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Autonomous Navigation System for Turtlebot for Pathfinding and Obstacle Avoidance

*Note: Be sure to have ROS Kinetic Kame installed with the desktop-full option. You will also need the ros-kinetic-turtlebot package.

Please consider further refining my Pathfinding and Obstacle Avoidance Algorithm.

Project Synopsis

The project aim is to develop an autonomous navigation mechanism for the Turtlebot in ROS Gazebo, capable of identifying the optimal route and dodging obstacles within a labyrinthine setting. For this, we've applied the A* search algorithm, which directs the robot's path to the target based on the data captured by its RGB-D Kinect sensor. This system can tackle both familiar and unfamiliar terrains as long as the initial and final coordinates are (0,0) and (4,4), respectively.

To incorporate this repository in a new or existing catkin workspace, refer to the instructions given below.

Preparation & Configuration

Integrating this repository with a new catkin workspace

  1. Establish a new catkin workspace.

    Open your terminal shell, move to your selected directory for the new workspace, and execute the following commands. Replace <wsname> with your chosen workspace name.

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    $ mkdir -p <wsname>/src $ cd <wsname>/src/ $ catkin_init_workspace $ cd .. $ catkin_make

  2. Clone this repository and transfer necessary folders into your catkin workspace.

    In the same <wsname> directory, execute the following:

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    $ git clone [https://github.com/neelgandhi108/Autonomous-Navigation-System-for-Turtlebot-for-Pathfinding-and-Obstacle-Avoidance.git](https://github.com/neelgandhi108/Autonomous-Navigation-System-for-Turtlebot-for-Pathfinding-and-Obstacle-Avoidance/) $ cd Autonomous-Navigation-System-for-Turtlebot-for-Pathfinding-and-Obstacle-Avoidance/ $ cp -r src/bot ../src $ cp -r worlds docs project_init.sh ../ $ cd .. $ catkin_make

Integrating this repository with an existing catkin workspace

  1. Clone this repository and transfer necessary folders into your existing catkin workspace.

    In your terminal shell, navigate to the root directory of your existing catkin workspace and execute the following:

    shellCopy code

    $ git clone [https://github.com/neelgandhi108/Autonomous-Navigation-System-for-Turtlebot-for-Pathfinding-and-Obstacle-Avoidance.git](https://github.com/neelgandhi108/Autonomous-Navigation-System-for-Turtlebot-for-Pathfinding-and-Obstacle-Avoidance/) $ cd Autonomous-Navigation-System-for-Turtlebot-for-Pathfinding-and-Obstacle-Avoidance/ $ cp -r src/bot ../src $ cp -r worlds docs project_init.sh ../ $ cd .. $ catkin_make

Running the Code

Initializing the Gazebo environment:

To set up the Gazebo world, execute the following commands in a terminal:

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$ source devel/setup.bash $ chmod +x project_init.sh $ ./project_init.sh

Starting autonomous navigation to goal:

Open a new terminal, execute the following commands to start the Turtlebot's autonomous navigation.

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$ source devel/setup.bash $ roslaunch bot bot.launch

Upon reaching the goal coordinates, the Turtlebot will display a notification in the terminal along with the time it took to reach the target.

Code Analysis & Clarifications

Nodes

We developed four main nodes for this package that handle different aspects of the Turtlebot's autonomous navigation.

  1. pos_info

    This node subscribes to the odom_combined topic published by the robot_pose_ekf node. It's a refined version of standard odometry provided by \odom as it merges Extended Kalman Filtering of the odometry data with Inertial Measurement Unit (IMU) data from the Turtlebot. It then publishes the Turtlebot's pose estimate for other nodes to subscribe to.

  2. depth_info

    This node publishes the depth information from the Turtlebot's RGB-D Kinect sensor at the center point. This acts as our basic obstacle detection system for barriers directly in the Turtlebot's path.

  3. scan_info

    This node subscribes to the \scan topic published by the laserscan_nodelet_manager node that obtains the scan depth information from the depthimage_to_laserscan. This transforms the RGB-D Kinect sensor data into 'virtual' laser readings akin to that of a LIDAR sensor. This node then publishes the depth information for the left-most, right-most, and middle readings on the horizontal axis. The bot_control node uses this data to act as our anticipatory obstacle detection system for obstructions along the Turtlebot's intended path.

  4. bot_control

    This node serves as our primary node that manages the motion control, obstacle evasion, and pathfinding for the Turtlebot. It subscribes to the /auto_ctrl/scan_info, /auto_ctrl/depth_info, and /auto_ctrl/pos_info topics. Using the algorithms found in the algo.h header file, it determines the next coordinate to move towards, dodging obstacles, updating the algorithm with the presence of obstacles to reach the goal coordinates.

Algorithm file algo.h

This file contains two algorithms, Flood Fill and A* search. However, we primarily utilize the A* search algorithm due to its speedy computation and reliability. This file also holds the code for the Turtlebot's internal 2D array map so that the algorithm can compute the next move.

Launch files

  1. world.launch

    This launch file gets started in the project_init.sh shell file to launch the Gazebo environment using the defined worlds in project_init.sh as well as the Turtlebot base. It also includes the empty_world.launch file which sets the different arguments for the Gazebo launch.

  2. empty_world.launch

    We added the line <remap from="tf" to="gazebo_tf"/> in this launch file to ensure that the tf tree created by Gazebo does not interfere with the tf tree created by Turtlebot. We needed to do this to use the robot_pose_ekf node as there was a conflict in the tf frames created by Gazebo and the robot_pose_ekf node which prevented \odom_combined from connecting to the tf tree.

  3. bot.launch

    This launch file is our primary launch file to run the robot_pose_ekf node and the four nodes that we mentioned previously.

Choosing the worlds for testing

To select the world for testing the Turtlebot performance, you need to amend the project_init.sh file to change the chosen world file.

  1. test_world_1.world

    Time taken to reach goal: ~50s

    This world is the simplest one to test our system as it has the shortest and easiest path in terms of obstacle evasion to guide the Turtlebot from start to goal.

    World 1

  2. test_world_2.world

    Time taken to reach goal: ~100s

    This world has additional complexity due to more obstacles to be avoided and a longer path to travel from start to goal, which increases the chance of the Turtlebot drifting off its intended course.

    World 2

  3. test_world_trap.world

    Time taken to reach goal: ~2000s

    We designed this world to challenge our system by adding five potential dead ends along the Turtlebot's path and to test if it can recover after being trapped in the dead end and navigate to the goal.

    World 3

Acknowledgments

This project was guided by tutorials from the course EE4308 - Advances in Intelligent Systems and Robotics at the National University of Singapore.

System Prerequisites

Version / Release

Operating System

Ubuntu 16.04 LTS (Xenial Xerus)

ROS Distribution Release

Kinetic Kame*

C++ Version

Minimum C++11

All dependencies have been updated and resolved as of June 22, 2023.