Exercise 3 - UNFINISHED¶

In this exercise you will use our MDP/LTL planning framework to encode an object search task in a simple environment. This example connects many of the different elements of our robot system, and may seem complex at first. If something is unclear, or you want more information, please just ask.

Background¶

You must first run some basic elements from the STRANDS system. You should ideally run each of these in a separate terminal where you have sourced both the ROS and your local workspace setup.bash files, as described in tutorial_prep.md.

MongoDB Store¶

(If you still have the database running from Exercise 1 you can skip this step)

First, check the db directory exists (which you should’ve created following tutorial_prep.md). The following should not list any files or report an error:

ls `rospack find planning_tutorial`/db

If that is ok, then launch mongodb_store using that directory as its data path:

roslaunch mongodb_store mongodb_store.launch db_path:=`rospack find planning_tutorial`/db

MORSE Simulation¶

In another terminal, launch our object simulation taken from the ALOOF Project.

roslaunch strands_morse aloof_morse.launch

If you press the ‘h’ key in MORSE you can see a list of available keyboard commands.

2D and Topological Navigation¶

We have predefined a simple topological map for you to use in this tutorial. The first time (and only the first time!) you want to use topological navigation in the ALOOF simulation, you must add this map to the mongodb store. Do it with the following command:

rosrun topological_utils load_yaml_map.py `rospack find strands_morse`/aloof/maps/aloof_top_map.yaml

You can check the result of this with the following command which should print a description of the topological map aloof.

rosrun topological_utils list_maps

If this was successful you can launch the 2D (amcl and move_base) and topological localisation and navigation for your simulated robot. Note that in this configuration, the statistics for edge transitions in the topological map will be learnt from experience (as opposed to manually specified as in Exercise 1).

roslaunch strands_morse aloof_navigation.launch

To see everything running, launch the ROS visualisation tool rviz with the provided config file:

rviz -d `rospack find planning_tutorial`/plan_tut.rviz

You’ll find that the robot is not localised at the correct place compared to the simulation. Therefore use the 2D Pose Estimate button and click (then hold and rotate to set angle) on the correct part of the map.

If you click on a green arrow in a topological node, the robot should start working its way there. Feel free to add whatever extra visualisation parts you want to this (or ask us what the various bits are if you’re new to robotics).

MDP Planning¶

Next launch the MDP-based task executive system in (yet another!) new terminal:

roslaunch mdp_plan_exec mdp_plan_exec_extended.launch

Semantic Map¶

Our object search framework makes use of a semantic map of the environment to know where to look for objects. There is a predefined map in this repository. Before you run the semantic mapping launch file for the first time, load the predefined map into mongodb with the following command.

mkdir ~/.semanticMap
mongorestore --port 62345 `rospack find planning_tutorial`/maps/soma_dump

If this was successful, you can launch the semantic map nodes with the following command:

roslaunch planning_tutorial aloof_semantic_map.launch

After you’ve done this you should see some blue and yellow regions appear in RViz.

Exercise 2a¶

In Exercise 1 you exploited the fact that the execution framework automatically creates an MDP for navigation across the topological map. In this exercise we will extend this MDP with additional actions which connect ROS actionlib servers to actions in the MDP.

In order for the robot to search for objects, it first needs to execute a meta-room sweep in each room where it may need to look. This allows it to build a 3D map of each room for reasoning about supporting surfaces and views.

We connect the

Original page: https://github.com/strands-project/planning_tutorial/blob/indigo-devel/doc/exercise_3.md