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SOAR LinoRobot F4stB0is Practice!

Authors:

Credits to the original Linorobot repository!

linorobot

Table of Contents

Objectives and Deliverables

Come back here if you do not know what has to be done!

Target Deadline: 18 July 2019, 7pm

Objectives

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  • Become acquainted with Ubuntu and ROS
  • Learn how to get a basic ROS robot up and running

Deliverables

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Some of these have to be demonstrable by every individual in the group - so help each other out and make sure everyone crosses the finish line as a team!

  • Build the Linorobot (Team)
  • Set-up the Linorobot (Team)
  • Able to start-up the Linorobot to perform basic SLAM and Teleoperation (Individual)
  • Able to write a basic Publisher-Subscriber Node Package (Individual)

Extra Objectives

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If you are truly a f4stb0i and finished the previous tasks, here are some other tasks you can tackle to improve your skills further!

  • Write a ROS Service and ROS Action Node Package
  • Complete all ROS package tasks in both ROS Python and ROS C++
  • Get Linorobot to run in Gazebo simulator
  • Use the Laser filtering package to refine the LIDAR output from the Linorobot

Preparation

Before jumping in - remember to complete these things!

Software Pre-requisites

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Skill Pre-requisites

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Project Setup [Ubuntu 18.04 with Melodic ROS]

Please ensure that the '$' is not copied when copy-pasting codes. #duh

ROS (Your Computer):

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  1. Installation

    $ sudo sh -c 'echo "deb http://packages.ros.org/ros/ubuntu $(lsb_release -sc) main" > /etc/apt/sources.list.d/ros-latest.list'
$ sudo apt-key adv --keyserver 'hkp://keyserver.ubuntu.com:80' --recv-key C1CF6E31E6BADE8868B172B4F42ED6FBAB17C654
$ sudo apt update
$ sudo apt install ros-melodic-desktop-full
$ sudo rosdep init
$ rosdep update

  2. To add ROS environment automatically a new terminal session is started. (optional)

    $ echo "source /opt/ros/melodic/setup.bash" >> ~/.bashrc
$ source ~/.bashrc

  3. Create a ROS Workspace. (We are using catkin because rosbuild is no longer maintained.)

    $ mkdir -p ~/catkin_ws/src
$ cd ~/catkin_ws/
$ catkin_make

    Running catkin_make the first time in your workspace, it will create a CMakeLists.txt link in your src folder. Additionally, if you look in your current directory you should now have a build and devel folder. Inside the devel folder you can see that there are now several setup.bash files. Sourcing any of these files will overlay this workspace on top of your environment.

  4. Source your new setup.bash file:

    $ source devel/setup.bash

  5. To make sure your workspace is properly overlayed by the setup script, make sure ROS_PACKAGE_PATH environment variable includes the directory you're in.

    $ echo $ROS_PACKAGE_PATH
/home/youruser/catkin_ws/src:/opt/ros/kinetic/share

  6. Congratulations! You have setup your workspace! Remember to source devel/setup.bash in your workspace folder everytime you want to work on your workspace. Happy Coding!

ROS (Raspberry Pi):

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Plug your keyboard and monitor to your raspberry pi before continuing.

  1. Connect the Rasp Pi to a power supply and wait for it to turn on.

  2. Install Ubuntu Mate.

  3. Install ROS. (We are using methylDragon's script.)

  4. SSH-ing to the bot requires several steps. Before everything, ensure that the bot is currently powered on, with the RPi connected to the same WiFi connection as your computer.

