A Global Path Planning Algorithm for Mobile Robot in Cluttered Environments with an Improved Initial Cost Solution and Convergence Rate

Sampling-based path planning algorithms are popularly used in autonomous mobile robot navigation applications. Optimal Rapidly exploring Random Trees (RRT*) is one of the well-known sampling-based single-query path planning algorithms and it is asymptotically optimal, but its convergence is slow. To address the slow convergence problem of the RRT* algorithm, this paper proposes a directional RRT* algorithm called D-RRT*. The key idea of D-RRT* is to reduce the sampling space. This is achieved in this proposed work by focusing on the direction of the goal from the starting configuration through a simple elliptical heuristic formed between them. The proposed methodology is validated in two different cluttered 2D environments and compared with existing algorithms. The proposed D-RRT* path planning algorithm outperforms the RRT* in three performance measures: the initial cost solution, convergence time, and the number of nodes visited. The convergence rate of the proposed D-RRT* is improved over RRT* by 8.5% and 14.7% in the two cluttered environments considered. Also, the proposed D-RRT* algorithm is validated in a real-time environment using the TurtleBot3 robot.