Gazebo之Sensors简介

原创已于 2024-08-05 15:56:51 修改 · 1.6k 阅读

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于 2024-08-04 10:46:02 首次发布

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1. 源由

本章将学习如何将传感器添加到机器人仿真世界中。

典型的三种不同传感器：IMU传感器、接触传感器和激光雷达传感器。
同时，还将学习如何使用 gz launch 通过一个文件启动多个任务。

2. 初步准备

默认插件不会加载。因此，在添加传感器之前，首先需要添加一些逻辑上的默认设置，以便使用 GZ GUI：

<sdf version='1.9'>
  <world name='demo'>
    <plugin
        filename="gz-sim-physics-system"
        name="gz::sim::systems::Physics">
    </plugin>
    <plugin
        filename="gz-sim-scene-broadcaster-system"
        name="gz::sim::systems::SceneBroadcaster">
    </plugin>

    <!-- ... -->

测试资料：SnapLearnGazebo/lesson_02_sensor

3. 传感器

使用 moving_robot.sdf 作为基础SDF文件，创建一个新的文件 sensor_tutorial.sdf，在上面逐步添加上述传感器。

3.1 IMU 传感器

惯性测量单元 (IMU) 输出三个轴 (X, Y, Z) 的角速度和三个轴上的线性加速度。

Step 1: 引用 IMU 传感器

在标签下添加以下代码：

<plugin filename="gz-sim-imu-system"
        name="gz::sim::systems::Imu">
</plugin>

Step 2: 添加 IMU 传感器

添加到机器人世界中：

<always_on> 如果为 true，传感器将始终根据更新速率进行更新。
<update_rate> 生成传感器数据的频率。
如果为 true，传感器将在 GUI 中可视化。
发布数据的主题名称。

注：并非所有传感器都支持所有标签。

<sensor name="imu_sensor" type="imu">
    <always_on>1</always_on>
    <update_rate>1</update_rate>
    <visualize>true</visualize>
    <topic>imu</topic>
</sensor>

Step 3: 读取 IMU 传感数据

在一个终端中运行世界并按下播放按钮：

$ gz sim sensor_tutorial.sdf

在另一个终端中执行以下命令：

$ gz topic -e -t /imu

使用键盘的向上键移动你的机器人前进，可以看到传感器值在变化。

$ gz topic -e -t /imu
header {
  stamp {
    nsec: 1000000
  }
  data {
    key: "frame_id"
    value: "vehicle_blue::chassis::imu_sensor"
  }
  data {
    key: "seq"
    value: "0"
  }
}
entity_name: "vehicle_blue::chassis::imu_sensor"
orientation {
  x: 2.1215887269564552e-22
  y: 5.4613557001741309e-23
  z: 4.2036654388084205e-24
  w: 1
}
orientation_covariance {
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
}
angular_velocity {
  x: 4.24317745391291e-19
  y: 1.0922711400348261e-19
  z: 8.4073308776168415e-21
}
angular_velocity_covariance {
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
}
linear_acceleration {
  x: 3.5127917569011972e-18
  y: -2.8126787673456782e-17
}
linear_acceleration_covariance {
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
}

header {
  stamp {
    sec: 1
  }
  data {
    key: "frame_id"
    value: "vehicle_blue::chassis::imu_sensor"
  }
  data {
    key: "seq"
    value: "1"
  }
}
entity_name: "vehicle_blue::chassis::imu_sensor"
orientation {
  x: -3.1711735761273735e-06
  y: 5.1765558419587438e-06
  z: 0.0077879738067922165
  w: 0.99996967325371
}
orientation_covariance {
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
}
angular_velocity {
  x: 6.3239137476902e-05
  y: -9.01573735557911e-05
  z: 6.8026775775727753e-10
}
angular_velocity_covariance {
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
}
linear_acceleration {
  x: 0.00023964047644863543
  y: 0.00019414434201520824
  z: 9.7991386189340943
}
linear_acceleration_covariance {
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
}

header {
  stamp {
    sec: 2
  }
  data {
    key: "frame_id"
    value: "vehicle_blue::chassis::imu_sensor"
  }
  data {
    key: "seq"
    value: "2"
  }
}
entity_name: "vehicle_blue::chassis::imu_sensor"
orientation {
  x: -2.3117893552150796e-08
  y: 2.9654079887270204e-06
  z: 0.0077879344431916247
  w: 0.99996967357431121
}
orientation_covariance {
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
}
angular_velocity {
  x: -2.643416560507971e-11
  y: 5.5508724142631941e-06
  z: 2.61864093623602e-13
}
angular_velocity_covariance {
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
}
linear_acceleration {
  x: -7.0848758728578524e-05
  y: -1.1039375494702606e-09
  z: 9.799992568914794
}
linear_acceleration_covariance {
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
}

header {
  stamp {
    sec: 3
  }
  data {
    key: "frame_id"
    value: "vehicle_blue::chassis::imu_sensor"
  }
  data {
    key: "seq"
    value: "3"
  }
}
entity_name: "vehicle_blue::chassis::imu_sensor"
orientation {
  x: -2.3722197175311586e-08
  y: 3.0429764124486936e-06
  z: 0.0077879344081707427
  w: 0.99996967357435085
}
orientation_covariance {
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
}
angular_velocity {
  x: 8.7327529922574356e-12
  y: -5.277354213423571e-06
  z: -2.817821160315417e-11
}
angular_velocity_covariance {
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
}
linear_acceleration {
  x: -5.4889792669568007e-05
  y: -8.05116325338902e-10
  z: 9.7999923870500609
}
linear_acceleration_covariance {
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
  data: 0
}

