正文

从零开始，ROS2具身智能框架入门与实践指南

/0 浏览量
0507

引言

ROS（Robot Operating System）是一个用于机器人开发的跨平台、可扩展的软件框架。随着ROS2的发布，ROS社区迎来了新的发展阶段。ROS2提供了更加模块化和高效的通信机制，为具身智能（Embodied AI）的研究和实践提供了强大的支持。本文将从零开始，详细介绍ROS2具身智能框架的入门知识与实践方法。

ROS2简介

1. ROS2的背景

ROS最初由Willow Garage开发，后来由Open Robotics维护。随着机器人技术的快速发展，ROS逐渐成为机器人开发领域的标准框架。ROS2是ROS的下一代版本，它解决了ROS1中的一些性能和可扩展性问题，并引入了新的通信机制。

2. ROS2的特点

  • Distributed Communication: ROS2使用DDS（Data Distribution Service）作为通信协议，提供了更加高效和可靠的分布式通信。
  • C++11/14/17: ROS2主要使用C++11/14/17编写，支持现代C++特性。
  • QoS（Quality of Service）: ROS2提供了丰富的QoS选项，可以根据实际需求调整通信的可靠性和性能。
  • 中间件: ROS2使用eProsima Fast RTPS作为默认的中间件，支持多种编程语言。

ROS2具身智能框架入门

1. 环境搭建

首先，需要安装ROS2。以下是在Ubuntu 20.04上安装ROS2的步骤：

sudo apt update
sudo apt install -y \
  python3-rosdep \
  python3-rosinstall-generator \
  python3-wstool \
  python3-rosinstall

然后，使用rosdep工具安装ROS2依赖：

rosdep init
rosdep update
rosdep install --from-paths src --ignore-src --rosdistro bionic

2. 创建工作空间

创建一个新的工作空间，用于存放ROS2项目：

mkdir -p ~/catkin_ws/src
cd ~/catkin_ws/
catkin_make

3. 编写第一个节点

以下是一个简单的ROS2节点示例，它订阅一个名为/chatter的话题，并打印接收到的消息：

#include "rclcpp/rclcpp.hpp"

class MinimalPublisher : public rclcpp::Node
{
public:
  MinimalPublisher()
  : Node("minimal_publisher")
  {
    this->publisher_ = this->create_publisher<std_msgs::msg::String>("chatter", 10);
  }

  void publish_message()
  {
    auto msg = std_msgs::msg::String();
    msg.data = "Hello, ROS2!";
    this->publisher_->publish(msg);
  }

private:
  rclcpp::Publisher<std_msgs::msg::String>::SharedPtr publisher_;
};

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

编译并运行节点：

cd ~/catkin_ws/
catkin_make
source devel/setup.bash
ros2 run my_package minimal_publisher

此时，你可以在另一个终端中运行以下命令来订阅话题并打印消息：

ros2 topic echo /chatter

ROS2具身智能框架实践

1. 机器人控制

ROS2提供了丰富的库和工具，用于控制机器人。以下是一个使用rclcpp_action库控制机器人的示例：

#include "rclcpp/rclcpp.hpp"
#include "rclcpp_action/rclcpp_action.hpp"
#include "geometry_msgs/msg/twist.hpp"

class RobotController : public rclcpp::Node
{
public:
  RobotController()
  : Node("robot_controller")
  {
    this->action_client_ = rclcpp_action::create_client<geometry_msgs::msg::Twist>(
      this, "move_robot");
  }

  void move_robot()
  {
    auto goal = geometry_msgs::msg::Twist();
    goal.linear.x = 1.0;
    goal.angular.z = 0.5;

    auto send_goal_options = rclcpp_action::GoalResponse();
    send_goal_options = this->action_client_->send_goal(goal, std::bind(&RobotController::goal_callback, this, std::placeholders::_1));
    if (send_goal_options != rclcpp_action::GoalResponse::ACCEPT_AND_EXECUTE)
    {
      RCLCPP_ERROR(this->get_logger(), "Goal rejected by server");
      return;
    }

    auto feedback_callback = std::bind(&RobotController::feedback_callback, this, std::placeholders::_1, std::placeholders::_2);
    auto finish_callback = std::bind(&RobotController::finish_callback, this, std::placeholders::_1, std::placeholders::_2);
    this->action_client_->wait_for_result(std::bind(&RobotController::result_callback, this, std::placeholders::_1), feedback_callback, finish_callback);
  }

private:
  void goal_callback(rclcpp_action::GoalUUID uuid)
  {
    RCLCPP_INFO(this->get_logger(), "Received goal request for %s", uuid.get_string().c_str());
  }

  void feedback_callback(const std::shared_ptr<geometry_msgs::msg::Twist> feedback, double /*progress*/)
  {
    RCLCPP_INFO(this->get_logger(), "Robot moving at linear.x: %f, angular.z: %f", feedback->linear.x, feedback->angular.z);
  }

  void finish_callback(const rclcpp_action::GoalUUID &uuid, const rclcpp_action::Status &status)
  {
    RCLCPP_INFO(this->get_logger(), "Goal %s finished with status %s", uuid.get_string().c_str(), status.toString().c_str());
  }

  rclcpp_action::Client<geometry_msgs::msg::Twist>::SharedPtr action_client_;
};

int main(int argc, char **argv)
{
  rclcpp::init(argc, argv);
  auto node = std::make_shared<RobotController>();
  node->move_robot();
  rclcpp::shutdown();
  return 0;
}