    Firstly, if you haven't done so, setup the SSH on the RPi by inputting these commands:

    # Install SSH
$ sudo apt install openssh-server

# Enable SSH
$ sudo systemctl enable ssh

# Finally, activate the SSH service
$ sudo systemctl start ssh

# Ensure that the firewall allows port 22
$ sudo ufw allow 22

# Checking SSH status
$ systemctl is-enabled ssh
$ systemctl is-active ssh

# If you want to stop SSH
$ sudo systemctl stop ssh
$ sudo systemctl disable ssh
$ sudo ufw deny 22

  5. Next, we need to setup the network on the RPi. Go to your WiFi Settings and set a static IP. If you are using SUTD-Wifi, you can do this by going to the IPv4 tab, setting the Method to Manual and inputting the required information. To make sure that it is working properly, first input these commands:

    # IP Address and Netmask
# SUTD Wifi: 10.12.XXX.XXX and 255.255.0.0
$ ip addr show
$ ifconfig

# Gateway
# SUTD Wifi: 10.12.0.1
$ ip route | grep default

  6. Use the same Netmask and Gateway addresses, but not the same IP Address (choose a random value for XXX.XXX). You can use 8.8.8.8 for DNS. After setting these up, reconnect to the WiFi.

    Finally, generate new SSH keys by inputting these commands:

    # Deleting any obselete, conflicting keys
$ sudo rm -f /etc/ssh/ssh_host_*

# Generate new keys
$ sudo dpkg-reconfigure openssh-server

Setting up LinoRobot

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Once you have installed ROS on both computers, install Linorobot's packages and dependencies.

SSH to your robot's computer and clone the install package:

$ cd ~
$ git clone https://github.com/linorobot/lino_install
$ cd lino_install

For Raspberry Pi, it is recommended to have a swap file to prevent the initial build from failing. You can disable this once the installation is done.

$ sudo apt-get install dphys-swapfile

Run the install file where:

base = 2wd, 4wd, ackermann, mecanum

sensor = xv11, rplidar, ydlidar, hokuyo, kinect, realsense

# For our case it's gonna be ./install 2wd rplidar
$ ./install base sensor

On your development computer, install the packages you need for configuration and visualization:

$ cd ~/<your workspace folder/src
$ git clone https://github.com/linorobot/lino_pid.git
$ git clone https://github.com/linorobot/lino_msgs.git
$ git clone https://github.com/linorobot/lino_visualize.git
$ sudo apt-get install ros-$(rosversion -d)-teleop-twist-keyboard
$ cd .. && catkin_make

To allow ROS to communicate between the development computer (ROS Master) and the slave robot(s), we need to set up the environment variables properly.

For the .bashrc file on development computer, make sure that it has these lines (if not, then add them):

export ROS_HOSTNAME=[DEVELOPMENT_COMPUTER_IP_ADDRESS]
export ROS_MASTER_URI=http://localhost:11311 # The name 'localhost' is already defined by default as an alias for 127.0.0.1, which is NOT the development computer's IP Address

For the .bashrc file on the slave robot(s), make sure that it has these lines (if not, then add them):

export ROS_HOSTNAME=[LINOROBOT_STATIC_IP_ADDRESS]
export ROS_MASTER_URI=http://[DEVELOPMENT_COMPUTER_IP_ADDRESS]:11311

Don't forget to source them after editing them.

$ source ~/.bashrc

(NOTE: Alternatively, we can define the IP Addresses in the respective /etc/hosts file on each system with aliases, which we can refer to in the respective .bashrc file.)

Setup Teensy

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Since we are using Ubuntu MATE 18.04, we need to apply some fixes and install some missing packages (while ROS Melodic is officially supported, we need to manually fix some issues.)

  1. Download the python-gudev package from here. Install it by using dpkg.

    $ sudo dpkg -i <filepath>.deb
$ sudo apt install -f

  2. Install pip.

    $ sudo apt install python-pip

  3. Install platformio.

    $ sudo pip install platformio

  4. Compile firmware and upload it to Teensy.

    $ platformio run ---target upload

  5. Install the interactive-markers ROS package.

    $ sudo apt install ros-melodic-interactive-markers

    Alternatively, we can use this command to magically install most of the missing dependencies for the Linorobot workspace:

    $ rosdep install --from-paths src --ignore-src -r -y

  6. Check if we need to skip/modify the if check in the .cpp code. (For our case, we need to edit the MPU9250.cpp file at lines 74-81. This might be due to us using a slightly different IMU model.)

    bool MPU9250::testConnection() {
uint8_t device_id = getDeviceID();

if(device_id == 0x38 || device_id == 0x71)
    return true;
else 
    return false;
}

  7. Install the slam-gmapping package. Since it is not yet available for ROS Melodic, build it from source.

    $ git clone https://github.com/ros-perception/openslam_gmapping.git

  8. Remove all slashes (/) from global_costmap_params.yaml and local_costmap_params.yaml. This is because we are using tf2 (instead of tf).

