Adaptive metaheuristic-based methods for autonomous robot path planning: Sustainable agricultural applications

Basit Öğe Kaydı
dc.authoridFarzad Kiani / 0000-0002-0354-9344en_US
dc.authoridAmir Seyyedabbasi / 0000-0001-5186-4499
dc.authoridFateme Ayşin Anka / 0000-0002-2795-6438
dc.authorscopusidFarzad Kiani / 36662461100
dc.authorscopusidAmir Seyyedabbasi / 57202833910
dc.authorscopusidFateme Ayşin Anka / 57414861500
dc.authorwosidFateme Ayşin Anka / GNA-1067-2022
dc.authorwosidAmir Seyyedabbasi / HJH-7387-2023
dc.authorwosidFarzad Kiani / O-3363-2013
dc.contributor.authorKiani, Farzad
dc.contributor.authorSeyyedabbasi, Amir
dc.contributor.authorNematzadeh, Sajjad
dc.contributor.authorCandan, Fuat
dc.contributor.authorÇevik, Taner
dc.contributor.authorAnka, Fateme Ayşin
dc.contributor.authorRandazzo, Giovanni
dc.contributor.authorLanza, Stefania
dc.contributor.authorMuzirafuti, Anselme
dc.date.accessioned2022-01-26T13:49:44Z
dc.date.available2022-01-26T13:49:44Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümüen_US
dc.description.abstractThe increasing need for food in recent years means that environmental protection and sustainable agriculture are necessary. For this, smart agricultural systems and autonomous robots have become widespread. One of the most significant and persistent problems related to robots is 3D path planning, which is an NP-hard problem, for mobile robots. In this paper, efficient methods are proposed by two metaheuristic algorithms (Incremental Gray Wolf Optimization (I-GWO) and Expanded Gray Wolf Optimization (Ex-GWO)). The proposed methods try to find collision-free optimal paths between two points for robots without human intervention in an acceptable time with the lowest process costs and efficient use of resources in large-scale and crowded farmlands. Thanks to the methods proposed in this study, various tasks such as tracking crops can be performed efficiently by autonomous robots. The simulations are carried out using three methods, and the obtained results are compared with each other and analyzed. The relevant results show that in the proposed methods, the mobile robots avoid the obstacles successfully and obtain the optimal path cost from source to destination. According to the simulation results, the proposed method based on the Ex-GWO algorithm has a better success rate of 55.56% in optimal path cost. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.en_US
dc.identifier.citationKiani, F., Seyyedabbasi, A., Nematzadeh, S., Candan, F., Çevik, T., Anka, F. A., . . . Muzirafuti, A. (2022). Adaptive metaheuristic-based methods for autonomous robot path planning: Sustainable agricultural applications. Applied Sciences (Switzerland), 12(3) doi:10.3390/app12030943en_US
dc.identifier.doi10.3390/app12030943en_US
dc.identifier.issn2076-3417en_US
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85122953049en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/app12030943
dc.identifier.urihttps://hdl.handle.net/20.500.12713/2423
dc.identifier.volume12en_US
dc.identifier.wosWOS:000755331700001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorKiani, Farzad
dc.institutionauthorSeyyedabbasi, Amiren_US
dc.institutionauthorAnka, Fateme Ayşin
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.ispartofApplied Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAutonomous Robotsen_US
dc.subjectClimate Changeen_US
dc.subjectDroneen_US
dc.subjectEnvironmental Monitoringen_US
dc.subjectEnvironmental Protectionen_US
dc.subjectInternet Of Thingsen_US
dc.subjectPath Planningen_US
dc.subjectPhotogrammetryen_US
dc.subjectRemote Sensingen_US
dc.subjectSmart Agricultureen_US
dc.titleAdaptive metaheuristic-based methods for autonomous robot path planning: Sustainable agricultural applicationsen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
applsci-12-00943-v2.pdf
Boyut:
4.41 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Makale dosyası
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu
Siyaset Bilimi ve Kamu Yönetimi Bölümü Makale Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu
Yazılım Mühendisliği Bölümü Makale Koleksiyonu