Neurointerfaces for semi-autonomous object moving systems

Abstract This article presents a framework and shows how neural networks can be employed in the generation of human-machine interfaces (neurointerfaces) for real time object moving problems. In a great number of applications, due to technical and economic factors, full automation is not possible. In such cases, the human presence is essential and indeed, the system performance becomes highly dependent on human skills. Accordingly, an interface that modiiies the problem, allowing unskilled human operators to perform the same task in a satisfactory way, becomes extremely useful. The basic concepts and the scope necessary for the problem formulation arc built in a clear framework. The adaptive nonlinear inverse modeling approach is employed as the basic methodology for specification and design of nenrointerfaces A successful application of a neurointerface that helps an operator to back up a scaled truck model connected to a double-trailer configuration is presented