Heuristic approaches in robot navigation

This paper presents the implementation of A* algorithm as a global path planner for the navigation of a Turtlebot robot. A map of the navigation environment has been developed for a Gazebo simulator's world. Manhattan distance, octile distance, and Euclidean distance heuristic functions are used to estimate the cost of moving from a cell to the goal cell of the environment. Time taken by the global planner, length of the path covered and the number of cells to visit are presented in tabular form. Mat plots for global costmap changes are also given using `rqt' tool of ROS (robot operating system).