TurtleBot3 - Visual Slam and Path Planning with Dynamic Obstacle avoidance

Objective of the project :

To make Localization through Visual SLAM and making a local costmap using OAKD and global costmap using LIDAR on the TurtleBot3, along with this we need to implement dynamic Obstacle Avoidance

Project
- Demo
- Table of Contents
- Stages
- Demo
- Algorithm Flowchart
- Our Approach
- Getting Started

Stages

Stage 1:
- Understanding Various Path Planning Algorithms
- Writing a brute Code for BFS
- Using LIDAR to create a map and then use MOVE_BASE along with a custom GLOBAL PLANNER for BFS
- Implement BFS in Simulation with Global/Static Map
Stage 2:
- Mapping and Localization through OAK-D
- Implementing Algorithm for Dynamic Obstacle Avoidance in simulation
- Creating an Arena for the same
Reference for the Model : Robotis

We are Using turtle bot burger for our Path planning objective

Demo:

Stage1_demo.mp4

Algorithm for BFS

Basic Algorithm for BFS

Breadth-first search (BFS) is an algorithm for traversing or searching tree or graph data structures. It starts at the tree root (or some arbitrary node of a graph, sometimes referred to as a 'search key') and explores the neighbor nodes first, before moving to the next level neighbors.

Pseudocode : Using Queues

Note: That we use stack and layers instead of traditional queues to implement the BFS algorithm

BFS(root)

  Pre: root is the node of the BST
  Post: the nodes in the BST have been visited in breadth first order
  q ← queue

  while root = ø

    yield root.value
    if root.left = ø
      q.enqueue(root.left)
    end if

    if root.right = ø
      q.enqueue(root.right)
    end if

    if !q.isEmpty()
      root ← q.dequeue()
    else
      root ← ø
    end if

  end while

end BFS

Before Moving on to the next part we need to have a better understanding of three data structures
- Vectors : Vectors are sequence containers representing arrays that can change in size. Click Here to know more
  - We don't specify namespace in the code because there are two main things ros and std so instead we use scope resolution operator to specify the functionality
- Stacks : Stacks are a type of container adaptors with LIFO(Last In First Out) type of working, where a new element is added at one end (top) and an element is removed from that end only. Click here to know more.
- Pairs : Pair is used to combine together two values that may be of different data types. Pair provides a way to store two heterogeneous objects as a single unit. It is basically used if we want to store tuples. The pair container is a simple container defined in < utility > header consisting of two data elements or objects. Click here to know more.

Pseudocode : Using Stacks

Some Initializations:

path : vector of pair, where two elements in the pair are the ith and jth index respectively
layer : vector of pair of pairs, where one pair is for the parent node indices and the other pair is for child's node index
stack : stack of vector of pair of pairs, basically used to store layers

Note : Layer contains the elements which lie in the same level.

BFS(root)

  Pre: root is the node of the BST
  Post: the nodes in the BST have been visited in breadth first order
  st ← stack : which will store individual layers
  layer ← layer : vector of pair of pairs, each pair contains pairs of indices of parent node and current node respectively

  // Creating a tree
  while root != final index:

    check for the neighbours in east, south, north and west directions
      layer = children which are unvisited, and then mark them visited
    
    st.push(layer) ← adding the whole layer

    clear layer ← After this new layer begins as the previous layer is already pushed into the stack and marked visited

  end while

  if root == final index:

    path.push_back(final index)
    while stack is not empty:

      temp_layer = st.top() ← get the topmost layer in the stack because it definitely will contain the final index
      x = find final index in temp layer ← this will give us the node we were looking for
      
      if (found):
        
        parent = temp_layer[x].first ← Now get the parent of the and put that in the path
        path.push_back(parent) ← parent is now in the path

      end if

      st.pop()

    end while

  end if

  return(path)

end BFS

Applications

Finding the shortest path between two nodes u and v, with path length measured by number of edges (an advantage over depth-first search)
Serialization/Deserialization of a binary tree vs serialization in sorted order, allows the tree to be re-constructed in an efficient manner.
Construction of the failure function of the Aho-Corasick pattern matcher.
Testing bipartiteness of a graph.

Our use case

There are two main steps that we need to perform here:

Storing each node by connecting them to its neighbours which are unoccupied and unvisited. The subpart for this is:
1. Storing a single parent of current node.
2. Storing all the unvisited neighbours as children in the form of a list.
After storing, we just need to reach the required node, that will be set as target and will be given as the input by the user

How to run BFS from this project

In the first terminal source the cloning repo in the src folder of the workspace that you have created git clone https://github.com/aPR0T0/TurtleBot-V3-BFS.git


nano ~/.bashrc
// And add these lines and the source the bashrc
alias get_tb3='source /opt/ros/noetic/setup.bash && export TURTLEBOT3_MODEL=burger && source ~/your_ws/devel/setup.bash'

As we have now created an alias so no need to repeat the previous step as you relaunch the nodes, just use the alias to source the directories!

// alias
get_tb3

roslaunch turtlebot3_gazebo turtlebot3_world.launch

// In 3rd terminal
get_tb3

roslaunch turtlebot3_slam turtlebot3_slam slam_method:=gmapping

// In 4th terminal
get_tb3

roslaunch turtlebot3_teleop turtlebot3_teleop_key.launch 

// Now, just move the bot until whole arena is traversed and then close 4th terminal as map is not created
// Once done mapping close terminal 3 and go to next instruction

// In 5th terminal
get_tb3

roslaunch turtlebot3 map_node.launch map_file:=$HOME/map.yaml

// Now use Estimated pose Icon on GUI to give initial estimate of where bot is...
// then in 6th terminal
get_tb3

roslaunch turtlebot3_teleop turtlebot3_teleop_key.launch 

// traverse the map until all particles closer and closer to the bot

Voila! You got the simulation done!! Now, Just click on nav_goal_2d in RVIZ GUI and see the magic of path planning

aPR0T0 / Visual_SLAM_on_TB3