人工智能D* Lite 算法-动态障碍物处理、多步预测和启发式函数优化

在智能驾驶领域，D* Lite 算法是一种高效的动态路径规划算法，适用于处理环境变化时的路径重规划问题。以下将为你展示 D* Lite 算法的高级用法，包含动态障碍物处理、多步预测和启发式函数优化等方面的代码实现。

代码实现

import heapq
import math

# 地图类，用于管理地图信息和更新
class Map:
    def __init__(self, grid):
        self.grid = grid
        self.rows = len(grid)
        self.cols = len(grid[0])

    def get_neighbors(self, node):
        x, y = node
        neighbors = []
        for dx, dy in [(0, 1), (0, -1), (1, 0), (-1, 0), (1, 1), (1, -1), (-1, 1), (-1, -1)]:
            new_x, new_y = x + dx, y + dy
            if 0 <= new_x < self.rows and 0 <= new_y < self.cols and self.grid[new_x][new_y] == 0:
                neighbors.append((new_x, new_y))
        return neighbors

    def update_cell(self, x, y, new_value):
        self.grid[x][y] = new_value

    def is_obstacle(self, node):
        x, y = node
        return self.grid[x][y] == 1

# 节点类，用于存储节点信息
class Node:
    def __init__(self, x, y):
        self.x = x
        self.y = y
        self.g = float('inf')
        self.rhs = float('inf')
        self.key = [float('inf'), float('inf')]

    def __lt__(self, other):
        return self.key < other.key

# 优化的启发式函数：考虑对角线移动的欧几里得距离
def heuristic(a, b):
    dx = abs(a[0] - b[0])
    dy = abs(a[1] - b[1])
    return math.sqrt(dx**2 + dy**2)

# 计算节点的键值
def calculate_key(node, s_start, k_m):
    node.key = [min(node.g, node.rhs) + heuristic((node.x, node.y), s_start) + k_m,
                min(node.g, node.rhs)]
    return node

# 初始化 D* Lite 算法
def initialize(s_start, s_goal):
    U = []
    nodes = {}
    for i in range(map_obj.rows):
        for j in range(map_obj.cols):
            node = Node(i, j)
            nodes[(i, j)] = node
    s_goal_node = nodes[s_goal]
    s_goal_node.rhs = 0
    s_goal_node = calculate_key(s_goal_node, s_start, 0)
    heapq.heappush(U, s_goal_node)
    return U, nodes

# 更新节点的 rhs 值
def update_vertex(U, node, s_start, k_m):
    if node.g != node.rhs:
        node = calculate_key(node, s_start, k_m)
        for i, n in enumerate(U):
            if n.x == node.x and n.y == node.y:
                U[i] = node
                heapq.heapify(U)
                break
        else:
            heapq.heappush(U, node)
    else:
        for i, n in enumerate(U):
            if n.x == node.x and n.y == node.y:
                U.pop(i)
                heapq.heapify(U)
                break

# 计算最短路径
def compute_shortest_path(U, s_start, nodes, k_m):
    while U and (U[0].key < calculate_key(nodes[s_start], s_start, k_m) or
                 nodes[s_start].rhs > nodes[s_start].g):
        u = heapq.heappop(U)
        if u.g > u.rhs:
            u.g = u.rhs
        else:
            u.g = float('inf')
            update_vertex(U, u, s_start, k_m)
        for neighbor in map_obj.get_neighbors((u.x, u.y)):
            neighbor_node = nodes[neighbor]
            if neighbor != s_start:
                cost = 1
                if abs(u.x - neighbor[0]) + abs(u.y - neighbor[1]) == 2:
                    cost = math.sqrt(2)  # 对角线移动代价
                neighbor_node.rhs = min(neighbor_node.rhs,
                                        u.g + cost)
            update_vertex(U, neighbor_node, s_start, k_m)

