round((self.maxy - self.miny) / self.reso)
+        print("xwidth:", self.xwidth)
+        print("ywidth:", self.ywidth)
+
+        # obstacle map generation
+        self.obmap = [[False for _ in range(self.ywidth)]
+                      for _ in range(self.xwidth)]
+        for ix in range(self.xwidth):
+            x = self.calc_grid_position(ix, self.minx)
+            for iy in range(self.ywidth):
+                y = self.calc_grid_position(iy, self.miny)
+                for iox, ioy in zip(ox, oy):
+                    d = math.hypot(iox - x, ioy - y)
+                    if d <= self.rr:
+                        self.obmap[ix][iy] = True
+                        break
+
+    @staticmethod
+    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_radius = 1.0  # [m]
+
+    # set obstacle positions
+    ox, oy = [], []
+    for i in range(-10, 60):
+        ox.append(i)
+        oy.append(-10.0)
+    for i in range(-10, 60):
+        ox.append(60.0)
+        oy.append(i)
+    for i in range(-10, 61):
+        ox.append(i)
+        oy.append(60.0)
+    for i in range(-10, 61):
+        ox.append(-10.0)
+        oy.append(i)
+    for i in range(-10, 40):
+        ox.append(20.0)
+        oy.append(i)
+    for i in range(0, 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, "og")
+        plt.plot(gx, gy, "xb")
+        plt.grid(True)
+        plt.axis("equal")
+
+    greedybestfirst = BestFirstSearchPlanner(ox, oy, grid_size, robot_radius)
+    rx, ry = greedybestfirst.planning(sx, sy, gx, gy)
+
+    if show_animation:  # pragma: no cover
+        plt.plot(rx, ry, "-r")
+        plt.pause(0.01)
+        plt.show()
+
+
+if __name__ == '__main__':
+    main()