Repository logo
 
Publication

Dynamic Multi-Objective Auction-Based (DYMO-Auction) Task Allocation

2020Journal article Open access
Simple item page
dc.contributor.authorBaroudi, Uthman
dc.contributor.authorAlshaboti, Mohammad
dc.contributor.authorKoubaa, Anis
dc.contributor.authorTrigui, Sahar
dc.date.accessioned2022-01-13T10:45:20Z
dc.date.available2022-01-13T10:45:20Z
dc.date.issued2020
dc.description.abstractIn this paper, we address the problem of online dynamic multi-robot task allocation (MRTA) problem. In the existing literature, several works investigated this problem as a multi-objective optimization (MOO) problem and proposed different approaches to solve it including heuristic methods. Existing works attempted to find Pareto-optimal solutions to the MOO problem. However, to the best of authors’ knowledge, none of the existing works used the task quality as an objective to optimize. In this paper, we address this gap, and we propose a new method, distributed multi-objective task allocation approach (DYMO-Auction), that considers tasks’ quality requirement, along with travel distance and load balancing. A robot is capable of performing the same task with different levels of perfection, and a task needs to be performed with a level of perfection. We call this level of perfection quality level. We designed a new utility function to consider four competing metrics, namely the cost, energy, distance, type of tasks. It assigns the tasks dynamically as they emerge without global information and selects the auctioneer randomly for each new task to avoid the single point of failure. Extensive simulation experiments using a 3D Webots simulator are conducted to evaluate the performance of the proposed DYMO-Auction. DYMO-Auction is compared with the sequential single-item approach (SSI), which requires global information and offline calculations, and with Fuzzy Logic Multiple Traveling Salesman Problem (FL-MTSP) approach. The results demonstrate a proper matching with SSI in terms of quality satisfaction and load balancing. However, DYMO-Auction demands 20% more travel distance. We experimented with DYMO-Auction using real Turtlebot2 robots. The results of simulation experiments and prototype experiments follow the same trend. This demonstrates the usefulness and practicality of the proposed method in real-world scenarios.pt_PT
dc.description.sponsorshipThis research was funded by the National Plan for Science, Technology, and Innovation (MAARIFAH)—King Abdulaziz City for Science and Technology through the Science and Technology Unit at King Fahd University of Petroleum and Minerals (KFUPM), the Kingdom of Saudi Arabia, award project No. 11-ELE2147-4. In addition, Anis Koubaa would like to acknowledge the support by the Robotics and Internet-of-Things Lab of Prince Sultan Universitypt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/app10093264pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/19443
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relation.publisherversionhttps://www.mdpi.com/2076-3417/10/9/3264pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/pt_PT
dc.subjectMulti-robot system (MRS)pt_PT
dc.subjectMulti-robot task allocation (MRTA)pt_PT
dc.subjectAuction-based task allocationpt_PT
dc.subjectTask qualitypt_PT
dc.subjectOnline decision makingpt_PT
dc.titleDynamic Multi-Objective Auction-Based (DYMO-Auction) Task Allocationpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue9pt_PT
oaire.citation.startPage3264pt_PT
oaire.citation.titleApplied Sciencespt_PT
oaire.citation.volume10pt_PT
person.familyNameKoubaa
person.givenNameAnis
person.identifier989131
person.identifier.ciencia-idCA19-2399-D94A
person.identifier.orcid0000-0003-3787-7423
person.identifier.scopus-author-id15923354900
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication0337d7df-5f77-46a4-8269-83d14bd5ea6b
relation.isAuthorOfPublication.latestForDiscovery0337d7df-5f77-46a4-8269-83d14bd5ea6b

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
ART_CISTER_applsci_2020.pdf
Size:
1.09 MB
Format:
Adobe Portable Document Format
Download

Collections

ISEP – CISTER – Artigos