Yapay Arı Koloni Algoritması ile Eğitilmiş Tekrarlayıcı Sinir Ağlarının Robot Navigasyonu İçin Kullanılması

Year 2020, Ejosat Special Issue 2020 (ARACONF), 318 - 324, 01.04.2020

Ebru Yönem Rüştü Akay

https://doi.org/10.31590/ejosat.araconf41

Abstract

Örneklere bağlı olarak dinamik öğrenme yetenekleri sayesinde, doğrusal ve doğrusal olmayan ilişkileri çözümleyerek başarılı sonuçlar üreten yapay sinir ağları birçok alanda karşımıza çıkmaktadır. Yapay sinir ağlarında istenen düzeyde performansın sağlanması birçok parametreye bağlı olmakla birlikte, kullanılan ağ modeli ve bu ağın eğitiminde kullanılan algoritmalar üzerinde yapılan çalışmalar giderek artmaktadır. Bu çalışmada, arıların doğada yiyecek arama davranışlarından esinlenilerek geliştirilen yapay arı koloni (Artificial Bee Colony, ABC) algoritması ile eğitilmiş tekrarlayıcı sinir ağlarının (Recurrent Neural Network, RNN) robot navigasyonunda kullanımına yönelik yeni bir tasarım önerilmiştir. Robotun kontrol stratejisi için üzerine yerleştirilen 24 adet ultrasonik sensörden elde edilen veriler kullanılmıştır. Literatürdeki benzer çalışmalarla karşılaştırmak için ortalama karesel hatanın karekökü ve simetrik oransal ortalama mutlak hata ölçüm metrikleri kullanılmıştır. Elde edilen sonuçlar, önerilen tasarım modelinin robotun hareket yönünün tayininde etkin bir şekilde kullanılabileceğini göstermiştir. Özellikle çok sayıda sensör kullanıldığında önerilen modelin performansı diğer modellere nazaran çok daha iyi olmuştur.

Keywords

robot navigasyonu, tekrarlayıcı sinir ağları, yapay arı koloni, akıllı kontrol

Supporting Institution

Erciyes Üniversitesi BAP Koordinasyon Birimi

Project Number

FYL-2017-7662

Thanks

Bu çalışmayı FYL-2017-7662 proje kodu ile destekleyen, Erciyes Üniversitesi BAP Koordinasyon Birimine teşekkür ederiz.

Using Recurrent Neural Network Models Trained With Artificial Bee Colony Algorithm for Robot Navigation

Abstract

Artificial neural networks, which produce successful results by establishing linear and nonlinear relations by their dynamic learning abilities, are used in a wide range of fields. Even if the desired performance of networks depends on too many parameters, the number of researches on networks models and learning algorithms are gradually increasing. In this paper a new recurrent neural network (RNN) trained with artificial bee colony (ABC) algorithm was proposed for robot navigation problem. The RNN network trained with sample dataset, obtained from 24 sensors, was used for control strategy in robot movements. Root mean square error (RMS) and symmetric mean absolute percentage error (SMAPE) evalutian metrics were used to compare the proposed method against state of the art algorithms. The results showed that, the performance of the proposed method in determination of robots motion is good and especially, when a large number of sensors used, the proposed model as better performance than the other models.

Keywords

navigation, recurrent neural network, artificial bee colony, intelligent control

Project Number

FYL-2017-7662

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