Keywords
trajectory
planning methods
mobile robots
How to Cite
Abstract
This paper presents a comparison of response times, route optimization, and graph complexity in path planning methods for autonomous mobile robots. The developments of Voronoi, Potential Fields, Probabilistic Roadmap, and Decomposition in cells for navigation in the same environment are contrasted and validated for a variable number of obstacles. Evaluations show that the path generation method by Potential Fields improves navigation with respect to the shortest route obtained, the Rapidly Random Tree method generates graphs of less complexity, and the Decomposition in cells method performs with less response time and lower computational cost.
Keywords: optimization, trajectory, planning methods, mobile robots.
References
H. Ajeil., K. Ibraheem, A. Sahib., J. Humaidi., “Multi-objective path planning of an autonomous mobile robot
using a hybrid PSO-MFB optimization algorithm, ” Applied Soft Computing, vol. 89, April 2020.
K.Patle., G. Babu., A. Pandey., D.R.K. Parhi., A. Jagadeesh., “A review: On path planning strategies for navigation
of mobile robot,” Defence Technology, vol. 15, pp. 582-606, August 2019.
T. Mack., C. Copot., D. Trung., R. De Keyser., “Heuristic approaches in robot path planning: A survey,” Robotics
and Autonomous Systems, vol. 86, pp. 13-28, December 2016.
L. Zhang., Z. Lin., J. Wang., B. He., “Rapidly-exploring Random Trees multi-robot map exploration under
optimization framework,” Robotics and Autonomous Systems, vol. 131, 2020.
S. Khan and M. K. Ahmmed, "Where am I? The autonomous navigation system of a mobile robot in an unknown environment," 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV), pp. 56-61, December 2016.
V. Castro, J. P. Neira, C. L. Rueda, J. C. Villamizar and L. Angel, "Autonomous Navigation Strategies for Mobile
Robots using a Probabilistic Neural Network (PNN)," IECON 2007 - 33rd Annual Conference of the IEEE
Industrial Electronics Society, pp. 2795-2800, Taipei, 2007.
Y. Li., W. Wei., Y. Gao., D. Wang., C. Fan., “PQ-RRT*: An improved path planning algorithm for mobile robots,”
Expert Systems with Applications, vol. 152, August 2020.
A. Muñoz., “Generación global de trayectorias para robots móviles, basada en curvas betaspline,” Dep. Ingeniería de Sistemas y Automática Escuela Técnica Superior de Ingeniería Universidad de Sevilla, 2014.
H. Montiel, E. Jacinto, H. Martínez, “Generación de Ruta Óptima para Robots Móviles a Partir de Segmentación
de Imágenes,” Información Tecnológica, vol. 26, 2015.
C. Expósito, “Los diagramas de Vornooi, la forma matemática de dividir el mundo,” Dialnet, Diciembre
N. Sotomayor., A. Yandún., “Planeación y seguimiento de trayectorias para un robot móvil,” Researchgate,
Agosto 2012.
Y. Zhang, Z. Liu, and L. Chang, "A new adaptive artificial potential field and rolling window method for mobile
robot path planning," 2017 29th Chinese Control And Decision Conference (CCDC), pp. 7144-7148 July, 2017.
S. Garrido., L. Moreno., D. Blanco, P. Jurewicz., “Path Planning for Mobile Robot Navigation using Voronoi
Diagram and Fast Marching,” International Journal of Robotics and Automation (IJRA), vol. 2, 2011.
Q. Jia, X. Wang, "An improved potential field method for path planning," 2010 Chinese Control and Decision
The conference, pp. 2265-2270, 2010.
T. Osuna, L. Gonzáles y L. Aguilar, “Técnica de navegación de campos potenciales para un robot móvil para la evasión de obstáculos”, Marzo, 2010.
V. Gonzáles y R. Parkin, “Evadiendo obstáculos con robots móviles”, Enero 2005.
J. Sfeir, M. Saad and H. Salah-Hassane, "An improved Artificial Potential Field approach to real-time mobile robot path planning in an unknown environment," 2011 IEEE International Symposium on Robotic and Sensors Environments (ROSE), pp. 208-213, 2011.
I. Pérez-Hurtado, M. J. Pérez-Jiménez, G. Zhang and D. Orellana-Martín, "Robot path planning using rapidly-exploring random trees: A membrane computing approach," 2018 7th International Conference on Computers Communications and Control (ICCCC), pp. 37-46, 2018.
S. Zhang, J. Pu, Y. Si., L. Sun, "Smooth Path Planning for Mobile Robot Based on Adaptive Rapidly-exploring Random Tree*," 2018 IEEE International Conference on Information and Automation (ICIA), pp. 591-596, 2018.
Y. Ying, Z. Li, G. Ruihong, H. Yisa, T. Haiyan and M. Junxi, "Path planning of mobile robot based on Improved RRT Algorithm," 2019 Chinese Automation Congress (CAC), Hangzhou, pp. 4741-4746, 2019.
C. Calderón, T. Bustillo, “Determinación de rutas por medio de un algoritmo RRT adaptado a un discretización del espacio de trabajo”, Junio 2018.
D. López, F. Gómez, F. Cuesta y A. Ollero, “Planificación de trayectorias con el algoritmo RRT. Aplicación a robots no holónomos”, Enero, 2010.
JW Kang, SJ Kim, MJ Chung, H. Myung, JH Park y SW Bang, "Planificación de ruta para la operación de cobertura completa y eficiente de robots móviles", Conferencia Internacional de 2007 sobre Mecatrónica y Automatización, pp. 2126-2131, 2007.
N. Zafara, J.C.Mohanta, “Methodology for Path Planning and Optimization of Mobile Robots: A Review,” Procedia Computer Science, vol. 133,pp. 141-152, 2018.
H. A. Vasseur, F. G. Pin, and J. R. Taylor, "Navigation of a car-like mobile robot using a decomposition of the environment in convex cells," Proceedings. 1991 IEEE International Conference on Robotics and Automation, vol. 2, pp. 1496-1502, 1991.
C. Vélez, J. Guzmán y I. Durley, “Evaluación de las técnicas de planificación de movimientos, descomposición exacta trapezoidal y descomposición adaptativa de celdas a través de mallas”. Dialnet, Marzo, 2012.
W. Regli, “Robot Lab: Robot path planning,” Lecture Notes of the department of computer science, Drexel University, Oct 2007.