编译并运行节点：

cd ~/catkin_ws/
catkin_make
source devel/setup.bash
ros2 run my_package robot_controller

2. 传感器数据处理

ROS2提供了丰富的传感器数据处理库，例如sensor_msgs和nav_msgs。以下是一个使用sensor_msgs::msg::Imu数据的示例：

#include "rclcpp/rclcpp.hpp"
#include "sensor_msgs/msg/imu.hpp"

class ImuProcessor : public rclcpp::Node
{
public:
  ImuProcessor()
  : Node("imu_processor")
  {
    this->imu_subscription_ = this->create_subscription<sensor_msgs::msg::Imu>(
      "imu_data", 10, std::bind(&ImuProcessor::imu_callback, this, std::placeholders::_1));
  }

private:
  void imu_callback(const sensor_msgs::msg::Imu::SharedPtr msg)
  {
    RCLCPP_INFO(this->get_logger(), "Received IMU data: orientation: (%f, %f, %f, %f), angular_velocity: (%f, %f, %f), linear_acceleration: (%f, %f, %f)",
      msg->orientation.x, msg->orientation.y, msg->orientation.z, msg->orientation.w,
      msg->angular_velocity.x, msg->angular_velocity.y, msg->angular_velocity.z,
      msg->linear_acceleration.x, msg->linear_acceleration.y, msg->linear_acceleration.z);
  }

  rclcpp::Subscription<sensor_msgs::msg::Imu>::SharedPtr imu_subscription_;
};

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

编译并运行节点：

cd ~/catkin_ws/
catkin_make
source devel/setup.bash
ros2 run my_package imu_processor

3. 机器人导航

ROS2提供了navigation2包，用于实现机器人导航。以下是一个使用navigation2包实现机器人导航的示例：

#include "rclcpp/rclcpp.hpp"
#include "nav2_msgs/action/navigate_to_goal.hpp"

class NavigationNode : public rclcpp::Node
{
public:
  NavigationNode()
  : Node("navigation_node")
  {
    this->action_client_ = rclcpp_action::create_client<nav2_msgs::action::NavigateToGoal>(
      this, "navigate_to_goal");
  }

  void navigate_to_goal()
  {
    auto goal = nav2_msgs::action::NavigateToGoal::Goal();
    goal.target_pose.pose.position.x = 1.0;
    goal.target_pose.pose.position.y = 1.0;

    auto send_goal_options = rclcpp_action::GoalResponse();
    send_goal_options = this->action_client_->send_goal(goal, std::bind(&NavigationNode::goal_callback, this, std::placeholders::_1));
    if (send_goal_options != rclcpp_action::GoalResponse::ACCEPT_AND_EXECUTE)
    {
      RCLCPP_ERROR(this->get_logger(), "Goal rejected by server");
      return;
    }

    auto feedback_callback = std::bind(&NavigationNode::feedback_callback, this, std::placeholders::_1, std::placeholders::_2);
    auto finish_callback = std::bind(&NavigationNode::finish_callback, this, std::placeholders::_1, std::placeholders::_2);
    this->action_client_->wait_for_result(std::bind(&NavigationNode::result_callback, this, std::placeholders::_1), feedback_callback, finish_callback);
  }

private:
  void goal_callback(rclcpp_action::GoalUUID uuid)
  {
    RCLCPP_INFO(this->get_logger(), "Received goal request for %s", uuid.get_string().c_str());
  }

  void feedback_callback(const std::shared_ptr<nav2_msgs::action::NavigateToGoal::Feedback> feedback, double /*progress*/)
  {
    RCLCPP_INFO(this->get_logger(), "Robot navigating to goal: progress: %f", feedback->progress);
  }

  void finish_callback(const rclcpp_action::GoalUUID &uuid, const rclcpp_action::Status &status)
  {
    RCLCPP_INFO(this->get_logger(), "Goal %s finished with status %s", uuid.get_string().c_str(), status.toString().c_str());
  }

  rclcpp_action::Client<nav2_msgs::action::NavigateToGoal>::SharedPtr action_client_;
};

int main(int argc, char **argv)
{
  rclcpp::init(argc, argv);
  auto node = std::make_shared<NavigationNode>();
  node->navigate_to_goal();
  rclcpp::shutdown();
  return 0;
}

编译并运行节点：

cd ~/catkin_ws/
catkin_make
source devel/setup.bash
ros2 run my_package navigation_node

总结

ROS2为具身智能框架提供了强大的支持，通过本文的介绍，相信你已经对ROS2具身智能框架有了基本的了解。在实际应用中，你可以根据自己的需求，结合ROS2提供的各种库和工具，开发出功能强大的机器人系统。希望本文对你有所帮助！

-- 展开阅读全文 --