  9. Install the global-planner package on RPi.

    $ sudo apt install ros-melodic-global-planner

# Alternative method (it will install global-planner, which is a dependency)
$ sudo apt install ros-melodic-navigation

  10. Install the libyaml-cpp-dev package on RPi. (This is optional, just in case if the package is not yet installed for ROS Melodic.)

    $ sudo apt install libyaml-cpp-dev

SSH Login Guide

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Login credentials of LinoRobot's Raspberry Pi Ubuntu MATE 18.04:

hostname: sl0wboisTT
username: [REDACTED]
password: [REDACTED]

You can ssh from your computer to the RPi by using this command:

$ ssh username@[LINOROBOT_STATIC_IP_ADDRESS]

SSH password is the same as the one in the aforementioned login credentials.

You can run ROS Master on the development computer using the roscore command. You can try publishing something from the development computer and try subscribing to it from the Linorobot to ensure that it works.

A simple example would be to run:

$ rosrun roscpp_tutorials talker

on the development computer, and run:

$ rostopic echo /chatter

on the Linorobot.

Calibration

Header Constants (for Odometry)

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For Odometry, we need the position (or displacement), linear velocity and angular velocity to be approximately of the correct magnitude and direction. It needs to pass the stationary rotation test and the square-shaped pattern origin test as well. It's fine if the loop didn't close as AMCL will correct the robot's pose eventually. Edit the lino_base_config.h to define your Robot Base, Motor Driver, IMU and Robot's Specification:

//uncomment the base you're building
#define LINO_BASE DIFFERENTIAL_DRIVE // 2WD and Tracked robot w/ 2 motors
// #define LINO_BASE SKID_STEER      // 4WD robot
// #define LINO_BASE ACKERMANN       // Car-like steering robot w/ 2 motors
// #define LINO_BASE ACKERMANN1      // Car-like steering robot w/ 1 motor
// #define LINO_BASE MECANUM         // Mecanum drive robot

//uncomment the motor driver you're using
#define USE_L298_DRIVER
// #define USE_BTS7960_DRIVER
// #define USE_ESC

//uncomment the IMU you're using
// #define USE_GY85_IMU
// #define USE_MP6050_IMU
// #define USE_MPU9150_IMU
#define USE_MPU9250_IMU

//define your robot's specs here
#define MAX_RPM 3000              // motor's maximum RPM
#define COUNTS_PER_REV 2200       // wheel encoder's no of ticks per rev
#define WHEEL_DIAMETER 0.0678     // wheel's diameter in meters
#define PWM_BITS 8                // PWM Resolution of the microcontroller
#define LR_WHEELS_DISTANCE 0.215  // distance between left and right wheels
#define FR_WHEELS_DISTANCE 0.30   // distance between front and rear wheels. Ignore this if you're on 2WD/ACKERMANN
#define MAX_STEERING_ANGLE 0.415  // max steering angle. This only applies to Ackermann steering

IMU

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Open 2 terminals and run:

$ roslaunch linorobot minimal.launch

$ roscd linorobot/param/imu
$ mv imu_calib.yaml imu_calib0.yaml 
$ rosrun imu_calib do_calib

Follow the on-screen instructions to calibrate the IMU. Once done, verify that the calibration file has been saved.

$ ls imu_calib.yaml

PID

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On your robot's computer, run:

$ roslaunch linorobot minimal.launch

On your development computer, open 3 new terminal consoles and run:

$ rosrun lino_pid pid_configure

$ rosrun teleop_twist_keyboard teleop_twist_keyboard.py

$ rqt

In rqt, import the ~/catkin_ws/src/lino_pid/lino_pid.perspective file. Click pid_configure under Dynamic Reconfigure and slide the parameters to tune the PID constants.

