Optimal navigation of autonomous vehicles

It is very important for an autonomous mobile vehicle to navigate properly without any collision or unsafe conditions in its environment. Mobile robot navigation is a very important exercise in all robotic application from a domestic household cleaner to highly dangerous life threatening situations like bomb diffusing and nuclear decommissioning. Path planning is the main issue related to navigation. Path planning in mobile robots must ensure the optimal path with least cost and collision free path. The standard A* algorithm is capable of finding the shortest path. Modified A* path planning algorithm takes into consideration the robot size and safe diagonal movement of autonomous vehicle. In this thesis, a known model of Autonomous Control Engineering (ACE) lab is made to test the A* and modified A* path planning algorithm. An algorithm to reduce the search process approximately into half by leaving a node and searching for the end result with the successor node is proposed. The outcome of this thesis is a comparison between the three algorithms mentioned above with taking into consideration the search steps, expanded nodes and cost. To test the planning algorithms in real time TurtleBot 2 is used.