Implementing autonomous navigation for the Unitree Go2 EDU quadruped robot, utilizing Simultaneous Localization and Mapping (SLAM) to travel from a specified starting location to any designated endpoint.
- Operating System: Ubuntu 22.04
- ROS 2 Distribution: Humble
- Simulator: Gazebo/Ignition for physics-based quadruped simulation
- SLAM: SLAM Toolbox for mapping and localization
- Navigation: Nav2 for global planning, local planning, and behavior execution
- Robot Interface: Unitree Go2 SDK2 or community ROS 2 driver
Obtain and verify the Unitree Go2 URDF model with proper joint configurations and sensor frames (map, odom, base_link, imu_link, lidar_link). Test the model in RViz and validate the TF tree structure.
Import the Go2 URDF into Gazebo with required physics and sensor plugins (LiDAR, IMU, optional depth camera). Create a test world with obstacles for navigation testing.
Establish correct transform relationships: odom → base_link from robot odometry, base_link → lidar_link as fixed transform, and map → odom from SLAM. Ensure stable frame structure for navigation.
Configure SLAM Toolbox with appropriate robot frames and sensor inputs. Enable online SLAM mode and verify data reception and TF stability.
Drive the simulated robot to generate a complete 2D map of the environment. Save map files and pose graphs for navigation use.
Configure Nav2 with the saved map, localization method, global and local costmaps, robot footprint, planners, and local controller. Set up Behavior Tree Navigator for autonomous navigation tasks.
Connect Nav2 motion commands to the Go2 control interface through the ROS 2 driver, ensuring proper translation of navigation commands to robot locomotion (gait and velocity control).
Launch complete navigation pipeline in Gazebo: set initial pose, send navigation goals, and verify global path planning, dynamic obstacle avoidance, and smooth local control.