Lab 01 - Introduction

Robotics II

Poznan University of Technology, Institute of Robotics and Machine Intelligence

Laboratory 1: Introduction to Formula Student Driverless Simulator

1. Formula Student Driverless Simulator

Formula Student is an international series of engineering competitions organized by Society of Automotive Engineers (SAE). The contest consists of static and dynamic events. Dynamic events include Skid Pad, Acceleration, Autocross, and Endurance.

Formula Student Driverless is a dedicated class of Formula Student racecars to autonomous vehicles. This type was launched in 2017 at Formula Student Germany. In Driverless competitions instead of Endurance events, Trackdrive is held. The figure below depicts Trackdrive track marking.

Formula Student Driverless Simulator is a community-based project whose goal was to provide an end-to-end simulation for FS Driverless teams. The simulator was also used in online competitions FS-Online 2020.

Docker image used during laboratories is based on: - Formula-Student-Driverless-Simulator - ROS noetic - NVIDIA docker image with Vulkan and CUDAGL

2. Requirements

2.1. Hardware

*  recommended system requirements:
    - 8 core 2.3Ghz CPU
    - 12 GB memory
    - 30GB free SSD storage
    - NVidia card with Vulkan support and 3 GB of memory

* testing machine:
    - OS: Ubuntu 20.04.1 LTS 64-bit
    - Intel® Core™ i5-8400 CPU @ 2.80GHz × 6
    - RAM 15.6 GiB
    - GeForce GTX 1060 6GB

* docker image size is about 9 GB

2.2. Software

docker >= 19.03
NVIDIA GPU - nvidia-docker2 section installation on Ubuntu

3. Installation

Important

Check the docker image is available on the registry using docker images command. If it is, skip to step 3.5.

Option 1 (suggested) - download docker image from docker hub

Download image from cloud or use the following command:

wget "https://chmura.put.poznan.pl/s/52jeTlBwcQY2bIG/download" -O fsds.tar

Load it with the following command:

docker load -i fsds.tar

Option 2 - build docker image from scratch

Clone repository

git clone https://github.com/PUTvision/RoboticsII-FSDS.git

Build docker image from docker directory

docker build -t fsds --network=host -f Dockerfile .

3.5 Run

Add docker access to Nvidia and display

NOTE: This step is required in every new terminal session if it needs to run GUI applications (like simulator, rviz, rqt_plot).

xhost + local:root

Run docker image

NOTE: It is required to change <STUDENT ID> to your student ID.

docker run \
    -it --gpus all --privileged \
    --env="DISPLAY=$DISPLAY" \
    --env="QT_X11_NO_MITSHM=1" \
    --env="NVIDIA_DISABLE_REQUIRE=1" \
    --volume="/tmp/.X11-unix:/tmp/.X11-unix:rw" \
    --env="XAUTHORITY=$XAUTH" \
    --volume="$XAUTH:$XAUTH" \
    --env="NVIDIA_VISIBLE_DEVICES=all" \
    --env="NVIDIA_DRIVER_CAPABILITIES=all" \
    --network=host \
    --name=<STUDENT ID> \
    fsds

Get into the running container from another terminal

docker exec -it <CONTAINER ID> bash

4. Simulator usage

Simulator

Start simulation calling:

./FSDS.sh -- nosound headless

Robot Operating System

start bridge between FSDS and ROS:

NOTE: this activates the autonomous state and disables keyboard control
```
roslaunch fsds_ros_bridge fsds_ros_bridge.launch
```
start rviz with cameras and lidar:
bash roslaunch fsds_ros_bridge rviz.launch
visualize sensors plots: bash roslaunch fsds_ros_bridge plot.launch
control racecar using gamepad:

NOTE: This is not required for the laboratory. It is only for testing purposes.
```
roslaunch joystick joystick.launch
```

5. Prerecorded ROSbags files

NOTE: Rosbags are data files recorded by ROS. They are used to record and play back data from topics. More information about rosbags can be found here.

Recorded rosbag files with test runs on the race track are available here.

6. Tasks

Run container and check if everything works correctly.
Start the simulator, check available maps, and move the racecar using a keyboard.
Connect ROS with simulator using fsds_ros_bridge package and check available topics and services. Then compare the list with documentation.
Using sine_steering.py script template from fsds_roboticsII ROS package (Formula-Student-Driverless-Simulator/ros/src/RoboticsII-FSDS directory) implement steering method based on sine signal.

Build ROS environment calling catkin build from Formula-Student-Driverless-Simulator/ros folder.
Source environment (source devel/setup.bash).
Make script executive using chmod +x sine_steering.py command if.
Test your control algorithm on the acceleration map with the command roslaunch fsds_roboticsII sine_steering.launch and try to move between yellow and blue cones.

TASK

As a laboratory outcome take and add to the eKursy platform two screenshots of:

simulator acceleration track running
Rviz viewer with Lidar and Image messages added