写出DWA避障算法Python实现

由于DWA算法较为复杂，需要多个函数的配合，下面是一个简化版的Python实现，仅供参考。

import math

# 声明常量
MAX_SPEED = 5.0  # 最大速度
MAX_ROTATION_SPEED = 0.5  # 最大旋转速度
VELOCITY_RESOLUTION = 0.1  # 速度分辨率
ROTATION_SPEED_RESOLUTION = 0.05  # 旋转速度分辨率
OBSTACLE_THRESHOLD = 0.5  # 障碍物距离阈值
STEP_SIZE = 0.1  # 步长
ROBOT_RADIUS = 0.2  # 机器人半径

def get_obstacle_distance(x, y, theta, obstacles):
    """计算机器人到最近障碍物的距离"""
    min_distance = float('inf')
    for obstacle in obstacles:
        obstacle_x, obstacle_y = obstacle
        d = math.sqrt(pow(obstacle_x - x, 2) + pow(obstacle_y - y, 2))
        if d < min_distance:
            min_distance = d
    return min_distance

def get_goal_distance(x, y, goal):
    """计算机器人到目标点的距离"""
    goal_x, goal_y = goal
    return math.sqrt(pow(goal_x - x, 2) + pow(goal_y - y, 2))

def get_allowable_velocities(x, y, theta, obstacles, goal):
    """计算可行的速度"""
    allowable_velocities = []
    for v in range(int(MAX_SPEED / VELOCITY_RESOLUTION)):
        v *= VELOCITY_RESOLUTION
        for w in range(int(MAX_ROTATION_SPEED / ROTATION_SPEED_RESOLUTION)):
            w -= int(MAX_ROTATION_SPEED / ROTATION_SPEED_RESOLUTION) / 2  # 使旋转速度为0时也能考虑
            w *= ROTATION_SPEED_RESOLUTION
            xt, yt, thetat = simulate_movement(x, y, theta, v, w)  # 模拟运动
            obstacle_distance = get_obstacle_distance(xt, yt, thetat, obstacles)
            goal_distance = get_goal_distance(xt, yt, goal)
            if obstacle_distance > OBSTACLE_THRESHOLD and goal_distance < obstacle_distance:
                allowable_velocities.append((v, w))
    return allowable_velocities

def simulate_movement(x, y, theta, v, w):
    """模拟运动"""
    xt = x + v * math.cos(theta) * STEP_SIZE
    yt = y + v * math.sin(theta) * STEP_SIZE
    thetat = theta + w * STEP_SIZE
    return xt, yt, thetat

def evaluate_controls(x, y, theta, obstacles, goal):
    """计算每个速度的cost，并选择代价最小的"""
    allowable_velocities = get_allowable_velocities(x, y, theta, obstacles, goal)
    if not allowable_velocities:
        return None
    allowable_velocities.sort(key=lambda velocity: velocity[1])  # 优先选择小的旋转速度
    min_cost = float('inf')
    best_velocity = None
    for v, w in allowable_velocities:
        xt, yt, thetat = simulate_movement(x, y, theta, v, w)  # 模拟运动
        obstacle_distance = get_obstacle_distance(xt, yt, thetat, obstacles)
        goal_distance = get_goal_distance(xt, yt, goal)
        cost = 1.5 * obstacle_distance + goal_distance  # 代价函数
        if cost < min_cost:
            min_cost = cost
            best_velocity = (v, w)
    return best_velocity

def move(x, y, theta, obstacles, goal):
    """执行移动"""
    control = evaluate_controls(x, y, theta, obstacles, goal)
    if control:
        v, w = control
        x, y, theta = simulate_movement(x, y, theta, v, w)
    return x, y, theta

这是一份简单实现，可能存在一些问题，需要自行进行测试和改进。完整的DWA算法实现中，一般还会包括对机器人姿态的处理、路径规划算法的配合等部分。