A neural network (NN) algorithm was implemented to an aqua-robot manipulator control to capture a floating object in water. The NN control performance was evaluated and compared with that of the conventional PD feedback control. The nonlinear dynamics of a floating object in water, caused by approaching motion of the aqua-robot manipulator to the object, was learned in a three-layered neural network. The neural network was implemented in the control system of the aqua-robot manipulator in parallel to a conventional PD feedback controller. The performance of the neural network capture control was experimentally evaluated and found essentially high in terms of capture time needed and capture-success ratio.
Neural network application to capture control in aqua-robot manipulator
Anwendung eines neuronalen Netzes zu Greifregelung beim einem Unterwasser-Robotermanipulator
4 Seiten, 5 Bilder, 3 Tabellen, 2 Quellen
Conference paper
English
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