Skip to main content

Advanced Physics and Sensor Simulation in Gazebo

Overview​

This lesson explores advanced topics in Gazebo, including fine-tuning physics parameters, simulating complex interactions, and integrating various sensors like LiDAR, Depth Cameras, and IMUs to gather realistic data.

Learning Objectives​

By the end of this lesson, you should be able to:

  • Configure advanced physics parameters for realistic simulation
  • Implement and tune different physics properties (friction, restitution, etc.)
  • Integrate and configure various robot sensors in Gazebo
  • Understand the differences between various physics engines
  • Optimize simulation performance for complex scenarios

Advanced Physics Configuration​

Physics Engine Selection​

Gazebo supports multiple physics engines, each with different characteristics:

  • ODE (Open Dynamics Engine): Default, good for most applications
  • Bullet: Good performance, widely used
  • DART: Advanced contact modeling
  • Simbody: Biomechanics-focused

To specify a physics engine in your world file:

<world name="my_world">
  <physics type="ode">
    <!-- ODE-specific parameters -->
  </physics>
</world>

Fine-tuning Physics Parameters​

For realistic simulation, tune these parameters carefully:

Time Step Configuration​

<physics type="ode">
  <max_step_size>0.001</max_step_size>      <!-- Smaller = more accurate but slower -->
  <real_time_factor>1.0</real_time_factor>  <!-- Target simulation speed -->
  <real_time_update_rate>1000</real_time_update_rate> <!-- Updates per second -->
</physics>

Solver Parameters​

<physics type="ode">
  <ode>
    <solver>
      <type>quick</type>          <!-- Type of solver -->
      <iters>10</iters>           <!-- Number of iterations -->
      <sor>1.3</sor>              <!-- Successive over-relaxation -->
    </solver>
    <constraints>
      <cfm>0.000001</cfm>         <!-- Constraint force mixing -->
      <erp>0.2</erp>              <!-- Error reduction parameter -->
      <contact_max_correcting_vel>100</contact_max_correcting_vel>
      <contact_surface_layer>0.001</contact_surface_layer>
    </constraints>
  </ode>
</physics>

Material Properties and Interactions​

Friction Parameters​

Properly setting friction is crucial for realistic robot movement:

<gazebo reference="wheel_link">
  <mu1>10.0</mu1>    <!-- Primary friction coefficient -->
  <mu2>10.0</mu2>    <!-- Secondary friction coefficient -->
  <fdir1>1 0 0</fdir1> <!-- Friction direction -->
</gazebo>

Restitution (Bounciness)​

<gazebo reference="ball_link">
  < restitution_coefficient >0.8</restitution_coefficient>  <!-- How bouncy -->
  < bounce_threshold >0.1</bounce_threshold>                <!-- Velocity threshold for bouncing -->
</gazebo>

Sensor Simulation in Gazebo​

LiDAR Sensors​

LiDAR sensors are crucial for navigation and mapping:

<gazebo reference="lidar_link">
  <sensor name="lidar_sensor" type="ray">
    <pose>0 0 0 0 0 0</pose>
    <visualize>true</visualize>
    <update_rate>10</update_rate>
    <ray>
      <scan>
        <horizontal>
          <samples>720</samples>        <!-- Number of rays -->
          <resolution>1</resolution>     <!-- Resolution -->
          <min_angle>-3.14159</min_angle> <!-- Min angle (-180īŋŊ) -->
          <max_angle>3.14159</max_angle>  <!-- Max angle (180īŋŊ) -->
        </horizontal>
      </scan>
      <range>
        <min>0.1</min>    <!-- Minimum range -->
        <max>30.0</max>   <!-- Maximum range -->
        <resolution>0.01</resolution> <!-- Range resolution -->
      </range>
    </ray>
    <plugin name="lidar_controller" filename="libgazebo_ros_ray_sensor.so">
      <ros>
        <namespace>lidar</namespace>
        <remapping>~/out:=scan</remapping>
      </ros>
      <output_type>sensor_msgs/LaserScan</output_type>
    </plugin>
  </sensor>
</gazebo>

Depth Camera Sensors​

Depth cameras provide 3D perception capabilities:

<gazebo reference="camera_link">
  <sensor name="depth_camera" type="depth">
    <update_rate>30</update_rate>
    <camera name="head">
      <horizontal_fov>1.047</horizontal_fov> <!-- 60 degrees -->
      <image>
        <format>R8G8B8</format>
        <width>640</width>
        <height>480</height>
      </image>
      <clip>
        <near>0.1</near>
        <far>10.0</far>
      </clip>
    </camera>
    <always_on>true</always_on>
    <visualize>true</visualize>
    <plugin name="camera_controller" filename="libgazebo_ros_camera.so">
      <ros>
        <namespace>camera</namespace>
        <remapping>image_raw:=image</remapping>
        <remapping>camera_info:=camera_info</remapping>
      </ros>
      <camera_name>depth_camera</camera_name>
      <frame_name>camera_optical_frame</frame_name>
    </plugin>
  </sensor>
</gazebo>

