Piecewise Rapidly-Exploring Random Tree Star

In this paper, we propose the piecewise technique of Rapidly-exploring Random Tree-Star (P-RRT*) algorithm used in low or medium specification agent(s) (rovers) in the two- dimensional (2-D) workspace. The traditional RRT, RRT*, and other path planning algorithms however efficient they have become; all treat a given environment as a whole and attempt to find a feasible path. This may result in higher memory utilization and a significant increase in processing time.We utilize the RRT* algorithm as the base and integrate it with the piecewise approach. Through P-RRT* technique, given an environment with no obstacles, we attempt to minimize the three vital elements used in the RRT* path planning algorithm (memory, power consumption, and time).A 2D simulation is utilized for demonstration purposes. Given a large workspace, we simulate over subregional workspaces where the number of nodes and step size are adjusted properly to minimize the cost. The simulation results show that dividing the entire simulation workspace into subregions and treating each subregion as a new workspace not only reduces memory utilization and processing time but also the power consumption as a result.The simulation results are shown versus the traditional RRT* algorithm; similar constraints are set for both the piecewise RRT* technique and the traditional RRT* algorithm; meaning that the number of nodes and step size is the same for both methods.