mirror of
https://github.com/AtsushiSakai/PythonRobotics.git
synced 2026-04-22 03:00:22 -04:00
clean up informed_rrt_star.py (#785)
* clean up informed_rrt_star.py * clean up informed_rrt_star.py
This commit is contained in:
Atsushi Sakai
@@ -11,6 +11,7 @@ https://arxiv.org/pdf/1404.2334.pdf
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"""
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import sys
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import pathlib
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sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
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import copy
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@@ -27,18 +28,17 @@ show_animation = True
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class InformedRRTStar:
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def __init__(self, start, goal,
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obstacleList, randArea,
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expandDis=0.5, goalSampleRate=10, maxIter=200):
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def __init__(self, start, goal, obstacle_list, rand_area, expand_dis=0.5,
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goal_sample_rate=10, max_iter=200):
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self.start = Node(start[0], start[1])
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self.goal = Node(goal[0], goal[1])
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self.min_rand = randArea[0]
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self.max_rand = randArea[1]
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self.expand_dis = expandDis
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self.goal_sample_rate = goalSampleRate
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self.max_iter = maxIter
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self.obstacle_list = obstacleList
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self.min_rand = rand_area[0]
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self.max_rand = rand_area[1]
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self.expand_dis = expand_dis
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self.goal_sample_rate = goal_sample_rate
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self.max_iter = max_iter
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self.obstacle_list = obstacle_list
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self.node_list = None
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def informed_rrt_star_search(self, animation=True):
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@@ -46,110 +46,109 @@ class InformedRRTStar:
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self.node_list = [self.start]
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# max length we expect to find in our 'informed' sample space,
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# starts as infinite
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cBest = float('inf')
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solutionSet = set()
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c_best = float('inf')
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solution_set = set()
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path = None
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# Computing the sampling space
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cMin = math.sqrt(pow(self.start.x - self.goal.x, 2)
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+ pow(self.start.y - self.goal.y, 2))
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xCenter = np.array([[(self.start.x + self.goal.x) / 2.0],
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[(self.start.y + self.goal.y) / 2.0], [0]])
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a1 = np.array([[(self.goal.x - self.start.x) / cMin],
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[(self.goal.y - self.start.y) / cMin], [0]])
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c_min = math.hypot(self.start.x - self.goal.x,
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self.start.y - self.goal.y)
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x_center = np.array([[(self.start.x + self.goal.x) / 2.0],
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[(self.start.y + self.goal.y) / 2.0], [0]])
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a1 = np.array([[(self.goal.x - self.start.x) / c_min],
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[(self.goal.y - self.start.y) / c_min], [0]])
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e_theta = math.atan2(a1[1], a1[0])
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# first column of identity matrix transposed
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id1_t = np.array([1.0, 0.0, 0.0]).reshape(1, 3)
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M = a1 @ id1_t
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U, S, Vh = np.linalg.svd(M, True, True)
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C = np.dot(np.dot(U, np.diag(
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[1.0, 1.0, np.linalg.det(U) * np.linalg.det(np.transpose(Vh))])),
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Vh)
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m = a1 @ id1_t
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u, s, vh = np.linalg.svd(m, True, True)
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c = u @ np.diag(
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[1.0, 1.0,
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np.linalg.det(u) * np.linalg.det(np.transpose(vh))]) @ vh
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for i in range(self.max_iter):
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# Sample space is defined by cBest
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# cMin is the minimum distance between the start point and the goal
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# xCenter is the midpoint between the start and the goal
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# cBest changes when a new path is found
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# Sample space is defined by c_best
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# c_min is the minimum distance between the start point and
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# the goal x_center is the midpoint between the start and the
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# goal c_best changes when a new path is found
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rnd = self.informed_sample(cBest, cMin, xCenter, C)
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rnd = self.informed_sample(c_best, c_min, x_center, c)
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n_ind = self.get_nearest_list_index(self.node_list, rnd)
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nearestNode = self.node_list[n_ind]
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nearest_node = self.node_list[n_ind]
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# steer
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theta = math.atan2(rnd[1] - nearestNode.y, rnd[0] - nearestNode.x)
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newNode = self.get_new_node(theta, n_ind, nearestNode)
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d = self.line_cost(nearestNode, newNode)
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theta = math.atan2(rnd[1] - nearest_node.y,
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rnd[0] - nearest_node.x)
