SBA*: An efficient method for 3D path planning of unmanned vehicles

Abstract

Recently, researchers have stated that the movement of unmanned vehicles (UVs) in 3D environments is more complex compared to 2D due to extra height and depth dimensions, and they have focused on the development of UV technology in this direction. Especially in path planning problems, studies on different parameters such as time, distance and energy consumption have gained importance. This paper focuses on path planning efficiency in complex 3D environments and proposes a method called Segment Based A* (SBA*), which runs on graphs created using random nodes. In this method, the path initially planned with A* on a global graph is divided into segments, and new local graphs are created on these segments for more efficient path planning. Extensive simulations in both 2D and 3D environments with various obstacle configurations demonstrate that SBA* significantly outperforms traditional algorithms in terms of key performance metrics including path length, total rotation angle, number of sharp turns and smoothness ratio. These improvements indicate that SBA* not only enhances path efficiency but also considerably reduces energy consumption, making it a valuable contribution to practical applications in UV technology.