Repository logo
 

Using genetic algorithms to optimize social robot behavior for improved pedestrian flow

Simple item page
dc.contributor.authorEldridge, Bryce D., author
dc.contributor.authorMaciejewski, Anthony A., author
dc.contributor.authorIEEE, publisher
dc.date.accessioned2015-07-28T20:36:11Z
dc.date.available2015-07-28T20:36:11Z
dc.date.issued2005
dc.description.abstractThis paper expands on previous research on the effect of introducing social robots into crowded situations in order to improve pedestrian flow. In this case, a genetic algorithm is applied to find the optimal parameters for the interaction model between the robots and the people. Preliminary results indicate that adding social robots to a crowded situation can result in significant improvement in pedestrian flow. Using the optimized values of the model parameters as a guide, these robots can be designed to be more effective at improving the pedestrian flow. While this work only applies to one situation, the technique presented can be applied to a wide variety of scenarios.
dc.format.mediumborn digital
dc.format.mediumproceedings (reports)
dc.identifier.bibliographicCitationEldridge, Bryce D. and Anthony A. Maciejewski, Using Genetic Algorithms to Optimize Social Robot Behavior for Improved Pedestrian Flow, Proceedings: the International Conference on Systems, Man and Cybernetics, Waikoloa, Hawaii, October 10-12, 2005: 524-529.
dc.identifier.urihttp://hdl.handle.net/10217/1294
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofFaculty Publications
dc.rights©2005 IEEE.
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectcrowd dynamics
dc.subjectgenetic algorithms
dc.subjectsocial robots
dc.titleUsing genetic algorithms to optimize social robot behavior for improved pedestrian flow
dc.typeText

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
ECEaam00113.pdf
Size:
416.02 KB
Format:
Adobe Portable Document Format
Description:
Download

Collections

Faculty Publications