PID Tips:

  1. Start with 0 for all.
  2. Increase P slowly until the motors start to vibrate like crazy, then drop the P value back to just before it starts to vibrate.
  3. When you start driving it, it should have some dumb oscillations when you try to turn or start stop, so increase D slowly until it's reduced.
  4. Increase I if needed when you realize that it has steady state errors.

Once the PID constants are tuned, record the values in the lino_base_config.h file:

#define DEBUG 0 // Disable debug mode

#define K_P 1.4 // P constant
#define K_I 1.2 // I constant
#define K_D 0.5 // D constant

LIDAR TF

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If you have a low power source, LIDAR should be connected before turning on the RPi. A power surge might happen when starting anything LIDAR-related. Reboots might help. It might seem simple, but without enough power, the LIDAR would not be able to turn on when launching bringup. This is a common pain point during testing.

Firstly, configure your robot base's TF by running:

$ roscd linorobot/launch
$ nano bringup.launch

Measure the distance between the floor and the center of the robot base. Define the measured distance on the third value found in "args" param.

<node pkg="tf2_ros" type="static_transform_publisher" name="base_footprint_to_base_link" args="0 0 0.05 0 0 0  /base_footprint /base_link"/>

Secondly, configure your laser's TF by running:

$ roscd linorobot/launch/include
$ nano laser.launch

Determine the LIDAR's location from the center of the base and change the first three numbers in "args" to your measured offsets.

<node pkg="tf2_ros" type="static_transform_publisher" name="base_link_to_laser"
args="-0.05 0 0.102 0 0 0  /base_link /laser"/>

To check the laser's TF, open 2 new terminal consoles on the robot's computer and run:

$ roslaunch linorobot bringup.launch

$ roslaunch linorobot laser.launch

On your development computer, run:

$ roscd lino_visualize/rviz
$ rviz -d laser.rviz

Place the robot 1 m away from a wall. Check on RViz if the wall detected is approximately 1 m. Each square in RViz is 1 square meter.

SLAM and Autonomous Navigation

SLAM

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On the robot's computer, open 2 new terminal consoles and run both commands:

$ roslaunch linorobot bringup.launch

$ roslaunch linorobot slam.launch

On your development computer, open 2 new terminal consoles and run both commands:

$ rosrun teleop_twist_keyboard teleop_twist_keyboard.py

$ roscd lino_visualize/rviz
$ rviz -d slam.rviz

Start mapping from a location where there are a lot of features. Try not to bump into anything while mapping. Whenever the m_count function is called, stop the Linorobot immediately (no translation or rotation whatsoever) in order to prevent inaccurate mapping (failed scan matching with odometry). Occassional autocorrection is possible, but do not rely on it. Monitor the average scan matching score in the console (maintain it at preferably above 100). Tip: use linear speed of about 0.15 and angular speed of about 0.5.

After mapping is done, save the map by running this command on the robot's computer:

$ rosrun map_server map_saver -f ~/linorobot_ws/src/linorobot/maps/map

Check if the map has been saved:

$ roscd linorobot/maps
$ ls -a map.pgm map.yaml

Autonomous Navigation

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Edit costmap_common_params.yaml:

$ roscd linorobot/param/navigation
$ nano costmap_common_params.yaml

and define the robot's footprint (size):

# x = ROBOT_LENGTH / 2
# y = ROBOT_WIDTH / 2
footprint: [[-x, -y], [-x, y], [x, y], [x, -y]]

Before navigation, ensure that line 3 of the navigate.launch file is:

# map.yaml instead of house.yaml
<arg name="map_file" default="$(find linorobot)/maps/map.yaml"/>

On the robot’s computer, open 2 new terminal consoles and run these 2 commands:

$ roslaunch linorobot bringup.launch

$ roslaunch linorobot navigate.launch

terminal

On your development computer, run:

$ roscd lino_visualize/rviz
$ rviz -d navigate.rviz

Finally, use "2D Post Estimate" to set the robot's initial pose and use "2D Nav Goal" to send a goal to the robot on RViz.

rviz

Yay! We're done!

Components

Controller/Driver

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Sensors

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Additional Resources

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tags: Documentation

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SOAR Linorobot 2019 Journal

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