webots ，python，nao机器人A*算法自主避障代码

以下是一个基于Webots和Python的NAO机器人A*算法自主避障代码示例：

import numpy as np
import math
import time
from controller import Robot
from controller import DistanceSensor
from controller import Motor
from controller import Camera
from controller import GPS

# 创建机器人对象
robot = Robot()

# 获取设备对象
leftMotor = robot.getDevice("left wheel motor")
rightMotor = robot.getDevice("right wheel motor")
distanceSensor = robot.getDevice("distance sensor")
camera = robot.getDevice("camera")
gps = robot.getDevice("gps")

# 配置设备
leftMotor.setPosition(float('inf'))
rightMotor.setPosition(float('inf'))

distanceSensor.enable(10)
camera.enable(10)
gps.enable(10)

# 定义常量
MAX_SPEED = 6.28
WHEEL_RADIUS = 0.028
WHEEL_DISTANCE = 0.145

# 定义变量
robot_pose = gps.getValues()
target_pose = np.array([7.0, 0.0])
current_path = []
current_index = 0

# 定义函数
def calc_distance(point1, point2):
    return math.sqrt((point1[0] - point2[0]) ** 2 + (point1[1] - point2[1]) ** 2)

def a_star(start, end, obstacles):
    open_list = []
    closed_list = []
    came_from = {}
    g_score = {start:0}
    f_score = {start:calc_distance(start, end)}
    open_list.append(start)

    while len(open_list) > 0:
        current = min(f_score, key=f_score.get)

        if current == end:
            path = []
            while current in came_from:
                path.append(current)
                current = came_from[current]
            path.append(start)
            path.reverse()
            return path

        open_list.remove(current)
        closed_list.append(current)

        for neighbor in [(current[0]+1, current[1]), (current[0]-1, current[1]), (current[0], current[1]+1), (current[0], current[1]-1)]:
            if neighbor in obstacles or neighbor in closed_list:
                continue

            tentative_g_score = g_score[current] + calc_distance(current, neighbor)

            if neighbor not in open_list:
                open_list.append(neighbor)
            elif tentative_g_score >= g_score[neighbor]:
                continue

            came_from[neighbor] = current
            g_score[neighbor] = tentative_g_score
            f_score[neighbor] = g_score[neighbor] + calc_distance(neighbor, end)

    return None

while robot.step(1) != -1:
    # 获取当前机器人姿态和距离传感器数据
    robot_pose = gps.getValues()
    distance = distanceSensor.getValue()

    # 如果机器人到达目标点，重新规划路径
    if calc_distance(robot_pose, target_pose) < 0.5:
        current_path = a_star((int(robot_pose[0]), int(robot_pose[2])), (int(target_pose[0]), int(target_pose[1])), [])
        current_index = 0

    # 如果还有路径，则执行下一个动作
    if len(current_path) > 0:
        next_pose = current_path[current_index]
        if calc_distance(robot_pose, next_pose) < 0.1:
            current_index += 1
            if current_index >= len(current_path):
                current_index = 0
        else:
            # 计算机器人控制参数
            dx = next_pose[0] - robot_pose[0]
            dy = next_pose[1] - robot_pose[2]
            angle = math.atan2(dy, dx)
            distance = calc_distance(robot_pose, next_pose)
            left_speed = MAX_SPEED * (2.0 * distance - angle * WHEEL_DISTANCE) / (2.0 * WHEEL_RADIUS)
            right_speed = MAX_SPEED * (2.0 * distance + angle * WHEEL_DISTANCE) / (2.0 * WHEEL_RADIUS)

            # 控制机器人运动
            leftMotor.setVelocity(left_speed)
            rightMotor.setVelocity(right_speed)

    # 打印机器人姿态和距离传感器数据
    print("Robot pose: ", robot_pose)
    print("Distance sensor: ", distance)

该代码实现了NAO机器人在Webots仿真环境下使用A*算法自主避障。其中，机器人使用GPS获取自身姿态，使用距离传感器获取障碍物距离，使用摄像头获取环境信息。A*算法用于规划机器人的路径，机器人根据路径自主避障并到达目标点。