Research Topic: Intelligent Control for Robotic Contact Tasks

Primary aim of the research in this area is the synthesis of intelligent robotic controllers for contact tasks, as well as the application in the domain of locomotion robotic mechanisms. The synthesis of new control algorithms based on adaptive learning systems - neural networks is performed. The various forms of neural network training and different topologies of neural networks for efficient realization of robotic contact and non-contact tasks are proposed. Good results are achieved in the application of neural networks for robotic contact tasks where the problem of uncertainties of the robot model and parameters, as well as uncertainties of the dynamic environment is very important. Very interesting and applicable results are obtained using hybrid control method based on combination of conventional control techniques and soft computing paradigms (neural networks, uzzy logics, genetic algorithms). This complementary approach is successfully applied to manipulation robots and locomotion robots. The proposed intelligent techniques which are analyzed for a broader range of contact robotic tasks, show an important advantage in comparison with conventional techniques of robot compliance control.

Wavelet network classifier for  advanced  learning control of robotic contact tasks

Fig. 1. Wavelet network classifier for advanced learning control of robotic contact tasks.

Connectionist Reactive Control by Robot Hand with soft grippers

Fig. 2. Connectionist Reactive Control by Robot Hand with soft grippers.