ClusterNav: Learning-Based Robust Navigation Operating in Cluttered Environments

G. S. Martins, R. P. Rocha, F. J. Pais and P. Menezes

Proc. of IEEE Int. Conf. on Robotics and Automation 2019 (ICRA 2019), pp. 9624-9630, Montreal, Canada, May 20-24, 2019. DOI: 10.1109/ICRA.2019.8794262

Abstract

Robust autonomous navigation is one of the most important aspects in the acceptance of social robots by elderly users. Traditional model-based navigation techniques provide a stable theoretical and practical foundation for autonomous operation in domestic environments, but fall short in achieving human-like, acceptable behaviour while still being able to robustly navigate cluttered environments. In this work, we propose ClusterNav, a novel learning-based technique for navigation. Our technique consists of teaching the robot how it should move in the environment in a human-like manner, capturing key features of this demonstration in a geometric representation of the environment. This representation is then used to generate new trajectories for execution, allowing the robot move in an acceptable manner. We have tested our technique in a real environment in an elderly care facility, comparing it with the traditional model-based approach. Tests involved both expert and non-expert users teleoperating the robot. Results show that ClusterNav is capable of navigating the environment, achieving better similarity with the reference trajectories and higher execution speed when compared to the model-based approach.

Index Terms — Safe navigation; human-like navigation; social robot; learning by demonstration.

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BibTeX

@INPROCEEDINGS(Martins_et_al_19,

AUTHOR = "Martins, G. S. and Rocha, R. P. and Pais, F. J. And Menezes, P.",

TITLE = "\textsc{C}luster\textsc{N}av: Towards Learning-Based Robust Navigation Operating in Cluttered Environments",

BOOKTITLE = "Proc. of IEEE Int. Conf. on Robotics and Automation 2019 (ICRA 2019)",

ADDRESS = " Montreal, Canada",

YEAR = "2019",

MONTH = "May",

PAGES = "9624-9630"

)

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Last update: 22/07/2019