A ROS based robot for autonomous lawn mowing.

• Arunava Basu
• Aditi Ramadwar

With the sprawl of the urbanized communities, today's houses have large lawns that demand constant upkeep. Such a manual an repetitive task is not only laborious but also taxing and truly unnecessary. Robot are great at performing complex repetitive tasks autonomously, and as this this is a great problem that can be solved using autonomous systems.

We propose to implement a robust robotic system in Gazebo and ROS inspired by an autonomous lawn mower called “Automower” which is capable of mowing a desired lawn area, following a predefined pattern to cut the lawn and dynamically avoid obstacles. This product would serve a long term project for ACME's robotics division and would be an ideal way to utilize the recent capital infusion in the department. Assuming an environment map known beforehand, our robot uses the TurtleBot robot as its base platform as it follows its trajectory for mowing the lawn.

Our system is built using C++ and will leverage the ROS Navigation stack for performing autonomous navigation.

The Agile Iterative Process will be used for the development of this system consisting of two sprints.

1. The project proposal can be found here.
2. The quad chart can be found here.
3. The overall backlog table and the tables for each sprints can be found here.
4. The sprint planning notes can be found here.
5. The technical presentation slides can be found here and the video presentation, here.
6. The two parts of the demo video of the package is linked here and here.

The following shows the activity diagram for our proposed schema :

Fig 1 : Activity Diagram

The corresponding class diagram can be found here.

• ROS Melodic : installation instructions here
• Ubuntu 18.04
• C++ Version 14+
• cmake minimum version 3.0.2
• turtlebot3 packages

Run the following commands to install the dependencies required:

1. Installing the ROS navigation stack:

sudo apt-get install ros-melodic-navigation

2. Installing the turtlebot3 dependencies:

sudo apt-get install ros-melodic-turtlebot3 ros-melodic-turtlebot3-msgs ros-melodic-turtlebot3-navigation ros-melodic-turtlebot3-simulations

Make a catkin workspace catkin_ws and run the following commands :

cd <path_to_ws>/catkin_ws/src
git clone https://github.com/aditiramadwar/Autonomous_Lawn_Mower.git
cd ../
catkin_make

1. In one terminal run :

source devel/setup.bash
roslaunch alm spawn.launch

This spawns the turtlebot3 simulation in the custom world environment on the green lawn.

Fig 2 : Spawn the robot in the world

2. In a second terminal run the following to bring up the mowing routine node:

source devel/setup.bash
roslaunch alm mower.launch

This executes the lawn mowing simulation and bring up rviz for visualization of the different parameters.

3. In a third terminal run the following to start up the UI Node on the terminal:

source devel/setup.bash
roslaunch alm teleop_alm.launch

Fig 3 : RVIZ visualisation

1. Press 's' key to start the entire trajectory tracking
2. Press 'p' key to pause the robot after it reaches the next desired trajectory point.
3. Press 'r' key to resume the trajectory tracking from the point where it was paused.
4. Press 'e' key to stop the robot immediately in case of a critical issue.

Fig 4: Launch the User Interface to control the robot

By default the rosbag recording is disabled, to enable the recording, run this:

roslaunch alm mower.launch record:=true

Inspecting the recorded rosbag file: stop the launch file and run this,

rosbag info results/ros_bag_pub_sub.bag

Unit Testing is used to test each submodule and ensure complete code coverage. For this Google test (gtest) has been leveraged and identical test classes and methods are created with minimal modification in order to facilitate testing. To run the test cases, terminate the above three processes and run the following in a new terminal:

catkin_make tests
catkin_make test

or

roslaunch alm test.launch

A unit test to check the mowing functionality has been implemented which feeds a test waypoint file and checks for the number of successful points navigated.

Since coveralls could not be integrated due to Travis issues, we have manually checked for the code coverage. With the unit tests written, all the functions of the NavigationUtils and LawnMower class are being executed during testing which by our estimates, bring our coverage to nearly 90%.

Run the following command in the root directory to generate cpplint results in results folder

sh run_cpplint.sh

Run the following command in the root directory to generate cppcheck results in results folder

sh run_cppcheck.sh

To generate the doxygen documents:

doxygen doxy_config

The documents are generated in ./docs folder.

• Final Proposal has been updated with the suggestion made from the last phase
• The github repo, travi-ci, and coveralls has been linked.
• Quad chart, UMLs, Backlog Tables, and Sprint Planning sheet has been added.

Please refer to the backlog table, here, for an exhaustive list of tasks completed in Phase 1.

• The ROS package for autonomous lawn mowing was created
• The class structures were defined
• A new world to simulate our algoritm was created and mapped
• Major functions for waypoint navigation have been implemented
• A mock L shaped trajectory has been approximated for navigation
• UMLs have been revised
• Unit test case to check the quaternion conversion has been written
• Launch files to spawn the simulation, run the lawn mowing node and the unit tests have been written

Please refer to the backlog table, here, for an exhaustive list of tasks completed in Phase 2.

• Methods for trajectory tracking verification were developed.
• Methods for functionality of robot to come back to initial position after trajectory tracking were implemented.
• UI interface was developed and was integrated with the main functionality of the robot.
• Final trajectory was developed for the navigation of robot on the lawn.
• Designed the unit test structure of each class and developed unit testing for functionalities.
• UMLs were revised according to the appropriate changes made.

Please refer to the backlog table, here, for an exhaustive list of tasks completed in Phase 3.