This paper\'s intention is to adapt prediction#N# algorithms well known in the field of time series analysis#N# to problems being faced it the field of mobile robotics and Human-Computer-Interation (HRI). The idea is to predict#N# movement data by understanding it as time series. The#N# prediction takes place with o black box model, which#N# means that no further knowledge on motion dynamics is#N# used then the post of the trajectory itself:This means, the#N# suggested approaches are able to adapt yo different#N# situations. Several state-of-the-art algorithms such as#N# Local Modeling, Cluster Weighted Modeling, Echo State#N# Networks and Autoregressive Models are evaluated and compared. For experiments, real movement trajectories of#N# a human are used. Since mobile robots highly depend on#N# real-time application, computing time is also considered.#N# Experiments show that Echo State Networks and Local#N# Models show impressive results for long term motion#N# prediction with a prediction horizon of up to eight seconds.
['Sven Hellbach', 'Julian Eggert', 'Edgar Kiirner', 'Horst-Michael Gross']
Experimental Evaluation of Approaches for Long Term Prediction of Human Movement Trajectories