ClusterNav: Learning-Based Robust Navigation Operating in Cluttered Environments

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.