用ROS2实现 智能助理小车（待完善）

功能有： 人脸识别 手势识别 智能避障 人机对话

硬件选择

1. 主控板：

   • 使用Raspberry Pi 4或NVIDIA Jetson Nano，具有较强的处理能力。

2. 摄像头：

   • 高分辨率摄像头模块。

3. 传感器：

   • 超声波传感器或红外传感器。

4. 麦克风和扬声器：

   • 高质量的麦克风模块和扬声器。

5. 电机和车体：

   • 直流电机和一个小车底盘。

软件框架

1. 操作系统：

   • 嵌入式Linux（如Raspbian或Ubuntu）。

2. 编程语言：

   • C++（主要用于性能关键部分），Python（用于高级任务和集成现有库）。

3. 中间件：

   • ROS2（机器人操作系统，版本选择较新的Foxy或Galactic）。

安装ROS2

在Raspberry Pi或Jetson Nano上安装ROS2，可以参考官方安装指南：ROS2 Installation

创建ROS2工作空间

创建并初始化一个ROS2工作空间：

mkdir -p ~/ros2_ws/src
cd ~/ros2_ws
colcon build
source install/local_setup.bash

创建ROS2包

在工作空间中创建新的ROS2包：

cd ~/ros2_ws/src
ros2 pkg create --build-type ament_cmake smart_car
cd ~/ros2_ws
colcon build
source install/local_setup.bash

功能模块的ROS2节点实现

1. 人脸识别

创建一个新的C++文件 face_recognition_node.cpp：

#include <rclcpp/rclcpp.hpp>
#include <sensor_msgs/msg/image.hpp>
#include <opencv2/opencv.hpp>
#include <dlib/opencv.h>
#include <dlib/image_processing/frontal_face_detector.h>
#include <dlib/image_processing/render_face_detections.h>
#include <dlib/image_processing.h>
#include <cv_bridge/cv_bridge.h>

class FaceRecognitionNode : public rclcpp::Node {
public:
    FaceRecognitionNode() : Node("face_recognition_node") {
        subscription_ = this->create_subscription<sensor_msgs::msg::Image>(
            "camera/image", 10, std::bind(&FaceRecognitionNode::faceRecognitionCallback, this, std::placeholders::_1));
        
        detector_ = dlib::get_frontal_face_detector();
        dlib::deserialize("shape_predictor_68_face_landmarks.dat") >> sp_;
    }

private:
    void faceRecognitionCallback(const sensor_msgs::msg::Image::SharedPtr msg) {
        cv_bridge::CvImagePtr cv_ptr;
        try {
            cv_ptr = cv_bridge::toCvCopy(msg, sensor_msgs::image_encodings::BGR8);
        } catch (cv_bridge::Exception& e) {
            RCLCPP_ERROR(this->get_logger(), "cv_bridge exception: %s", e.what());
            return;
        }

        dlib::cv_image<dlib::bgr_pixel> cimg(cv_ptr->image);
        std::vector<dlib::rectangle> faces = detector_(cimg);

        for (auto face : faces) {
            dlib::full_object_detection shape = sp_(cimg, face);
            // 处理人脸特征点
        }

        cv::imshow("Face Recognition", cv_ptr->image);
        cv::waitKey(30);
    }

    rclcpp::Subscription<sensor_msgs::msg::Image>::SharedPtr subscription_;
    dlib::frontal_face_detector detector_;
    dlib::shape_predictor sp_;
};

int main(int argc, char** argv) {
    rclcpp::init(argc, argv);
    rclcpp::spin(std::make_shared<FaceRecognitionNode>());
    rclcpp::shutdown();
    return 0;
}

2. 手势识别

创建一个新的Python文件 gesture_recognition_node.py：

import rclpy
from rclpy.node import Node
from sensor_msgs.msg import Image
from cv_bridge import CvBridge, CvBridgeError
import cv2
import mediapipe as mp

class GestureRecognitionNode(Node):
    def __init__(self):
        super().__init__('gesture_recognition_node')
        self.subscription = self.create_subscription(Image, 'camera/image', self.listener_callback, 10)
        self.bridge = CvBridge()
        self.mp_hands = mp.solutions.hands.Hands()

    def listener_callback(self, msg):
        try:
            cv_image = self.bridge.imgmsg_to_cv2(msg, "bgr8")
        except CvBridgeError as e:
            self.get_logger().error('Could not convert image: %s' % e)
            return

        image = cv2.cvtColor(cv2.flip(cv_image, 1), cv2.COLOR_BGR2RGB)
        results = self.mp_hands.process(image)

        if results.multi_hand_landmarks:
            for hand_landmarks in results.multi_hand_landmarks:
                mp.solutions.drawing_utils.draw_landmarks(image, hand_landmarks, mp.solutions.hands.HAND_CONNECTIONS)

        cv2.imshow('Hand Gesture Recognition', cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
        cv2.waitKey(5)

def main(args=None):
    rclpy.init(args=args)
    node = GestureRecognitionNode()
    try:
        rclpy.spin(node)
    except KeyboardInterrupt:
        pass
    node.destroy_node()
    rclpy.shutdown()

if __name__ == '__main__':
    main()

