Abstract:In this paper it is presented an integration of algorithms that permits maps construction and navigation of mobile robots. Simultaneous Localization and Mapping (SLAM) algorithm is used based on FastSLAM method. Navigation system is based on Vector Field Histogram algorithm to avoid obstacle and a spiral way trajectory method. Up to three different complex simulation maps have been used to evaluate the system.

This paper describes experimental results applying artificial neural networks to perform the position control of a real scara manipulator robot. The general control strategy consists of a neural cantroller that operates in parallel with a conventional controller based on the feedback e r o r learning architecture, The main advantage of this architecture is that it does not require any modification of the previous conventional controller algorithm. MLP and RBF neural networks trained online have been used, without requiring any previous knowledge about the system to be controlled. The approach has performed very successfully, with better results obtained with the REF networks when compared to PID and sliding mode positional controllers.

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This paper describes experimental results achieved applying artificial neural networks (NNs) trying to perform force/position control of a real scara manipulator robot. The general control strategy consists of a neural controller that operates in parallel with a conventional controller based on feedback error learning architecture. There are used two NNs: one for the position loop control and another to the force loop control based on the structure knew as hybrid force/position control. The main purpose was lets the NNs compensates dynamical effects that arises when a manipulator is in contact with an environment. Successfully results have achieved for the position loop control but practical problems was observed related to force loop control using NNs even with different sets of input data for this NN. Practical problems are discussed.

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