“D* Lite”（D星Lite）是一种用于动态环境中路径规划的增量式搜索算法。它旨在在已知地图上解决由动态环境变化引起的路径规划问题。下面是一个简单的示例Python代码，展示了D* Lite算法的基本实现：

import heapq

class Cell:
    def __init__(self, x, y):
        self.x = x
        self.y = y
        self.g = float('inf')  # 到达该细胞的成本
        self.rhs = float('inf')  # 启发式成本
        self.parent = None

    def __lt__(self, other):
        return (self.g, self.rhs) < (other.g, other.rhs)

class DStarLite:
    def __init__(self, grid, start, goal):
        self.grid = grid
        self.start = start
        self.goal = goal
        self.km = 0  # 估算的最短路径长度
        self.cells = {}  # 存储细胞信息
        self.open_list = []  # 存储待扩展的细胞

    def heuristic(self, cell):
        # 启发式函数，例如使用欧几里得距离
        return abs(cell.x - self.goal[0]) + abs(cell.y - self.goal[1])

    def initialize(self):
        # 初始化网格
        for x in range(len(self.grid)):
            for y in range(len(self.grid[0])):
                if self.grid[x][y] != 1:
                    self.cells[(x, y)] = Cell(x, y)
        self.cells[self.goal].rhs = 0
        heapq.heappush(self.open_list, self.cells[self.goal])

    def update_vertex(self, u):
        # 更新细胞u的启发式成本
        if u != self.goal:
            min_rhs = min((self.cells[(x, y)].g + self.cost(u, (x, y)) for x, y in self.neighbors(u)))
            self.cells[u].rhs = min_rhs
        if self.cells[u] in self.open_list:
            self.open_list.remove(self.cells[u])
        if self.cells[u].g != self.cells[u].rhs:
            heapq.heappush(self.open_list, self.cells[u])

    def compute_shortest_path(self):
        while self.open_list and (self.open_list[0].g < self.heuristic(self.start) or self.cells[self.start].rhs != self.cells[self.start].g):
            u = heapq.heappop(self.open_list)
            if u.g > u.rhs:
                u.g = u.rhs
                for pred in self.predecessors(u):
                    self.update_vertex(pred)
            else:
                u.g = float('inf')
                self.update_vertex(u)
                for pred in self.predecessors(u):
                    self.update_vertex(pred)

    def predecessors(self, cell):
        # 获取细胞的前驱细胞
        x, y = cell.x, cell.y
        preds = [(x-1, y), (x+1, y), (x, y-1), (x, y+1)]
        return [p for p in preds if p in self.cells]

    def neighbors(self, cell):
        # 获取细胞的邻居细胞
        x, y = cell.x, cell.y
        neighbors = [(x-1, y), (x+1, y), (x, y-1), (x, y+1)]
        return [n for n in neighbors if n in self.cells and self.grid[n[0]][n[1]] != 1]

    def cost(self, u, v):
        # 计算细胞u到细胞v的成本
        return 1 if self.grid[v[0]][v[1]] != 1 else float('inf')

    def replan(self):
        # 重新规划路径
        while self.start != self.goal:
            self.compute_shortest_path()
            min_rhs = min((self.cells[(x, y)].g + self.cost(self.start, (x, y)) for x, y in self.neighbors(self.start)))
            self.km += self.heuristic(self.start)
            self.start = min((self.neighbors(self.start), key=lambda n: self.cells[n].g + self.cost(self.start, n)))
            if min_rhs == float('inf'):
                break

# 示例用法
grid = [[0, 0, 0, 0, 0],
        [0, 1, 1, 0, 0],
        [0, 0, 1, 0, 0],
        [0, 0, 0, 0, 0]]

start = (0, 0)
goal = (3, 4)

d_star_lite = DStarLite(grid, start, goal)
d_star_lite.initialize()
d_star_lite.compute_shortest_path()
d_star_lite.replan()

以上是一个简单的D* Lite算法的实现示例。在实际应用中，你可能需要根据具体的情况进行更改和优化。