Neural Trajectory Optimization (NTO) for Manipulator Tracking of Unknown Surfaces

We developed a novel approach for surface tracking and force/position control based on neural networks. A new concept of neural trajectory optimization NTO will be presented as a part of the neural force/position control NFC and as a very capable and versatile tool for the generation of natural manipulator movements (fast, flexible and smooth). The NTO concept is based on DRBF neural networks, an extension of the RBF type network, to be proposed in this paper. Experimental results of the realtime implementation of NTO and simulation results of its combination with the neural force/position control NFC will be presented. As a testbed we use a 6 DOF industrial manipulator executing demanding tasks such as surface tracking with defined normal force.1

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