No abstract

No abstract

Though acoustic perception is well established in literature, it seems to be insufficiently implemented in practice. Experimental results are excellent but a lot of issues arise when it comes to the application in real conditions. Using artificial neural networks makes acoustic signal processing very comfortable from the mathematical point of view. However, a great job has to be done in order to make it possible. The procedure includes data acquisition, filtering, feature extraction and selection. These techniques require much more resources than mere artificial neural networks and this represents a limiting factor for the implementation. The paper investigates the complete procedure of acoustic perception, in terms of time, in order to identify limitations.

The experiment investigated the performance of an artificial neural network in solving the inverse kinematic problem of a soft robot. For this purpose, a simple soft robot was designed of building blocks, stringed on three rubber hoses, and an actuating system, to provide the hydraulic pressure. An axial extending of a hose, while the others are in the relaxed state, results in bending of the robot. The network was employed, as a black box, to approximate the behavior of the system. In accordance with the purpose, the input consisted of the desired spatial coordinates and the output of the step motor angular displacements. The network was trained and tested using records collected at 200 randomly chosen robot positions. The relative testing error of positioning, about 5%, confirmed a predictable robot behavior. The solution proposed is competitive in terms of simplicity, safety and price of realization. The experiment provided basics for the future research of the design of modular soft robots.