A Novel and Reduced CPU Time Modeling and Simulation Methodology for Path Planning Based on Resistive Grids

Abstract

In mobile robotics area, path planning has been considered as key part for expanding the intelligent properties because it faces the problem of finding collision-free motions for robot systems from one configuration to another. This problem deals with the challenge of searching trajectories that optimize some cost function as the minimum cumulative edge cost, i.e., the shortest path. In addition, it is necessary that path planning can be carried out with reduced CPU time in order to tackle more complex environments in real time. In this work, the analysis of different resistive grid (RG) configurations in order to achieve the shortest path is presented. Additionally, the use of an alternative technique to decrease computing time is introduced. As a result of this research, a modeling and simulation methodology is presented. This methodology is capable of modeling the environment, formulating the equilibrium equations, solving the system of equations and finding the path between the starting and ending points. The configuration space has been modeled with square, square with diagonals, square with center, hexagonal and hexagonal with center RGs. Besides, the formulation of the equilibrium equation has been established using modified nodal analysis. Furthermore, the solution of the system of equations has been carried out using multifrontal method. In addition, the local current comparison algorithm has been resorted in order to find the shortest path. Moreover, the impact of incorporating obstacles into the configuration space of the RGs is explored. As part of the exploration to reduce CPU times, the novel methodology has been expanded into high-level programming language, i.e., Python. Finally, new contributions of this work are expounded and discussed.