Sampling-based path planning with goal oriented sampling

Abstract

Path planning in complicated environments is a time consuming and computationally expensive task. Especially in high-dimensional configuration spaces with complex obstacles, searching for a proper path while avoiding collisions is still challenging. This paper presents an improved sampling-based algorithm, called the Goal Oriented sampling method (GO sampling) that quickly generates an initial solution overcoming these problems. GO sampling extends the sampling method of the Rapidly-exploring Random Tree (RRT) algorithm. GO sampling is able to identify the initial solution in a shorter time than that of the RRT algorithm and shows significant improvement in computational efficiency. The algorithm is evaluated with simulations in 2D and 3D space.