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Optimum Enerji Verimliliğini Hedefleyen Rastgele Ağaçlar ve Yapay Arı Kolonisi Yöntemi ile Otonom Robotlarda Yol Planlama Algoritması

Yıl 2019, Cilt: 7 Sayı: 4, 903 - 915, 24.12.2019
https://doi.org/10.29109/gujsc.607996

Öz

Operatörüz
hareket edebilen robotlarda (otonom robotlar) hareket sırasında engellere
çarpmadan, en kısa yol ve en yumuşak yolu seçerek hedef konumuna ulaşması büyük
önem taşımaktadır. Bu çalışımda, yol planlama eylemi sezgisel ve klasik
yöntemlerinin avantajlarını birleştirmek dezavantajlarını minimize etmek için
iki yöntemin melez kullanımı ile gerçekleştirilmiştir. Klasik yöntemlerden
Rastgele ağaçlar yöntemi (Rapidly-exploring Random Tree-RRT) ve sezgisel
yöntemlerden de Yapay Arı Kolonisi yöntemi (Artificial bee colony-ABC) ayrı
ayrı kullanılarak ve daha sonra melez bir yaklaşımla, önceden keşfedilmiş,
başlangıç ve hedef noktası belli haritada optimum yol, MATLAB’ da Robotik
Sistem Araç Kutusu (Robotic System Toolbox) üzerinden benzetimi
gerçekleştirilmiştir. Sunulan melez algoritmada alınan yol hesaplanırken enerji
verimliği ile birlikte yol güvenliği de dikkate alınmıştır. İki tekerli mobil
robotun enerji tüketimini RRT, ABC ve melez RRT-ABC yöntemlerinin kullanılması
ile elde edilen yollarda hesaplanmış ve karşılaştırılmıştır. Yapılan karşılaştırmalar
sonucunda melez algoritmanın daha verimli çalıştığı gözlemlenmiştir.

Kaynakça

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Toplam 41 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Tasarım ve Teknoloji
Yazarlar

Yunis Torun 0000-0002-6187-0451

Züleyha Ergül Bu kişi benim 0000-0002-7108-8930

Ahmet Aksöz 0000-0002-2563-1218

Yayımlanma Tarihi 24 Aralık 2019
Gönderilme Tarihi 20 Ağustos 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 7 Sayı: 4

Kaynak Göster

APA Torun, Y., Ergül, Z., & Aksöz, A. (2019). Optimum Enerji Verimliliğini Hedefleyen Rastgele Ağaçlar ve Yapay Arı Kolonisi Yöntemi ile Otonom Robotlarda Yol Planlama Algoritması. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji, 7(4), 903-915. https://doi.org/10.29109/gujsc.607996

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