- How does the Robot “know” where it is?
- How does it determine it’s “pose”?
- Relative to a coordiante system
- Could be GPS coordinates
- But more likely coordiantes of a given map
Localizing the Robot on a map
- Key package is called
amcl - Adaptive Monte Carlo Localization
- It’s a deep theoretical area covered in Probabilistic Robotics by Sebsastian Thrun, et al
How it works at a high level
- Location of robot is its pose
- Always known with respect to a map coordinate frame (also called sometimes the world coordinate frame)
amcl maintains a set of candidate poses plus a probability that they reflect reality
- As robot moves, actual sensor readings are compared with expected sensor readings for each pose, and the probability of each candidate pose can be updated.
- Low probability poses are discarded, and high probability poses are updated based on odometry, scan, and probability.
- For path planning purposes the highest probability pose is used.
- It is definitely a guess not a certainty.
- We will rebuild the map here to make sure things are consistent
- A new wrinkle here is the use of turtlebot3_simulation which will take the place of teleop
- That node drives the robot around ranomly while gmapping is building the map
- After some time has gone by, save the map.
NB Be careful with the filenames of the map. You will get strange errors if the file name given to turtlebot3_navigation is incorrect or not resolvable!
$ roslaunch turtlebot3_gazebo turtlebot3_stage_4.launch
$ roslaunch turtlebot3_gazebo turtlebot3_simulation.launch
$ roslaunch turtlebot3_slam turtlebot3_slam.launch slam_methods:=gmapping
$ cd ~
$ rosrun map_server map_saver -f stage4
- The ROS nav stack is another complex bit of computer science and engineering
- For now we are going to just scratch the surface
- Inputs are a map, an estiamted current position, scanner inforamtion, and a destinaion
- Behavior is to generate a path and steer the robot to it
- Avoiding obstacles
- navigation goal is sent to the nav stack. This is done with an action call with a goal of MoveBaseGoal which specifies a target pose and a coordinate frame (called the map frame.)
- Nav stack uses a path-planning algorithm in the global plannner to create shortest path route
- Local planner drives along that path, while using sensor information to aboid obstacles.
- When the robot arrives at the goal pose the action terminates.
- Lets try it!
# Now, close all the exiting ROS nodes down and next run this. Be careful with the
# file names because the yaml file contains a file name too and it is easy to
# get things misaligned.
$ roslaunch turtlebot3_navigation turtlebot3_navigation.launch map_file:=stage4.yaml
- Give the robot a navigation goal by clicking the button in rviz
- Play around and see the robot solve the simple maze navigation including places it can’t ‘see’ from where it is
- Uncheck everything except RobotModel, Map and ParticleCloud in rViz.
- The green arrows are the pose estimates from
amcl that the robot is somewhere else, and you see it do its best on guessing the pose
- Do this with the 2d pose estimate command
- Turn on the Lasert Scan display
- Play with the 2d pose estimate command and observe how the map becomes aligned
Behind the scenes
- amcl subscribes to a topic
- rviz command 2d pose estimate publishes the new proposed pose on that topic
- When amcl receives that message it resets its collection of candidate poses
Going inside the Nav Stack
- Global planner: works out best path assuming map is accurate
global costmap: How safe or unsafe is each spot on the map
- Published on /move_base/global_costmap/costmap
- Planner view shows what the planned path to the nav goal is
- Local Planner:
- Adjusts the global plan based on newly detected obstacles
- Map shows square area around robot with further analysis
- Color shows safe areas in cold colors (like blue) and dangerous areas in warm colors (like red)
Navigating in code
- Look at patrol.py
- It is a
SimpleActionClient which sends a repeating sequence of two Action Goals to
- There’s nothing tricky about itself.
- The challenge might be getting all the other bits set up so that it will work correctly
- How robots move around the world
- Building maps
- Using maps
move_base takes action goals to move to somewhere else
- Importance of coordinate systems
- Some more references: