Open AccessPseudo-Bacterial Potential Field Based Path Planner for Autonomous Mobile Robot Navigation
Author(s) -
Ulises Orozco-Rosas,
Oscar Montiel,
Roberto Sepúlveda
Publication year - 2015
Publication title -
international journal of advanced robotic systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.394
H-Index - 46
eISSN - 1729-8814
pISSN - 1729-8806
DOI - 10.5772/60715
Subject(s) - motion planning , computer science , mobile robot , path (computing) , potential field , implementation , fitness function , field (mathematics) , mathematical optimization , genetic algorithm , robot , function (biology) , artificial intelligence , mathematics , machine learning , geophysics , pure mathematics , programming language , geology , evolutionary biology , biology
This paper introduces the pseudo-bacterial potential field (PBPF) as a new path planning method for autonomous mobile robot navigation. The PBPF allows us to obtain an optimal and safe path, in contrast to the classical potential field approach which is not suitable for path planning because it lacks a means of obtaining the optimal proportional gains. The PBPF uses the pseudo-bacterial genetic algorithm (PBGA) and a fitness function based on the potential field concepts to construct viable paths in dynamical environments to mostly result in the optimal path being obtained. Comparative experiments of sequential and parallel implementations of the PBPF for off-line and online in structured and unstructured conditions are presented; the results are contrasted with the artificial potential field (APF) method to demonstrate how the PBPF proposal overcomes the traditional method