• A fast two-stage ACO algorithm...

Result: A fast two-stage ACO algorithm for robotic path planning : ISNN 2011

Title:
A fast two-stage ACO algorithm for robotic path planning : ISNN 2011
Authors:
XIONG CHEN, YINGYING KONG, XIANG FANG, QIDI WU
Source:
Neural computing & applications (Print). 22(2):313-319
Publisher Information:
London: Springer, 2013.
Publication Year:
2013
Physical Description:
print, 15 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Neurology, Neurologie, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Analyse mathématique, Mathematical analysis, Calcul des variations et contrôle optimal, Calculus of variations and optimal control, Probabilités et statistiques, Probability and statistics, Statistiques, Statistics, Applications, Biologie, psychologie, sciences sociales, Biology, psychology, social sciences, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Méthodes numériques en programmation mathématique, optimisation et calcul variationnel, Numerical methods in mathematical programming, optimization and calculus of variations, Optimisation et calcul variationnel numériques, Numerical methods in optimization and calculus of variations, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Algorithme rapide, Fast algorithm, Algoritmo rápido, Calcul neuronal, Neural computation, computación neuronal, Complexité, Complexity, Complejidad, Fonction transfert, Transfer function, Función traspaso, Haute résolution, High resolution, Alta resolucion, Maillage, Grid pattern, Celdarada, Méthode optimisation, Optimization method, Método optimización, Optimisation, Optimization, Optimización, Planification trajectoire, Path planning, Qualité, Quality, Calidad, Robotique, Robotics, Robótica, Réseau neuronal, Neural network, Red neuronal, Vitesse convergence, Convergence speed, Velocidad convergencia, 49XX, 62P12, 65K10, 65Kxx, Ant colony algorithm, Less-1 search, Scent broadcast
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Intelligent Control Research Lab, Fudan University, Shanghai, China
Center of CIMS, Tongji University, Shanghai, China
ISSN:
0941-0643
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=27659212
Rights:
Copyright 2014 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Mathematics
Accession Number:
edscal.27659212
Database:
PASCAL Archive

Further Information

Ant colony optimization (ACO) algorithms are often used in robotic path planning; however, the algorithms have two inherent problems. On one hand, the distance elicitation function and transfer function are usually used to improve the ACO algorithms, whereas, the two indexes often fail to balance between algorithm efficiency and optimization effect; On the other hand, the algorithms are heavily affected by environmental complexity. Based on the scent pervasion principle, a fast two-stage ACO algorithm is proposed in this paper, which overcomes the inherent problems of traditional ACO algorithms. The basic idea is to split the heuristic search into two stages: preprocess stage and path planning stage. In the preprocess stage, the scent information is broadcasted to the whole map and then ants do path planning under the direction of scent information. The algorithm is tested in maps of various complexities and compared with different algorithms. The results show the good performance and convergence speed of the proposed algorithm, even the high grid resolution does not affect the quality of the path found.