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Duckietown Baseline

✎

Modified 2021-10-30 by liampaull

This section describes the basic procedure for making a submission using the Robot Operating System and the Duckietown software stack.

That you have made a submission with the ROS template and you understand how it works.

You already know something about ROS.

You have a competitive submission.

AI-DO 5 (NeurIPS 2020) - Second Webinar — ROS template

Quickstart

✎

Modified 2021-11-03 by tanij

Clone this repo

$ git clone git@github.com:duckietown/challenge-aido_LF-baseline-duckietown.git

Change into the directory:

$ cd challenge-aido_LF-baseline-duckietown

Test the submission, either locally with:

$ dts challenges evaluate --challenge CHALLENGE_NAME

or make an official submission when you are ready with

$ dts challenges submit --challenge CHALLENGE_NAME

You can find the list of challenges here. Make sure that it is marked as “Open”.

Baseline Details

✎

Modified 2020-11-11 by Liam Paull

The “Duckietown” baseline is based on the ROS template.

Dockerfile

✎

Modified 2021-11-03 by tanij

One important fact of the Dockerfile is that we use a “multi-stage build”:

FROM ${DOCKER_REGISTRY}/duckietown/dt-car-interface:${BASE_TAG} AS dt-car-interface

FROM ${DOCKER_REGISTRY}/duckietown/challenge-aido_lf-template-ros:${BASE_TAG} AS template

FROM ${DOCKER_REGISTRY}/duckietown/dt-core:${BASE_TAG} AS base

This allows us to take some elements from each of the first two base images, and copy them into the dt-core image:

COPY --from=dt-car-interface ${CATKIN_WS_DIR}/src/dt-car-interface ${CATKIN_WS_DIR}/src/dt-car-interface
COPY --from=template /data/config /data/config
COPY --from=template /code/rosagent.py .

As a result, we have the calibration files (from /data/config) as well as the rosagent.py from the challenge-aido_lf-template-ros and all the source files from the dt-car-interface image.

We also get everything that is in the dt-core image.

The remainder of the Dockerfile is very similar to the Dockerfile in the ROS template.

`solution.py`

✎

Modified 2021-11-03 by tanij

There is no solution.py because it is inherited from the ROS template. In the event that you wanted to, for example, change the launcher that was run in the final CMD line.

`launchers/`

✎

Modified 2021-10-30 by liampaull

There is only one “launcher”, and it deviates slightly from the one in the ROS template:

#!/bin/bash

source /environment.sh

source /opt/ros/noetic/setup.bash
source /code/catkin_ws/devel/setup.bash --extend
source /code/solution/devel/setup.bash --extend
source /code/submission_ws/devel/setup.bash --extend

set -eux

dt-exec-BG roscore

dt-exec-BG roslaunch --wait car_interface all.launch veh:="${VEHICLE_NAME}"
dt-exec-BG roslaunch --wait duckietown_demos lane_following.launch

sleep 5 # for some reason we still need this so that nodes can startup
dt-exec-BG roslaunch --wait duckietown_demos set_state.launch veh:="${VEHICLE_NAME}" state:="LANE_FOLLOWING"

rostopic list
# foreground
dt-exec-FG roslaunch --wait agent agent_node.launch || true
rostopic list
copy-ros-logs

Here we launch the lane_following.launch launch file from the duckietown_demos package. We don’t go into the intricate details of everything that is run in this launch file here, but some of the more consequential nodes which are getting launched are the following:

line_detector_node: Used to detect the lines in the image.
ground_projection_node: Used to project the lines onto the ground plane using the camera extrinsic calibration.
lane_filter_node: Used to take the ground projected line segments and estimate the Duckiebot’s position and orientation in the lane.
lane_controller_node: Used to take the estimate of the robot and generate a reference linear and angular velocities for the Duckiebot.

In the event that you wanted to, for example change the launcher that was run in the final CMD line.

`submission_ws/`

✎

Modified 2021-11-03 by tanij

The submission_ws folder contains all the new ROS packages that you would like to include in your submission. It is currently empty, but there is a reference package included in the ROS template.

Importantly, your submissions_ws is sourced after the existing catkin_ws that is included in dt-core. As a result, if you include a node and package in your submission_ws with the same name as one in dt-core, the one in submission_ws will get executed. This is convenient because it means that, as long as you adhere to the same subscriptions and publications, you don’t need to define any new launch file, lane_following.launch will automatically launch your newly written node.

