Socio-realistic optimal path planning for indoor realtime autonomous mobile robot navigation

Abstract

An autonomous mobile robotic navigation system consists of many modules which work co-ordinately and concurrently. The most important module is the realistic and optimal path planning algorithm (ROPPA) through which the overall system improves it performance. Many algorithms have been developed and deployed for data structures, and computer games are partially modified for use in real time robotic environments. The major drawback of such modified algorithms is they are designed for unconstrained artificial environments where the robot's collision with a static obstacle or moving object is partially allowed. Many researchers successfully developed the path planning algorithm through improving the basic A* algorithm, such as D* lite, theta*, any angle path planning, and jump point search. In real environments one should seek the optimal path along with maintaining uniform safer distance with the objects or in-path obstacles. This paper describes the implementation and evaluation of a new realistic optimal path planning algorithm which follows the safer distance rule through exploring minimum workspace arena. Along with less memory overhead, the algorithm explores shortest final path with fewer sub-paths if they exist. The experimentation with different map sizes and obstacle densities clearly defines improvement in ROPPA over the other path planning methods.