A Comparative Study on Grid-Based and Non-grid-based Path Planning Algorithm

Abstract

Recent years have seen a dramatic uptick in research efforts dedicated to the development of mobile robots. One of the most common research topics in this area involves the path planning of mobile robots. The existing algorithms use the samples to construct a network or a route. There are many methods available for creating samples on the map as well. However, planners need to explore a bigger search space while building a path for the mobile robot because the samples are dispersed around the map. In this study, we examine a gridbased sampling strategy that narrows the search while still allowing us to probe potential avenues of exploration. For this objective, we implement the three most well-known path planning algorithms namely, $\mathbf{A}^{*}$, Probabilistic Roadmap (PRM), and Rapidly-Exploring Random Tree (RRT). The algorithms are compared using a grid-based path planner and a non-grid-based planner. The observed findings show that the proposed sampling technique is more effective than the previous one.