Data-based dynamical modeling of externally observed actuators-only robots

This paper describes the derivation of dynamical models for mobile robots using system-identification methods and shows how such models can be used in the control of autonomous devices. Unlike traditional approaches that are based on the system physics alone, in this paper, an autoregressive model structure is derived from the physics of the process, and its parameters are estimated using real input and output data. From the resulting model, efficient control can be obtained for centralized and remotely controlled robots (typically externally observed) with little or no local "intelligence," thus overcoming many of the severe shortcomings presented by such systems. System dead time is one example of a serious problem that can be efficaciously minimized by using the model as an online predictor. Several experimental results using small differential-driven robots are included to demonstrate the applicability and robustness of the suggested procedure in actual real-world scenarios.