Comparative analysis on path planning of ATR using RRT*, PSO, and modified APF in CG-Space

Abstract

Path planning of All-Terrain Rover over 3D terrains is one of the most challenging robotics problems. Prior research has been carried out over 2D static and dynamic configuration space. Currently, all available algorithms for 3D path planning of articulated rover require optimization for wheel-terrain interaction. In this comparative analysis, the path planning is carried out over the 10 degrees of freedom Rover CG-Space instead of ground. CG-Space is the collection of all possible center of gravity locations of the Rover while traversing on terrain. The introduction of preobtained CG-Space eliminates the need for optimization during the planning stage. In this work, a comprehensive path planning comparison has been obtained among modified artificial potential field (APF) method, sampling-based technique (RRT*), and particle swarm optimization (PSO) algorithm based on path length, path smoothness, and computational time. All algorithms are applied over CG-Space associated with two categories of terrains: even and uneven. Even terrains have a well-defined surface equation, while uneven terrains are generated using Kinect V2 sensor mounted over the sand arena. A continuous surface from CG-Space is generated using cubic spline interpolation. The simulation results show that all three algorithms generate obstacle-free paths in a different time span. Although sampling-based and APF-based techniques take about five times lesser time, the obtained paths have moderate smoothness. PSO performs better than RRT* and modified APF for the requirements of optimal path length and smoothness. Thus, PSO is the optimal way to plan the path if the computational cost associated is acceptable. On the other hand, if time is the major planning concern, then RRT* is the best because the path smoothening takes very little time when using separate cubic spline techniques after obtaining the path nodes. This comparative analysis is useful in selecting an appropriate algorithm depending on the terrain complexity and obstacle conditions.