3.2 Contact 传感器

接触传感器，这个传感器会在接触到其他物体时发出指示。

我们将构建一个障碍物（墙）并在其上添加接触传感器。如果机器人碰到障碍物，它将停止，从而防止机器人损坏自身。

Step 1: 构建障碍物

添加一堵墙：

<model name='wall'>
    <static>true</static>
    <pose>5 0 0 0 0 0</pose><!--pose relative to the world-->
    <link name='box'>
        <visual name='visual'>
            <geometry>
                <box>
                    <size>0.5 10.0 2.0</size>
                </box>
            </geometry>
            <!--let's add color to our link-->
            <material>
                <ambient>0.0 0.0 1.0 1</ambient>
                <diffuse>0.0 0.0 1.0 1</diffuse>
                <specular>0.0 0.0 1.0 1</specular>
            </material>
        </visual>
        <collision name='collision'>
            <geometry>
                <box>
                    <size>0.5 10.0 2.0</size>
                </box>
            </geometry>
        </collision>
    </link>
</model>

Step 2: 引入 contact 传感器

通过添加以下代码来定义接触传感器：

<plugin filename="gz-sim-contact-system"
        name="gz::sim::systems::Contact">
</plugin>

Step 3: 将传感器链接到墙模型

将接触传感器添加到墙模型的箱体链接中：

<sensor name='sensor_contact' type='contact'>
    <contact>
        <collision>collision</collision>
    </contact>
</sensor>

的定义很简单，我们只需定义传感器的名称和类型。
在 collision 中，我们定义箱体链接的碰撞名称为 collision。

Step 4: 添加 TouchPlugin事件

TouchPlugin 会在墙被触摸时发布（发送）一条消息。插件的标签如下：

：将与我们的墙接触的对象，在我们的例子中是 vehicle_blue。
：设置我们话题的命名空间，因此当机器人碰到墙时，它会向 /wall/touched 话题发送一条消息。

<plugin filename="gz-sim-touchplugin-system"
        name="gz::sim::systems::TouchPlugin">
    <target>vehicle_blue</target>
    <namespace>wall</namespace>
    <time>0.001</time>
    <enabled>true</enabled>
</plugin>

Step 5: 简单测试

在一个终端中运行世界并按下播放按钮：

$ gz sim sensor_tutorial.sdf

在另一个终端中执行以下命令：

$ gz topic -e -t /wall/touched

使用键盘箭头键将你的机器人向前驱动到墙壁，可以看到数据输出。

$ gz topic -e -t /wall/touched
data: true

Step 6: 添加 TriggeredPublisher 插件

<plugin filename="gz-sim-triggered-publisher-system"
        name="gz::sim::systems::TriggeredPublisher">
    <input type="gz.msgs.Boolean" topic="/wall/touched">
        <match>data: true</match>
    </input>
    <output type="gz.msgs.Twist" topic="/cmd_vel">
        linear: {x: 0.0}, angular: {z: 0.0}
    </output>
</plugin>

当我们在 /wall/touched 话题上接收到数据 true 时，我们发布 linear: {x: 0.0}, angular: {z: 0.0}，使我们的机器人停止。

3.3 Lidar 传感器

我们不希望机器人碰到墙壁，因为这可能会造成一些损坏，所以可以使用激光雷达（Lidar）代替接触传感器。Lidar 是“光探测和测距”的缩写。这个传感器可以帮助我们检测机器人周围的障碍物。我们将使用它来测量机器人和墙壁之间的距离。

Step 1: 固定激光雷达

首先，让我们创建一个框架来固定我们的激光雷达。

这应该添加到 vehicle_blue 的标签内，因为激光雷达框架是固定在机器人的底盘上的：

<frame name="lidar_frame" attached_to='chassis'>
    <pose>0.8 0 0.5 0 0 0</pose>
</frame>

Step 2: 引入 Lidar 传感器

然后在标签下添加这个插件，以便能够使用激光雷达传感器：

    <plugin
      filename="gz-sim-sensors-system"
      name="gz::sim::systems::Sensors">
      <render_engine>ogre2</render_engine>
    </plugin>