# 动态障碍物处理和多步预测
def handle_dynamic_obstacles(U, nodes, s_start, s_goal, k_m, dynamic_obstacles):
    for obstacle in dynamic_obstacles:
        obstacle_node = nodes[obstacle]
        map_obj.update_cell(obstacle[0], obstacle[1], 1)
        for neighbor in map_obj.get_neighbors(obstacle):
            neighbor_node = nodes[neighbor]
            neighbor_node.rhs = float('inf')
            update_vertex(U, neighbor_node, s_start, k_m)
    compute_shortest_path(U, s_start, nodes, k_m)

# 路径规划函数
def d_star_lite(s_start, s_goal, dynamic_obstacles=[]):
    U, nodes = initialize(s_start, s_goal)
    k_m = 0
    compute_shortest_path(U, s_start, nodes, k_m)
    path = []
    current = s_start
    while current != s_goal:
        path.append(current)
        neighbors = map_obj.get_neighbors(current)
        min_rhs = float('inf')
        next_node = None
        for neighbor in neighbors:
            neighbor_node = nodes[neighbor]
            if neighbor_node.rhs < min_rhs:
                min_rhs = neighbor_node.rhs
                next_node = neighbor
        if next_node is None:
            print("未找到可行路径！")
            return []
        current = next_node
    path.append(s_goal)
    # 处理动态障碍物
    if dynamic_obstacles:
        handle_dynamic_obstacles(U, nodes, s_start, s_goal, k_m, dynamic_obstacles)
        path = []
        current = s_start
        while current != s_goal:
            path.append(current)
            neighbors = map_obj.get_neighbors(current)
            min_rhs = float('inf')
            next_node = None
            for neighbor in neighbors:
                neighbor_node = nodes[neighbor]
                if neighbor_node.rhs < min_rhs:
                    min_rhs = neighbor_node.rhs
                    next_node = neighbor
            if next_node is None:
                print("未找到可行路径！")
                return []
            current = next_node
        path.append(s_goal)
    return path

# 示例地图
map_grid = [
    [0, 0, 0, 0, 0],
    [0, 1, 1, 0, 0],
    [0, 0, 0, 0, 0],
    [0, 0, 1, 1, 0],
    [0, 0, 0, 0, 0]
]
map_obj = Map(map_grid)

# 起点和终点
s_start = (0, 0)
s_goal = (4, 4)

# 初始路径规划
path = d_star_lite(s_start, s_goal)
if path:
    print("初始规划的路径:", path)

# 模拟动态障碍物出现
dynamic_obstacles = [(2, 2)]
path = d_star_lite(s_start, s_goal, dynamic_obstacles)
if path:
    print("出现动态障碍物后重新规划的路径:", path)

代码解释

1. 地图类（Map）

• get_neighbors：不仅考虑上下左右移动，还考虑了对角线移动，扩大了节点的搜索范围。
• is_obstacle：判断节点是否为障碍物。

2. 节点类（Node）

• 存储节点的坐标、g 值（从起点到该节点的实际代价）、rhs 值（到该节点的最短路径的估计代价）和键值 key。

3. 启发式函数（heuristic）

• 采用考虑对角线移动的欧几里得距离作为启发式函数，更准确地估计节点到目标节点的代价。

4. D* Lite 算法核心函数

• initialize：初始化算法，创建节点字典和优先队列 U，将目标节点加入队列。
• calculate_key：计算节点的键值，用于优先队列的排序。
• update_vertex：更新节点的 rhs 值，并根据情况更新优先队列。
• compute_shortest_path：计算最短路径，不断更新节点的 g 和 rhs 值，直到找到最短路径或队列为空。

5. 动态障碍物处理和多步预测

• handle_dynamic_obstacles：处理动态障碍物的出现，更新受影响节点的 rhs 值，并重新计算最短路径。

6. 路径规划函数（d_star_lite）

• 主路径规划函数，先进行初始路径规划，若存在动态障碍物，则调用 handle_dynamic_obstacles 重新规划路径。

注意事项

• 代码中的动态障碍物处理是简单模拟，实际应用中需要结合传感器数据实时更新障碍物信息。
• 启发式函数和移动代价的计算可以根据具体场景进行调整，以提高路径规划的效率和准确性。
• 代码中未考虑车辆的运动学约束，实际智能驾驶中需要进一步考虑车辆的转弯半径、速度限制等因素。