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Computación y Sistemas

Print version ISSN 1405-5546

Comp. y Sist. vol.12 n.1 México Jul./Sep. 2008

 

Sampling–Based Motion Planning: A Survey

 

Planificación de Movimientos Basada en Muestreo: Un Compendio

 

Abraham Sánchez López, René Zapata* and Maria A. Osorio Lama

 

Computer Science Department, Autonomous University of Puebla 14 Sur and San Claudio, CP 72570, Puebla, Pue. México

* LIRMM – UMR55606 CNRS, 161, rue Ada 34392 Montpellier Cedex 5, France

asanchez@cs.buap.mx, asorio@cs.buap.mx, zapata@lirmm.fr

 

Article received on April 13, 2008;
Accepted on June 20, 2008

 

Abstract

Sampling–based motion approaches, like Probabilistic Roadmap Methods or those based on Rapidly–exploring Random Trees are giving good results in robot motion planning problems with many degrees of freedom. Following these approaches, several strategies have been proposed for biasing the sampling towards the most promising regions, thus improving the efficiency and allowing to cope with difficult motion planning problems.

The success of these planners in solving challenging problems can be explained by the fact that no explicit representation of the free configuration space is required. This paper reviews some of the most influential proposals and ideas, providing indications on their practical and theoretical implications. The contributions made by Mexican researchers in this field are also presented.

Keywords: Motion planning, probabilistic roadmaps, sampling–based motion planning, path planning, algorithms.

 

Resumen

Los enfoques de planificación de movimientos basados en muestreo, como los métodos de Roadmap Probabilista o aquellos basados en los Árboles Aleatorios de Exploración Rápida están dando buenos resultados en la planificación de movimientos de robots con muchos grados de libertad. Con estos enfoques, se han propuesto varias estrategias para predisponer el muestreo hacia las regiones más prometedoras, mejorando con esto la eficiencia y permitiendo la solución de problemas difíciles de planificación de movimientos. El éxito de estos planificadores en la solución de problemas desafiantes se puede explicar por el hecho de que no se requiere ninguna representación explícita del espacio de configuraciones libre.

Este artículo repasa algunas de las propuestas e ideas más influyentes en el área, proporcionando indicaciones de sus implicaciones teóricas y prácticas. También se presentan las contribuciones realizadas por los investigadores Mexicanos en este campo.

Palabras claves: Planificación de movimientos, roadmaps probabilistas, planificación de movimientos basada en muestreo, planificación de trayectorias, algoritmos.

 

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