Step 3: 添加 Lidar 传感器

在底盘链接下，我们可以按如下方式添加激光雷达传感器：

首先，我们定义了传感器的名称和类型
然后，定义了它相对于 lidar_frame 的。
在中，我们定义了激光雷达数据将被发布的话题。
<update_rate> 是激光雷达数据生成的频率，在我们的例子中是 10 Hz，相当于 0.1 秒。
在和标签下，我们定义了水平和垂直激光束的属性。
- 是每个完整激光扫描周期生成的模拟激光雷达射线的数量。
- ：这个数字乘以样本数来确定范围数据点的数量。
- <min_angle> 和 <max_angle> 是生成的射线的角度范围。
在下，我们定义了每个模拟射线的范围属性：
- 和定义了每个激光雷达射线的最小和最大距离。
- 这里的标签定义了每个激光雷达射线的线性分辨率。
<always_on>：如果为 true，传感器将始终根据 <update_rate> 进行更新。
：如果为 true，传感器将在 GUI 中可视化。

<sensor name='gpu_lidar' type='gpu_lidar'>"
    <pose relative_to='lidar_frame'>0 0 0 0 0 0</pose>
    <topic>lidar</topic>
    <update_rate>10</update_rate>
    <ray>
        <scan>
            <horizontal>
                <samples>640</samples>
                <resolution>1</resolution>
                <min_angle>-1.396263</min_angle>
                <max_angle>1.396263</max_angle>
            </horizontal>
            <vertical>
                <samples>1</samples>
                <resolution>0.01</resolution>
                <min_angle>0</min_angle>
                <max_angle>0</max_angle>
            </vertical>
        </scan>
        <range>
            <min>0.08</min>
            <max>10.0</max>
            <resolution>0.01</resolution>
        </range>
    </ray>
    <always_on>1</always_on>
    <visualize>true</visualize>
</sensor>

Step 4: 简单测试

在一个终端中运行世界并按下播放按钮：

$ gz sim sensor_tutorial.sdf

在另一个终端中执行以下命令

$ gz topic -e -t /lidar

可以看到数据输出。

header {
  stamp {
    sec: 1
    nsec: 900000000
  }
  data {
    key: "frame_id"
    value: "vehicle_blue::chassis::gpu_lidar"
  }
  data {
    key: "seq"
    value: "18"
  }
}
frame: "vehicle_blue::chassis::gpu_lidar"
world_pose {
  position {
    x: 0.8
    z: 0.5
  }
  orientation {
    w: 1
  }
}
angle_min: -1.396263
angle_max: 1.396263
angle_step: 0.0043701502347417839
range_min: 0.08
range_max: 10
count: 640
vertical_angle_step: nan
vertical_count: 1
ranges: inf
ranges: inf
ranges: inf
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4. Lidar 避障

在机器人上安装了激光雷达（LIDAR），可以使用其测距数据让机器人避免撞到墙上。

为此，编写一个简短的 C++ 程序，该程序监听传感器数据并向机器人发送速度命令。这个程序被称为节点。

构建这个节点，订阅 /lidar 主题并读取其数据。

Step 1: 声明一个节点

该节点将发布到 cmd_vel 主题，并定义了消息类型 Twist。然后发布我们的节点。

gz::transport::Node node;
std::string topic_pub = "/cmd_vel";
gz::msgs::Twist data;
auto pub = node.Advertise<gz::msgs::Twist>(topic_pub);

Step 2: 回调检查

在回调函数中，检查所有光束的范围是否大于1.0。如果是这样，我们发布消息让汽车前进。否则，我们让机器人旋转。

void cb(const gz::msgs::LaserScan &_msg)
{
  bool allMore = true;
  for (int i = 0; i < _msg.ranges_size(); i++)
  {
    if (_msg.ranges(i) < 1.0)
    {
      allMore = false;
      break;
    }
  }
  if (allMore) //if all bigger than one
  {
    data.mutable_linear()->set_x(0.5);
    data.mutable_angular()->set_z(0.0);
  }
  else
  {
    data.mutable_linear()->set_x(0.0);
    data.mutable_angular()->set_z(0.5);
  }
  pub.Publish(data);
}

Step 3: 订阅数据源

在主函数中，订阅了 lidar 主题，并等待节点关闭。

int main(int argc, char **argv)
{
    std::string topic = "/lidar";
    // Subscribe to a topic by registering a callback.
    if (!node.Subscribe(topic, cb))
    {
        std::cerr << "Error subscribing to topic [" << topic << "]" << std::endl;
        return -1;
    }

    // Zzzzzz.
    gz::transport::waitForShutdown();

    return 0;
}

Step 4: 编译节点

$ mkdir build
$ cd build
$ cmake ..
$ make lidar_node

Step 5: 简单测试

$ ./build/lidar_node

$ gz sim sensor_tutorial.sdf

现在,可以看到机器人向前移动，当它接近墙壁时，它会开始向左转，直到前方没有障碍物后再次向前移动（确保按下左下角的播放按钮以启动机器人）。

5. Gazebo 启动

我们可以创建一个启动文件来同时运行 sensor_world 和 lidar_node，而不是从两个不同的终端运行两个不同的任务。

Step 1:

打开文本编辑器并添加以下代码。

<?xml version='1.0'?>
<gz version='1.0'>
  <executable name='sensor-world'>
    <command>gz sim sensor_tutorial.sdf</command>
  </executable>

  <executable name='lidar_node'>
    <command>./build/lidar_node</command>
  </executable>

</gz>