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new_node = self.get_new_node(theta, n_ind, nearest_node)
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d = self.line_cost(nearest_node, new_node)
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noCollision = self.check_collision(nearestNode, theta, d)
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no_collision = self.check_collision(nearest_node, theta, d)
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if noCollision:
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nearInds = self.find_near_nodes(newNode)
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newNode = self.choose_parent(newNode, nearInds)
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if no_collision:
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near_inds = self.find_near_nodes(new_node)
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new_node = self.choose_parent(new_node, near_inds)
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self.node_list.append(newNode)
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self.rewire(newNode, nearInds)
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self.node_list.append(new_node)
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self.rewire(new_node, near_inds)
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if self.is_near_goal(newNode):
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if self.check_segment_collision(newNode.x, newNode.y,
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if self.is_near_goal(new_node):
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if self.check_segment_collision(new_node.x, new_node.y,
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self.goal.x, self.goal.y):
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solutionSet.add(newNode)
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lastIndex = len(self.node_list) - 1
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tempPath = self.get_final_course(lastIndex)
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tempPathLen = self.get_path_len(tempPath)
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if tempPathLen < cBest:
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path = tempPath
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cBest = tempPathLen
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solution_set.add(new_node)
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last_index = len(self.node_list) - 1
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temp_path = self.get_final_course(last_index)
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temp_path_len = self.get_path_len(temp_path)
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if temp_path_len < c_best:
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path = temp_path
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c_best = temp_path_len
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if animation:
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self.draw_graph(xCenter=xCenter,
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cBest=cBest, cMin=cMin,
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self.draw_graph(x_center=x_center, c_best=c_best, c_min=c_min,
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e_theta=e_theta, rnd=rnd)
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return path
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def choose_parent(self, newNode, nearInds):
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if len(nearInds) == 0:
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return newNode
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def choose_parent(self, new_node, near_inds):
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if len(near_inds) == 0:
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return new_node
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dList = []
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for i in nearInds:
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dx = newNode.x - self.node_list[i].x
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dy = newNode.y - self.node_list[i].y
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d_list = []
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for i in near_inds:
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dx = new_node.x - self.node_list[i].x
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dy = new_node.y - self.node_list[i].y
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d = math.hypot(dx, dy)
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theta = math.atan2(dy, dx)
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if self.check_collision(self.node_list[i], theta, d):
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dList.append(self.node_list[i].cost + d)
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d_list.append(self.node_list[i].cost + d)
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else:
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dList.append(float('inf'))
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d_list.append(float('inf'))
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minCost = min(dList)
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minInd = nearInds[dList.index(minCost)]
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min_cost = min(d_list)
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min_ind = near_inds[d_list.index(min_cost)]
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if minCost == float('inf'):
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if min_cost == float('inf'):
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print("min cost is inf")
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return newNode
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return new_node
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newNode.cost = minCost
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newNode.parent = minInd
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new_node.cost = min_cost
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new_node.parent = min_ind
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return newNode
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return new_node
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def find_near_nodes(self, newNode):
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def find_near_nodes(self, new_node):
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n_node = len(self.node_list)
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r = 50.0 * math.sqrt((math.log(n_node) / n_node))
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d_list = [(node.x - newNode.x) ** 2 + (node.y - newNode.y) ** 2
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for node in self.node_list]
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d_list = [(node.x - new_node.x) ** 2 + (node.y - new_node.y) ** 2 for
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node in self.node_list]
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near_inds = [d_list.index(i) for i in d_list if i <= r ** 2]
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return near_inds
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def informed_sample(self, cMax, cMin, xCenter, C):
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if cMax < float('inf'):
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r = [cMax / 2.0,
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math.sqrt(cMax ** 2 - cMin ** 2) / 2.0,
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math.sqrt(cMax ** 2 - cMin ** 2) / 2.0]