3. 智能避障

创建一个新的C++文件 obstacle_avoidance_node.cpp：

#include <rclcpp/rclcpp.hpp>
#include <std_msgs/msg/bool.hpp>
#include <wiringPi.h>

#define TRIG_PIN 0
#define ECHO_PIN 1

class ObstacleAvoidanceNode : public rclcpp::Node {
public:
    ObstacleAvoidanceNode() : Node("obstacle_avoidance_node") {
        publisher_ = this->create_publisher<std_msgs::msg::Bool>("obstacle_detected", 10);
        setupUltrasonic();
        timer_ = this->create_wall_timer(std::chrono::milliseconds(100), std::bind(&ObstacleAvoidanceNode::checkObstacle, this));
    }

private:
    void setupUltrasonic() {
        wiringPiSetup();
        pinMode(TRIG_PIN, OUTPUT);
        pinMode(ECHO_PIN, INPUT);
    }

    int getDistance() {
        digitalWrite(TRIG_PIN, LOW);
        delayMicroseconds(2);
        digitalWrite(TRIG_PIN, HIGH);
        delayMicroseconds(10);
        digitalWrite(TRIG_PIN, LOW);

        while (digitalRead(ECHO_PIN) == LOW);
        long startTime = micros();
        while (digitalRead(ECHO_PIN) == HIGH);
        long travelTime = micros() - startTime;

        int distance = travelTime / 58;
        return distance;
    }

    void checkObstacle() {
        auto message = std_msgs::msg::Bool();
        int distance = getDistance();
        if (distance < 30) {
            message.data = true;
        } else {
            message.data = false;
        }
        publisher_->publish(message);
    }

    rclcpp::Publisher<std_msgs::msg::Bool>::SharedPtr publisher_;
    rclcpp::TimerBase::SharedPtr timer_;
};

int main(int argc, char** argv) {
    rclcpp::init(argc, argv);
    rclcpp::spin(std::make_shared<ObstacleAvoidanceNode>());
    rclcpp::shutdown();
    return 0;
}

4. 人机对话

创建一个新的Python文件 voice_assistant_node.py：

import rclpy
from rclpy.node import Node
from std_msgs.msg import String
import openai
import speech_recognition as sr

openai.api_key = 'your-api-key'

def chat_with_gpt(prompt):
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=prompt,
        max_tokens=150
    )
    return response.choices[0].text.strip()

class VoiceAssistantNode(Node):
    def __init__(self):
        super().__init__('voice_assistant_node')
        self.publisher_ = self.create_publisher(String, 'voice_response', 10)
        self.recognizer = sr.Recognizer()
        self.mic = sr.Microphone()
        self.create_timer(1.0, self.listen_and_respond)

    def listen_and_respond(self):
        with self.mic as source:
            audio = self.recognizer.listen(source)
        try:
            text = self.recognizer.recognize_google(audio)
            response = chat_with_gpt(text)
            msg = String()
            msg.data = response
            self.publisher_.publish(msg)
        except sr.UnknownValueError:
            self.get_logger().warn("Could not understand the audio")
        except sr.RequestError:
            self.get_logger().warn("Could not request results")

def main(args=None):
    rclpy.init(args=args)
    node = VoiceAssistantNode()
    try:
        rclpy.spin(node)
    except KeyboardInterrupt:
        pass
    node.destroy_node()
    rclpy.shutdown()

if __name__ == '__main__':
    main()

创建launch文件

创建一个launch文件 smart_car_launch.py 来启动所有节点：

from launch import LaunchDescription
from launch_ros.actions import Node

def generate_launch_description():
    return LaunchDescription([
        Node(
            package='smart_car',
            executable='face_recognition_node',
            name='face_recognition_node'
        ),
        Node(
            package='smart_car',
            executable='gesture_recognition_node.py',
            name='gesture_recognition_node'
        ),
        Node(
            package='smart_car',
            executable='obstacle_avoidance_node',
            name='obstacle_avoidance_node'
        ),
        Node(
            package='smart_car',
            executable='voice_assistant_node.py',
            name='voice_assistant_node'
        )
    ])

编译和运行

编译工作空间：

cd ~/ros2_ws
colcon build
source install/local_setup.bash

运行launch文件：

ros2 launch smart_car smart_car_launch.py

通过上述步骤，您可以使用ROS2来协调各个功能模块，实现智能助理小车的所有功能。每个模块通过ROS2节点独立实现，并通过ROS2的发布/订阅机制进行通信。