Path planning will lead to give ready to go environment for bot Navigation stack will function with move base package in this whole flow. The main purpose of this project is to develop a prototype that can be used for the autonomous vehicles and other fields like agriculture and ware house management for the supply of goods purposes. Fig. 1: Block diagram Deployed the turtlebot3 and some obstacle into the gazebo environment. Using the various options available in the Gazebo platform, we explored the ways in which the prototype could be implemented. We started exploring it with the various options that were available with us. Fig. 4: Tutlebot3 moving in Gazebo environment As the obstacles and the turtlebot 3 are deployed into the environment, we trained the Turtlebot3 in the gazebo environment, by making it move frontwards, backwards, and also on either side i.e. left and right. Fig 3: Hexagonal Gazebo environment with multiple obstacles and Turtlebot3 The gazebo simulator provides various size of obstacles which are of cylindrical, circular and others shapes. By making use of this as obstacles for the turtlebot 3, we designed the environments. Fig 5: Completely visualized map Fig 7: Navigating through the shortestpath using Dijkstra’s algorithm As soon as the turtlebot 3 get trained and able to visualize the environment, we can train the model. We have trained the turtlebot 3 with Dijkstra’s algorithm for finding the shortest path to navigate to the desired destination without colliding with the obstacles. Fig 8: Developed an environment with classroom setup using Build Editor
