Robot manipülatörler için akıllı GTGS sistemi

Yıl 2016, Cilt: 22 Sayı: 8, 677 - 684, 27.12.2016

Tolga Yüksel

Öz

Görsel
servolama (GS) yaklaşımları içinde görüntü-tabanlı görsel servolama (GTGS)
duruş kestirimi gerektirmediğinden robot manipülatörler için popüler GS
yaklaşımlarından biridir. Bu popülerliğin yanında GTGS, uygulanması sırasında
ise iki temel sorun ile uğraşır: Etkileşim matrisinin tersinin eldesi ve
kontrolör için uygun bir sabit kazanç değeri bulunması. GTGS için etkileşim
matrisi her ne kadar yalancı tersi ile beraber kullanılsa da tekilliklerin
oluşması durumunda kontrol yasası işleyememektedir. Diğer bir taraftan sabit
kazanç değeri yakınsama hızı ile sonlandırıcı hızları arasında bir ödünleşmeye
sebep olmaktadır. Bu çalışmada bu sorunları çözmek için akıllı bir GTGS sistemi
önerilmiştir. Sistemin ilk aşaması olarak eğitilmiş bir yapay sinir ağı (YSA)
etkileşim matrisinin tersinin yerini almakta ve tekillik sorunu çözülmektedir.
Ayrıca klasik hız kontrolcüsünün sebep olduğu başlangıç hız süreksizliği
yararlanılan sürekli hız kontrolcü ile giderilmiştir.  İkinci aşama olarak sabit kazanç yerine
bulanık kayan kipten esinlenen ve her çevrimde hata ve hata türevinin değerine
göre kazanç hesabı yapan bir bulanık mantık birimi kullanılmıştır. Bu
uyarlanabilir kazanç yaklaşımıyla yüksek hız ihtiyacı olmadan hızlı yakınsama
sağlanmıştır.

Anahtar Kelimeler

Görüntü-tabanlı görsel servolama, Robot manipülatör, Sinir ağı, Bulanık mantık

An intelligent IBVS system for robot manipulators

Öz

Image-Based Visual Servoing (IBVS) is one of the popular
approaches in visual servoing (VS) for robot manipulators by not requiring pose
estimation. Besides this popularity, IBVS has to deal with two common problems
in realization: obtaining the inverse of the interaction matrix and finding an
appropriate fixed gain value for the controller. Although the interaction
matrix for IBVS is used with pseudoinverse, the control law is not applicable
in the case of singularities. On the other hand, fixed gain value causes a
trade-off between convergence speed and end-effector velocities. In this study,
an intelligent IBVS scheme is proposed to solve these problems. As the first
stage of the system, the interaction matrix is replaced with a trained neural
network and the singularity problem has been solved. Furthermore, the
discontinuity of the initial velocities caused by the classical velocity
controller are resolved by the used continuous velocity controller. As the
second stage, instead of a fixed gain, a fuzzy logic unit inspired by fuzzy
sliding mode and computing a gain value according to error and error derivative
values in each loop is considered. Fast convergence without high velocity
demand is provided by this adaptive gain approach.

Anahtar Kelimeler

Image-based visual servoing, Robot manipulator, Neural network, Fuzzy logic

Kaynakça

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