IMU Sensors​

IMU sensors provide orientation and acceleration data:

<gazebo reference="imu_link">
  <sensor name="imu_sensor" type="imu">
    <always_on>true</always_on>
    <update_rate>100</update_rate>
    <visualize>false</visualize>
    <imu>
      <angular_velocity>
        <x>
          <noise type="gaussian">
            <mean>0.0</mean>
            <stddev>0.0017</stddev>  <!-- ~0.1 deg/s stddev -->
          </noise>
        </x>
        <y>
          <noise type="gaussian">
            <mean>0.0</mean>
            <stddev>0.0017</stddev>
          </noise>
        </y>
        <z>
          <noise type="gaussian">
            <mean>0.0</mean>
            <stddev>0.0017</stddev>
          </noise>
        </z>
      </angular_velocity>
      <linear_acceleration>
        <x>
          <noise type="gaussian">
            <mean>0.0</mean>
            <stddev>0.017</stddev>  <!-- ~0.017 m/sīŋŊ stddev -->
          </noise>
        </x>
        <y>
          <noise type="gaussian">
            <mean>0.0</mean>
            <stddev>0.017</stddev>
          </noise>
        </y>
        <z>
          <noise type="gaussian">
            <mean>0.0</mean>
            <stddev>0.017</stddev>
          </noise>
        </z>
      </linear_acceleration>
    </imu>
    <plugin name="imu_controller" filename="libgazebo_ros_imu_sensor.so">
      <ros>
        <namespace>imu</namespace>
      </ros>
      <frame_name>imu_link</frame_name>
    </plugin>
  </sensor>
</gazebo>

GPS Sensors​

For outdoor robots, GPS sensors can be simulated:

<gazebo reference="gps_link">
  <sensor name="gps_sensor" type="gps">
    <always_on>true</always_on>
    <update_rate>4</update_rate>
    <plugin name="gps_controller" filename="libgazebo_ros_gps.so">
      <ros>
        <namespace>gps</namespace>
      </ros>
      <frame_name>gps_link</frame_name>
      <update_rate>4</update_rate>
      <fix_topic>fix</fix_topic>
      <gaussian_noise>0.1</gaussian_noise>
    </plugin>
  </sensor>
</gazebo>

Sensor Data Processing Pipeline​

Connecting Sensors to ROS​

To use sensor data in ROS, you need to connect the Gazebo sensors to ROS topics using the appropriate plugins. The plugins handle the conversion from Gazebo's data format to ROS message types.

Sensor Noise and Realism​

Real sensors have noise and limitations. Configure noise parameters to make simulation more realistic:

<sensor name="noisy_camera" type="camera">
  <camera>
    <noise>
      <type>gaussian</type>
      <mean>0.0</mean>
      <stddev>0.007</stddev>
    </noise>
  </camera>
</sensor>

Performance Optimization​

Level of Detail (LOD)​

For complex environments, use LOD to maintain performance:

<link name="complex_object">
  <visual name="high_detail">
    <geometry>
      <!-- Complex geometry -->
    </geometry>
    <material>
      <script>ComplexMaterial</script>
    </material>
  </visual>
  <collision name="simplified_collision">
    <geometry>
      <!-- Simplified geometry for collision -->
    </geometry>
  </collision>
</link>

Sensor Optimization​

  • Reduce sensor update rates when possible
  • Limit sensor ranges to necessary distances
  • Use appropriate resolution settings
  • Disable visualization when not needed for performance

Multi-Robot Simulation Considerations​

When simulating multiple robots:

  • Ensure unique namespaces for each robot's sensors
  • Consider computational load on the physics engine
  • Use appropriate world boundaries to prevent robots from interfering with each other unnecessarily

Hands-On Exercise​

  1. Create a robot model with at least 3 different sensors (LiDAR, camera, IMU) using the examples above
  2. Configure physics parameters for a wheeled robot with realistic friction
  3. Create a world with obstacles and run the simulation
  4. Subscribe to the sensor topics and visualize the data in RViz
  5. Experiment with different physics parameters to see how they affect robot behavior

Example ROS 2 command to check sensor topics:

# Check available topics
ros2 topic list | grep sensor

# Echo sensor data
ros2 topic echo /lidar/scan sensor_msgs/msg/LaserScan
ros2 topic echo /camera/image sensor_msgs/msg/Image
ros2 topic echo /imu/data sensor_msgs/msg/Imu

Troubleshooting Tips​

  • If simulation is unstable, try reducing the time step size
  • If sensors don't publish data, check plugin configuration and namespaces
  • If robot behaves unrealistically, verify inertial properties and friction coefficients
  • Monitor CPU usage and adjust parameters if simulation runs too slowly

Summary​

This lesson covered advanced physics configuration and sensor simulation in Gazebo. You learned how to implement realistic physics properties and integrate various sensors to create a comprehensive simulation environment. In the next lesson, we'll explore high-fidelity rendering and human-robot interaction in Unity.