Here’s where we demonstrate how MOV.AI’s autonomous navigation flow enables a robot to know where it is on the map, know the destination to which it has to travel (goal) and to autonomously navigate the path to reach that point, as well as to handle dynamic obstacles (such as people walking by) along the way.
The following shows the Husky robot navigating in Gazebo Fortress and in RViz –
This demonstration also shows the usage of RViz, which is a 3D visualization tool for ROS applications that provides a view of your robot model and the sensor information that is captured by the robot’s sensors, as well as the Ignition Gazebo.
A Gazebo Fortress map and an RViz map are provided in which you can see the robot leaving the charging station, traveling to the pickup station, picking up the cart, traveling to the drop off station, leaving the cart there and then going back to its charging station.
As the robot travels from point to point, the relevant node flashes a green dot in the flow. For example, when the robot is going to point C, the green dot flashes on the go to C node.
This lesson demonstrates how MOV.AI’s autonomous navigation flow enables a robot to know where it is on the map, know the destination to which it has to travel (goal) and autonomously determine the path to reach that point, as well as to handle dynamic obstacles (such as people walking by) along the way.
The flow in this lesson demonstrates the localization that is enabled by the map that was saved when the robot scoped out its environment, as described in the Mapping the Environment. During the installation of MOV.AI, two ready-made maps were provided that will be used in this lesson – the map of the warehouse environment, as well as the localization map created in the previous demo.
This lesson is accompanied by a flow that we prepared for you named
tugbot_autonomous_navigation. During this lesson, you’ll open that flow and watch how the flow guides the robot in the simulator through this global localization/map-building process.
This flow demonstrates the autonomous navigation of both a Tugbot and a Husky robot. The depot map is used combined with ROS
ekf nodes for localization. The ROS
move_base node is also used combined with the
global planner and
teb local planner, as well as
global costmap and
local costmap to navigate the robot. In the flow, the
go_to node is used to send a goal (destination) to
move_base. After being localized, the robot will move to a preset location on the map, which can be configured using the parameters of the
Updated about 1 year ago