Task Characterisation and Cross-Platform Programming Through System Identification

Developing robust and reliable control code for autonomous mobile robots is difficult, because the interaction between a physical robot and the environment is highly complex, it is subject to noise and variation, and therefore partly unpredictable. This means that to date it is not possible to predict robot behaviour, based on theoretical models. Instead, current methods to develop robot control code still require a substantial trial-and-error component to the software design process. Such iterative refinement could be reduced, we argue, if a more profound theoretical understanding of robot-environment interaction existed. In this paper, we therefore present a modelling method that generates a faithful model of a robot's interaction with its environment, based on data logged while observing a physical robot's behaviour. Because this modelling method — nonlinear modelling using polynomials — is commonly used in the engineering discipline of system identification, we refer to it here as “robot identification”. We show in this paper that using robot identification to obtain a computer model of robot-environment interaction offers several distinct advantages: Very compact representations (one-line programs) of the robot control program are generated The model can be analysed, for example through sensitivity analysis, leading to a better understanding of the essential parameters underlying the robot's behaviour, and The generated, compact robot code can be used for cross-platform robot programming, allowing fast transfer of robot code from one type of robot to another. We demonstrate these points through experiments with a Magellan Pro and a Nomad 200 mobile robot