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L = np.diag(r)
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xBall = self.sample_unit_ball()
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rnd = np.dot(np.dot(C, L), xBall) + xCenter
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def informed_sample(self, c_max, c_min, x_center, c):
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if c_max < float('inf'):
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r = [c_max / 2.0, math.sqrt(c_max ** 2 - c_min ** 2) / 2.0,
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math.sqrt(c_max ** 2 - c_min ** 2) / 2.0]
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rl = np.diag(r)
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x_ball = self.sample_unit_ball()
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rnd = np.dot(np.dot(c, rl), x_ball) + x_center
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rnd = [rnd[(0, 0)], rnd[(1, 0)]]
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else:
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rnd = self.sample_free_space()
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@@ -179,16 +178,15 @@ class InformedRRTStar:
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@staticmethod
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def get_path_len(path):
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pathLen = 0
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path_len = 0
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for i in range(1, len(path)):
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node1_x = path[i][0]
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node1_y = path[i][1]
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node2_x = path[i - 1][0]
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node2_y = path[i - 1][1]
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pathLen += math.sqrt((node1_x - node2_x)
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** 2 + (node1_y - node2_y) ** 2)
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path_len += math.hypot(node1_x - node2_x, node1_y - node2_y)
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return pathLen
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return path_len
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@staticmethod
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def line_cost(node1, node2):
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@@ -196,20 +194,20 @@ class InformedRRTStar:
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@staticmethod
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def get_nearest_list_index(nodes, rnd):
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dList = [(node.x - rnd[0]) ** 2
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+ (node.y - rnd[1]) ** 2 for node in nodes]
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minIndex = dList.index(min(dList))
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return minIndex
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d_list = [(node.x - rnd[0]) ** 2 + (node.y - rnd[1]) ** 2 for node in
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nodes]
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min_index = d_list.index(min(d_list))
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return min_index
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def get_new_node(self, theta, n_ind, nearestNode):
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newNode = copy.deepcopy(nearestNode)
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def get_new_node(self, theta, n_ind, nearest_node):
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new_node = copy.deepcopy(nearest_node)
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newNode.x += self.expand_dis * math.cos(theta)
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newNode.y += self.expand_dis * math.sin(theta)
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new_node.x += self.expand_dis * math.cos(theta)
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new_node.y += self.expand_dis * math.sin(theta)
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newNode.cost += self.expand_dis
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newNode.parent = n_ind
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return newNode
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new_node.cost += self.expand_dis
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new_node.parent = n_ind
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return new_node
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def is_near_goal(self, node):
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d = self.line_cost(node, self.goal)
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@@ -217,21 +215,21 @@ class InformedRRTStar:
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return True
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return False
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def rewire(self, newNode, nearInds):
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def rewire(self, new_node, near_inds):
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n_node = len(self.node_list)
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for i in nearInds:
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nearNode = self.node_list[i]
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for i in near_inds:
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near_node = self.node_list[i]
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d = math.hypot(nearNode.x - newNode.x, nearNode.y - newNode.y)
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d = math.hypot(near_node.x - new_node.x, near_node.y - new_node.y)
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s_cost = newNode.cost + d
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s_cost = new_node.cost + d
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if nearNode.cost > s_cost:
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theta = math.atan2(newNode.y - nearNode.y,
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newNode.x - nearNode.x)
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if self.check_collision(nearNode, theta, d):
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nearNode.parent = n_node - 1
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nearNode.cost = s_cost
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if near_node.cost > s_cost:
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theta = math.atan2(new_node.y - near_node.y,
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new_node.x - near_node.x)
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if self.check_collision(near_node, theta, d):
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near_node.parent = n_node - 1
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near_node.cost = s_cost
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@staticmethod
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def distance_squared_point_to_segment(v, w, p):
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@@ -251,45 +249,44 @@ class InformedRRTStar:
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def check_segment_collision(self, x1, y1, x2, y2):
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for (ox, oy, size) in self.obstacle_list:
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dd = self.distance_squared_point_to_segment(
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np.array([x1, y1]),
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np.array([x2, y2]),