Local Development Workflow

✎

Modified 2021-10-31 by tanij

For rapid local development, you can make use of the dts exercises API (unknown ref opmanual_duckiebot/running-exercises)

warning (17 of 18) index

warning

I will ignore this because it is an external link. 

 > I do not know what is indicated by the link '#opmanual_duckiebot/running-exercises'.

Location not known more precisely.

Created by function n/a in module n/a.

, developed to build and test exercises and assignments in class settings.

Building your Code

✎

Modified 2020-11-13 by Liam Paull

From inside the challenge-aido_LF-baseline-duckietown folder, you can start by building your code with:

$ dts exercises build

This performs catkin build inside a docker container. If you go inside the submission_ws folder you will notice that there are more folders that weren’t there before. These are build artifacts that persist from the building procedure because of mounting.

Running in Simulation

✎

Modified 2021-11-03 by tanij

You can run your current solution in the gym simulator with:

 $ dts exercises test --sim

Then you can look at what’s happening by looking through the browser at http://localhost:8087. This will open a noVNC desktop. In it, open up the rqt_image_view, resize it, and choose /agent/camera_node/image/compressed in the dropdown. You should see the image from the robot in the simulator.

You might want to launch a virtual joystick by opening a terminal and doing:

$ dt-launcher-joystick

By default the Duckiebot is in joystick control mode, so you can freely drive it around. You can also set it to LANE FOLLOWING mode by pushing the a button when you have the virtual joystick active. If you do so you will see the robot move forward slowly and never turn.

At the same time, you can see a birds eye overview of the Duckiebot on the track though the browser at http://localhost:8090.

Testing Your Algorithm on the Robot

✎

Modified 2021-11-03 by tanij

If you are using a Linux laptop, you have two options, local (i.e., on your laptop) and remote (i.e., on the Duckiebot). To run “locally”

 $ dts exercises test --duckiebot_name ROBOT_NAME --local

To run on the Duckiebot:

$ dts exercises test --duckiebot_name ROBOT_NAME

In both cases you should still be able to look at things through noVNC by pointing your browser to http://localhost:8087 . If you are running on Linux, you can load up the virtual joystick and start lane following as above.

If you are Mac user unfortunately you should not use the --local flag

Starting Lane Following on Mac

✎

TODO: should be retested

task (2 of 2) index

task

The following was marked as "todo".

TODO: should be retested

Location not known more precisely.

Created by function n/a in module n/a.

Since we can’t publish from Mac and have it be received by ROS, we have to do something slightly different. In a new terminal on your Mac do:

$ docker -H ROBOT_NAME.local exec agent launchers/start_lane_following.sh

This will run the start_lane_following.sh bash script inside the agent container which initiates LANE_FOLLOWING mode.

Similarly, you can stop your Duckiebot from lane following by doing:

$ docker -H ROBOT_NAME.local exec agent launchers/stop_lane_following.sh

You could also do an equivalent thing through the Portainer interface in the dashboard by opening a new terminal in your agent container and running the corresponding launcher.

How to Improve your Submission

✎

Modified 2020-11-11 by Liam Paull

A good way to get started could be to copy one of the packages defined in the Duckietown dt-core repo or the Duckietown dt-car-interface repo into the submission_ws folder and modify it. Note that your modified package will automatically get run because of the order of the sourcing of the catkin workspaces in the run_and_start.sh launch file.

If you would like to add a new package and node that includes a functionality not already run by lane_following.launch or you would like to change the connectivity of interfaces of these nodes, then you will also need:

to write your own launch file that launches your node and also all of the other nodes from the base images that you would still like to use.
to modify the launch file run_and_start.sh so that it launches your newly created launchfile. You could equally define a new launchfile, but then make sure that it gets executed in the last line of your Dockerfile.

Other Possibly Useful Utilities

✎

Modified 2021-11-03 by tanij

All of the normal ROS debugging utilities are available to you through the noVNC desktop. For example, You might also explore the other outputs that you can look at in rqt_image_view.

Also useful are some debugging outputs that are published and visualized in RViz. You can open RViz through the terminal in the noVNC desktop by typing:

$ rviz

In the window that opens click “Add” the switch to the topic tab, then find the segment_markers, and you should see the projected segments appear. Do the same for the pose_markers.

Another tool that may be useful is rqt_plot which also can be opened through the terminal in noVNC. This opens a window where you can add “Topics” in the text box at the top left and then you will see the data get plotted live.

All of this data can be viewed as data through the command line also. Take a look at all of the rostopic command line utilities.