机器人路径规划D*算法代码

D*算法是一种增量式的路径规划算法，它可以在已知起点和终点的情况下，通过不断地更新地图信息，实现快速的路径规划。下面是一个简单的Python实现：

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.parent = {}
        self.open_list = []
        self.close_list = []

    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.open_list, (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(self.start, u) + self.km

    def heuristic(self, u, v):
        return self.graph.distance(u, v)

    def update_vertex(self, u):
        if u != self.goal:
            self.rhs[u] = min([self.graph.distance(u, v) + self.g[v] for v 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.open_list and (self.open_list[0][0] < self.calculate_key(self.start) or self.g.get(self.start, float('inf')) != self.rhs.get(self.start, float('inf'))):
            k_old, u = heapq.heappop(self.open_list)
            if k_old < self.calculate_key(u):
                heapq.heappush(self.open_list, (self.calculate_key(u), u))
            elif self.g.get(u, float('inf')) > self.rhs.get(u, float('inf')):
                self.g[u] = self.rhs[u]
                for v in self.graph.neighbors(u):
                    self.update_vertex(v)
            else:
                self.g[u] = float('inf')
                self.update_vertex(u)
                for v in self.graph.neighbors(u):
                    self.update_vertex(v)

    def search(self, start, goal):
        self.initialize(start, goal)
        self.compute_shortest_path()
        path = []
        if self.g.get(self.start, float('inf')) == self.rhs.get(self.start, float('inf')):
            u = self.start
            while u != self.goal:
                path.append(u)
                min_cost = float('inf')
                min_cost_node = None
                for v in self.graph.neighbors(u):
                    cost = self.graph.distance(u, v) + self.g[v]
                    if cost < min_cost:
                        min_cost = cost
                        min_cost_node = v
                u = min_cost_node
            path.append(self.goal)
        return path

这个实现中，我们使用了一个优先队列来存储需要更新的节点，每次从队列中取出最小的节点进行更新。同时，我们使用了一个rhs字典来存储每个节点的rhs值，g字典来存储每个节点的g值，parent字典来存储每个节点的父节点。在更新节点时，我们首先更新rhs值，然后根据g和rhs的值来判断是否需要将节点加入优先队列中。在计算路径时，我们从起点开始，每次选择一个邻居节点中代价最小的节点作为下一个节点，直到到达终点。