Adaptation of rescue robot behaviour in unknown terrains based on stochastic and fuzzy logic approaches

The purpose of this article is to provide rescue robots with an adaptive behaviour during searching for victims in disasters such as fire, earthquake, flood, wars etc. This experimental research work took place in previously unknown dynamic indoor terrains. The main phases of this framework are; 1) modelling of robot behaviours/dynamics in collapsed environments, 2) designing an adaptive controller, which regulates robot longitudinal velocity and heading (collision avoidance) based on the obstacles distribution histogram, 3) prediction of robot behaviours in another unknown terrain. Two approaches have been used to design the adaptive controller: the first one is the stochastic control theory, based on Kalman filter algorithms [10][8]. The second approach relies on fuzzy inference systems (FIS) [5][17][7]. Throughout this work, robot dynamics have been modelled using the auto regressive exogenous (ARX) scheme, while ARX model parameters have been identified using recursive least squares (RLS) [18]. This contribution presents a description and some discussion of the discrete Kalman filter, modelling techniques, and some discussion of robot behaviour analysis. Furthermore, the design of adaptive controllers using FIS-based techniques versus stochastic control systems has been demonstrated. 1