Logical sensing and intelligent perception based on rough sets and dynamic decay adjustment learning

This paper presents a logical sensor to be used in the intelligent perception for the motion planning of a mechanical snake searching for rescue in an unstructured environment of collapsed buildings after a natural disaster. The multi-link robot is equipped with ultrasound sensors and a thermal sensor, which detects body heat and isolates any survivor. In this study, the objective is to develop a logical sensing and an intelligent perception model that learns to identify the most suitable zone for control by minimizing the control unpredictability due to uncertainty. This model is based on a radial basis function network trained with dynamic decay adjustment algorithm that classifies the control region into positive regions. Uncertainty irregularities in the control region is modeled using the statistical rough set theory based on beliefs assigned to control cells in a cellular space. Logical sensors are then fused together to construct a wide-angle logical sensor unit in the control zone. Simulation results are given, in order to illustrate the efficiency of the approach developed for an unstructured environment.