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 = gridself.rows = len(grid)self.cols = len(grid[0])def get_neighbors(self, node):x, y = nodeneighbors = []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 + dyif 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 neighborsdef update_cell(self, x, y, new_value):self.grid[x][y] = new_valuedef is_obstacle(self, node):x, y = nodereturn 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 = xself.y = yself.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)] = nodes_goal_node = nodes[s_goal]s_goal_node.rhs = 0s_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] = nodeheapq.heapify(U)breakelse: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) ornodes[s_start].rhs > nodes[s_start].g):u = heapq.heappop(U)if u.g > u.rhs:u.g = u.rhselse: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 = 1if 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 = 0compute_shortest_path(U, s_start, nodes, k_m)path = []current = s_startwhile current != s_goal:path.append(current)neighbors = map_obj.get_neighbors(current)min_rhs = float('inf')next_node = Nonefor neighbor in neighbors:neighbor_node = nodes[neighbor]if neighbor_node.rhs < min_rhs:min_rhs = neighbor_node.rhsnext_node = neighborif next_node is None:print("未找到可行路径！")return []current = next_nodepath.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_startwhile current != s_goal:path.append(current)neighbors = map_obj.get_neighbors(current)min_rhs = float('inf')next_node = Nonefor neighbor in neighbors:neighbor_node = nodes[neighbor]if neighbor_node.rhs < min_rhs:min_rhs = neighbor_node.rhsnext_node = neighborif next_node is None:print("未找到可行路径！")return []current = next_nodepath.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。