a*与dwa算法融合python

### 结合A*与DWA算法的Python实现

为了将A*路径规划算法与动态窗口法(Dynamic Window Approach, DWA)相结合，在移动机器人导航领域可以创建一种混合策略。该方法利用A*来寻找全局最优路径，而通过DWA实现在局部环境中的实时避障。

#### 定义工作空间和障碍物地图
首先定义机器人的操作环境以及静态障碍的位置信息：

import numpy as np

def create_map(width=100, height=100):
    """Create an empty map with given dimensions."""
    grid = np.zeros((height, width))
    
    # Add obstacles (example)
    obstacle_positions = [(20, 30), (40, 50)]
    for pos in obstacle_positions:
        y, x = pos
        grid[y][x] = 1
        
    return grid

#### 实现A*寻路功能
接着基于给定的地图数据结构化表示，采用优先队列优化版A*搜索最短路径至目标位置[^1]:

from heapq import heappop, heappush

class Node:
    def __init__(self, position=None, parent=None):
        self.position = position
        self.parent = parent
        self.g = 0
        self.h = 0
        self.f = 0
    
    def __eq__(self, other):
        return self.position == other.position

def astar(maze, start, end):
    open_list = []
    closed_list = []

    start_node = Node(start, None)
    goal_node = Node(end, None)

    heappush(open_list, (start_node.f, start_node))

    while len(open_list)>0:
        current_f, current_node = heappop(open_list)
        
        if current_node.position==goal_node.position:
            path = []
            temp = current_node
            
            while temp is not None:
                path.append(temp.position)
                temp = temp.parent
                
            return path[::-1]

        children = []
        adjacent_squares = ((0,-1),(0,1),(-1,0),(1,0)) 

        for new_position in adjacent_squares:

            node_position = (current_node.position[0]+new_position[0],
                             current_node.position[1]+new_position[1])

            within_range_criteria = [
                    node_position[0]>maze.shape[0]-1,
                    node_position[0]<0,
                    node_position[1]>maze.shape[1]-1,
                    node_position[1]<0]

            if any(within_range_criteria): continue

            if maze[node_position[0]][node_position[1]] != 0: continue

            new_node = Node(node_position, current_node)

            children.append(new_node)

        for child in children:
            
            if len([closed_child for closed_child in closed_list 
                   if closed_child == child])>0:continue

            child.g = current_node.g + 1
            child.h = abs(child.position[0] - goal_node.position[0]) \
                      +abs(child.position[1] - goal_node.position[1])
            child.f = child.g + child.h

            if len([open_node for open_node in open_list 
                   if open_node[1]==child and child.g > open_node[1].g ])>0:
                continue

            heappush(open_list,(child.f, child))
                
        closed_list.append(current_node)

    raise ValueError('No Path Found')

#### 动态窗口方法调整速度指令
当获得由A*产生的参考轨迹之后，应用DWA计算适合当前状态的速度控制量以避开动态障碍并沿预定路线前进:

def dwa_control(x, config, goal, ob):
    """
    Dynamic Window Algorithm control.
    :param x: state vector [x(m), y(m), yaw(rad), v(m/s), omega(rad/s)].
    :param config: robot parameter class.
    :param goal: goal position [x(m), y(m)].
    :param ob: obstacle positions [[x(m), y(m)],...].
    """

    dw = calc_dynamic_window(x, config)

    u, trajectory = calc_final_input(x, dw, config, goal, ob)

    return u, trajectory


def motion(x, u, dt):
    """
    Motion Model.
    :param x: state vector [x(m), y(m), yaw(rad), v(m/s), omega(rad/s)].
    :param u: input vector [v(m/s), omega(rad/s)].
    :param dt: time tick[s].
    """
    x[0] += u[0]*np.cos(x[2])*dt
    x[1] += u[0]*np.sin(x[2])*dt
    x[2] += u[1]*dt
    x[3] = u[0]
    x[4] = u[1]

    return x

此代码片段展示了如何构建一个简单的框架用于融合这两种技术；然而实际部署时还需要考虑更多细节比如传感器读数处理、更复杂的运动学建模等。