diff --git a/PathPlanning/AStar/a_star.py b/PathPlanning/AStar/a_star.py index cd5a365c..14c51ad9 100644 --- a/PathPlanning/AStar/a_star.py +++ b/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) diff --git a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py index ed1e5869..65d1fc31 100644 --- a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py +++ b/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( - 0) if event.key == 'escape' else None]) + lambda event: + [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], - current_A.y + self.motion[i][1], - current_A.cost + self.motion[i][2], - c_id_A) + c_nodes = [self.Node(current_A.x + self.motion[i][0], + current_A.y + self.motion[i][1], + current_A.cost + self.motion[i][2], + 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], - current_B.y + self.motion[i][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) + n_ids = [self.calc_grid_index(c_nodes[0]), + self.calc_grid_index(c_nodes[1])] # If the node is not safe, do nothing - if not self.verify_node(child_node_A): - continue_A = True + continue_ = self.check_nodes_and_sets(c_nodes, closed_set_A, + closed_set_B, n_ids) - if not self.verify_node(child_node_B): - continue_B = True - - 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: + if not continue_[0]: + if n_ids[0] 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 n_id_B not in open_set_B: + if not continue_[1]: + 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): - rx_A, ry_A = self.calc_final_path(meetnode_A, closed_set_A) - rx_B, ry_B = self.calc_final_path(meetnode_B, closed_set_B) + # takes two sets and two meeting nodes and return the optimal path + def calc_final_bidirectional_path(self, n1, n2, setA, setB): + 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 diff --git a/PathPlanning/BreadthFirstSearch/breadth_first_search.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py index dbdeff47..198ddd2e 100644 --- a/PathPlanning/BreadthFirstSearch/breadth_first_search.py +++ b/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( - 0) if event.key == 'escape' else None]) + lambda event: + [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) diff --git a/PathPlanning/DepthFirstSearch/depth_first_search.py b/PathPlanning/DepthFirstSearch/depth_first_search.py index d42aa5f1..229eb70c 100644 --- a/PathPlanning/DepthFirstSearch/depth_first_search.py +++ b/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( - 0) if event.key == 'escape' else None]) + lambda event: + [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) diff --git a/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py b/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py new file mode 100644 index 00000000..c29e37e8 --- /dev/null +++ b/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()