Improved Control of Visually Observed Robotic Agents Based on Autoregressive Model Prediction

Recent investigation on the cooperation of multiple robotic agents with centralized control, has ushered in small and less sophisticated robots that are observed by a global vision system. This paper presents a modeling methodology that enables accurate prediction of such small robot's position and orientation from data measured using the global vision system. Results of several experiments show that the predictions of the model can be used to compensate time delays and improve the transient response of the robots