An Improvised A Algorithm for Mobile Robots to Find the Optimal Path in an Unknown Environment with Minimized Search Efforts

An Algorithm for finding the optimal path given the initial and final positions of a cylinder-shaped mobile robot with the constraint on minimizing the search efforts is presented in this paper. This algorithm is based on the well known AI strategy A* to get the optimal solution always. The strategy constructs a path from the available knowledge incrementally leading to the optimal path, and at the same time without any collision with the stationary obstacles. In this process, there are discontinuous “jumps” which demand the robot to move physically through the whole path traversing all along to reach the other end of the jump. Most of the times, the robot spends more time in traversing back and forth during the process of finding the optimal path. We have developed three new techniques, namely, Petri expansion, Markovian cost function, and Retaining shortest path nodes to minimize the search efforts in finding the optimal path. We have demonstrated the effectiveness of these techniques with ample examples. These three techniques always lead not only to the optimal path, but improves the search strategy by minimizing the “jumps” required. Computationally also, they reduce the number of nodes generated in the general A* algorithm. The important issue is that the incorporation of these techniques in A* is very easy with simple modifications and without any extra computational burden.