A neural network approach for a robot task sequencing problem

This paper presents a neural network approach with successful implementation for the robot task-sequencing problem. The problem addresses the sequencing of tasks comprising loading and unloading of parts into and from the machines by a material-handling robot. The performance criterion is to minimiz...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Artificial intelligence in engineering Jg. 14; H. 2; S. 175 - 189
Hauptverfasser: Maimon, O., Braha, D., Seth, V.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.04.2000
Schlagworte:
Asymmetric traveling salesman Problem
Automated storage and retrieval
Parallel and distributed computation
Recurrent neural networks
Robot task sequencing
Robot task sequencing
Recurrent neural networks
Asymmetric traveling salesman Problem
Automated storage and retrieval
Parallel and distributed computation
ISSN:0954-1810
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This paper presents a neural network approach with successful implementation for the robot task-sequencing problem. The problem addresses the sequencing of tasks comprising loading and unloading of parts into and from the machines by a material-handling robot. The performance criterion is to minimize a weighted objective of the total robot travel time for a set of tasks and the tardiness of the tasks being sequenced. A three-phased parallel implementation of the neural network algorithm on Thinking Machine's CM-5 parallel computer is also presented which resulted in a dramatic increase in the speed of finding solutions. To evaluate the performance of the neural network approach, a branch-and-bound method and a heuristic procedure have been developed for the problem. The neural network method is shown to give good results and is especially useful for solving large problems on a parallel-computing platform.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0954-1810
DOI:10.1016/S0954-1810(00)00008-X