Quick Goal Seeking Algorithm for Frontier based Robotic Navigation

There arises situations where an autonomous robot needs to navigate to a target location and no information is available about the terrain. Frontier based navigation is one of the most efficient methods of exploration and navigation for such situations. In a frontier based strategy, the robot navigates to the target location by detecting intermediate frontier regions, which are points lying on the boundary separating the explored region from the unexplored. In this paper, a new frontier based robotic navigation algorithm called the Quick Goal Seeking (QGS) algorithm is proposed. The QGS algorithm is tested in a real-time environment and its performance is compared with two major frontier based navigation algorithms; Modified Goal Seeking (MGS) and Fast Frontier Detector (FFD). The QGS algorithm uses heuristic informed search for path planning. It consists of a highly optimized and efficient scanning function which minimizes the search space. The performance of these three algorithms is compared based on the total time taken to reach the destination. It has been found out that the QGS algorithm performs better than the MGS and FFD algorithms in almost all the cases.