mirror of
https://github.com/zhm-real/PathPlanning.git
synced 2026-08-30 00:50:46 +08:00
zhm-real
135 lines
3.7 KiB
Python
135 lines
3.7 KiB
Python
"""
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Dijkstra 2D
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@author: huiming zhou
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"""
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import os
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import sys
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import math
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sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
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"/../../Search-based Planning/")
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from Search_2D import queue
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from Search_2D import plotting
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from Search_2D import env
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class Dijkstra:
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def __init__(self, s_start, s_goal):
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self.s_start, self.s_goal = s_start, s_goal
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self.Env = env.Env()
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self.plotting = plotting.Plotting(self.s_start, self.s_goal)
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self.u_set = self.Env.motions # feasible input set
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self.obs = self.Env.obs # position of obstacles
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self.g = {self.s_start: 0, self.s_goal: float("inf")} # cost to come
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self.OPEN = queue.QueuePrior() # priority queue / OPEN set
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self.OPEN.put(self.s_start, 0)
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self.CLOSED = [] # closed set & visited
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self.PARENT = {self.s_start: self.s_start}
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def searching(self):
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"""
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Dijkstra Searching.
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:return: path, order of visited nodes in the planning
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"""
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while not self.OPEN.empty():
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s = self.OPEN.get()
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self.CLOSED.append(s)
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if s == self.s_goal:
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break
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for s_n in self.get_neighbor(s):
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new_cost = self.g[s] + self.cost(s, s_n)
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if s_n not in self.g:
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self.g[s_n] = float("inf")
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if new_cost < self.g[s_n]:
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self.g[s_n] = new_cost
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self.OPEN.put(s_n, new_cost)
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self.PARENT[s_n] = s
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return self.extract_path(), self.CLOSED
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def get_neighbor(self, s):
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"""
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find neighbors of state s that not in obstacles.
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:param s: state
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:return: neighbors
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"""
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s_list = []
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for u in self.u_set:
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s_list.append(tuple([s[i] + u[i] for i in range(2)]))
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return s_list
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def extract_path(self):
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"""
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Extract the path based on PARENT set.
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:return: The planning path
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"""
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path = [self.s_goal]
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s = self.s_goal
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while True:
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s = self.PARENT[s]
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path.append(s)
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if s == self.s_start:
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break
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return list(path)
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def cost(self, s_start, s_goal):
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"""
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Calculate cost for this motion
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:param s_start: starting node
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:param s_goal: end node
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:return: cost for this motion
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:note: cost function could be more complicate!
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"""
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if self.is_collision(s_start, s_goal):
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return float("inf")
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return math.hypot(s_goal[0] - s_start[0], s_goal[1] - s_start[1])
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def is_collision(self, s_start, s_end):
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if s_start in self.obs or s_end in self.obs:
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return True
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if s_start[0] != s_end[0] and s_start[1] != s_end[1]:
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if s_end[0] - s_start[0] == s_start[1] - s_end[1]:
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s1 = (min(s_start[0], s_end[0]), min(s_start[1], s_end[1]))
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s2 = (max(s_start[0], s_end[0]), max(s_start[1], s_end[1]))
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else:
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s1 = (min(s_start[0], s_end[0]), max(s_start[1], s_end[1]))
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s2 = (max(s_start[0], s_end[0]), min(s_start[1], s_end[1]))
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if s1 in self.obs or s2 in self.obs:
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return True
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return False
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def main():
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s_start = (5, 5)
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s_goal = (45, 25)
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dijkstra = Dijkstra(s_start, s_goal)
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plot = plotting.Plotting(s_start, s_goal)
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path, visited = dijkstra.searching()
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plot.animation(path, visited, "Dijkstra's") # animation generate
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if __name__ == '__main__':
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main()
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