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np.array([ox, oy]))
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np.array([x1, y1]), np.array([x2, y2]), np.array([ox, oy]))
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if dd <= size ** 2:
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return False # collision
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return True
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def check_collision(self, nearNode, theta, d):
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tmpNode = copy.deepcopy(nearNode)
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end_x = tmpNode.x + math.cos(theta) * d
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end_y = tmpNode.y + math.sin(theta) * d
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return self.check_segment_collision(tmpNode.x, tmpNode.y, end_x, end_y)
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def check_collision(self, near_node, theta, d):
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tmp_node = copy.deepcopy(near_node)
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end_x = tmp_node.x + math.cos(theta) * d
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end_y = tmp_node.y + math.sin(theta) * d
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return self.check_segment_collision(tmp_node.x, tmp_node.y,
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end_x, end_y)
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def get_final_course(self, lastIndex):
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def get_final_course(self, last_index):
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path = [[self.goal.x, self.goal.y]]
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while self.node_list[lastIndex].parent is not None:
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node = self.node_list[lastIndex]
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while self.node_list[last_index].parent is not None:
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node = self.node_list[last_index]
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path.append([node.x, node.y])
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lastIndex = node.parent
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last_index = node.parent
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path.append([self.start.x, self.start.y])
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return path
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def draw_graph(self, xCenter=None, cBest=None, cMin=None, e_theta=None,
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def draw_graph(self, x_center=None, c_best=None, c_min=None, e_theta=None,
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rnd=None):
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plt.clf()
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# for stopping simulation with the esc key.
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plt.gcf().canvas.mpl_connect(
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'key_release_event',
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lambda event: [exit(0) if event.key == 'escape' else None])
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'key_release_event', lambda event:
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[exit(0) if event.key == 'escape' else None])
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if rnd is not None:
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plt.plot(rnd[0], rnd[1], "^k")
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if cBest != float('inf'):
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self.plot_ellipse(xCenter, cBest, cMin, e_theta)
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if c_best != float('inf'):
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self.plot_ellipse(x_center, c_best, c_min, e_theta)
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for node in self.node_list:
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if node.parent is not None:
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if node.x or node.y is not None:
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plt.plot([node.x, self.node_list[node.parent].x], [
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node.y, self.node_list[node.parent].y], "-g")
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plt.plot([node.x, self.node_list[node.parent].x],
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[node.y, self.node_list[node.parent].y], "-g")
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for (ox, oy, size) in self.obstacle_list:
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plt.plot(ox, oy, "ok", ms=30 * size)
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@@ -301,13 +298,13 @@ class InformedRRTStar:
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plt.pause(0.01)
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@staticmethod
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def plot_ellipse(xCenter, cBest, cMin, e_theta): # pragma: no cover
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def plot_ellipse(x_center, c_best, c_min, e_theta): # pragma: no cover
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a = math.sqrt(cBest ** 2 - cMin ** 2) / 2.0
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b = cBest / 2.0
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a = math.sqrt(c_best ** 2 - c_min ** 2) / 2.0
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b = c_best / 2.0
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angle = math.pi / 2.0 - e_theta
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cx = xCenter[0]
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cy = xCenter[1]
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cx = x_center[0]
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cy = x_center[1]
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t = np.arange(0, 2 * math.pi + 0.1, 0.1)
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x = [a * math.cos(it) for it in t]
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y = [b * math.sin(it) for it in t]
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@@ -331,18 +328,12 @@ def main():
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print("Start informed rrt star planning")
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# create obstacles
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obstacleList = [
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(5, 5, 0.5),
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(9, 6, 1),
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(7, 5, 1),
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(1, 5, 1),
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(3, 6, 1),
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(7, 9, 1)
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]
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obstacle_list = [(5, 5, 0.5), (9, 6, 1), (7, 5, 1), (1, 5, 1), (3, 6, 1),
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(7, 9, 1)]
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# Set params
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rrt = InformedRRTStar(start=[0, 0], goal=[5, 10],
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randArea=[-2, 15], obstacleList=obstacleList)
|
||||
rrt = InformedRRTStar(start=[0, 0], goal=[5, 10], rand_area=[-2, 15],
|
||||
obstacle_list=obstacle_list)
|
||||
path = rrt.informed_rrt_star_search(animation=show_animation)
|
||||
print("Done!!")
|
||||
|
||||
|
||||
Reference in New Issue
Block a user