python DSTAR
时间: 2023-12-08 11:39:07 浏览: 35
D*算法是一种路径规划算法,用于在已知地图上寻找从起点到目标点的最短路径。它是一种增量搜索算法,可以在地图上进行实时路径规划,同时可以在遇到新的障碍物时进行快速重新规划。下面是一个简单的Python D*算法实现的例子:
```python
import heapq
class DStar:
def __init__(self, graph):
self.graph = graph
self.start = None
self.goal = None
self.U = []
self.km = 0
self.rhs = {}
self.g = {}
self.back_pointers = {}
def initialize(self, start, goal):
self.start = start
self.goal = goal
self.rhs[self.goal] = 0
self.g[self.goal] = float('inf')
heapq.heappush(self.U, (self.calculate_key(self.goal), self.goal))
def calculate_key(self, u):
return (min(self.g.get(u, float('inf')), self.rhs.get(u, float('inf'))) + self.heuristic(u) + self.km, min(self.g.get(u, float('inf')), self.rhs.get(u, float('inf'))))
def heuristic(self, u):
return self.graph.distance(u, self.goal)
def update_vertex(self, u):
if u != self.goal:
self.rhs[u] = min([self.graph.distance(u, s) + self.g.get(s, float('inf')) for s in self.graph.neighbors(u)])
if (u in self.U):
self.U.remove(u)
if (self.g.get(u, float('inf')) != self.rhs.get(u, float('inf'))):
heapq.heappush(self.U, (self.calculate_key(u), u))
def compute_shortest_path(self):
while self.U and (self.U[0][0] < self.calculate_key(self.start) or self.g.get(self.start, float('inf')) != self.rhs.get(self.start, float('inf'))):
k_old = self.U[0][0]
u = heapq.heappop(self.U)[1]
k_new = self.calculate_key(u)
if k_old < k_new:
heapq.heappush(self.U, (k_new, u))
elif self.g.get(u, float('inf')) > self.rhs.get(u, float('inf')):
self.g[u] = self.rhs.get(u, float('inf'))
for s in self.graph.neighbors(u):
self.update_vertex(s)
else:
self.g[u] = float('inf')
self.update_vertex(u)
for s in self.graph.neighbors(u):
self.update_vertex(s)
def get_path(self):
path = [self.start]
while path[-1] != self.goal:
u = path[-1]
if self.g.get(u, float('inf')) == float('inf'):
return None
min_cost = float('inf')
min_cost_node = None
for s in self.graph.neighbors(u):
cost = self.graph.distance(u, s) + self.g.get(s, float('inf'))
if cost < min_cost:
min_cost = cost
min_cost_node = s
path.append(min_cost_node)
return path
class Graph:
def __init__(self):
self.edges = {}
def neighbors(self, node):
return self.edges[node]
def distance(self, u, v):
return self.edges[u][v]
def main():
graph = Graph()
graph.edges = {
'A': {'B': 1, 'C': 4},
'B': {'D': 3},
'C': {'D': 2},
'D': {}
}
dstar = DStar(graph)
dstar.initialize('A', 'D')
dstar.compute_shortest_path()
path = dstar.get_path()
if path is None:
print("No path found")
else:
print("Path:", path)
if __name__ == '__main__':
main()
```
这个例子中,我们首先定义了一个Graph类,用于存储地图信息。然后我们定义了一个DStar类,其中包含了D*算法的核心逻辑。在main函数中,我们创建了一个Graph对象,并将其传递给DStar对象。然后我们初始化了起点和终点,并调用compute_shortest_path方法计算最短路径。最后,我们调用get_path方法获取路径。如果路径不存在,则输出“No path found”。