Description

Deploying mobile robots in social environments requires novel navigation algorithms which are capable of providing valid solutions in such challenging scenarios. The main objective of the present dissertation is to develop new robot navigation approaches able to solve in an intelligent way the navigation problem in urban settings while considering at the same time the interactions with pedestrians, similar to what people easily do with little attention.

Before studying in depth navigation algorithms, this thesis focuses on prediction algorithms to provide a more detailed model of the scene. Understanding human motion in outdoor and indoor scenarios is an appealing requirement to characterize correctly urban settings. Urban environments consist essentially of static obstacles and people, which are treated as dynamic and highly uncertain obstacles. Accordingly, it is mandatory to calculate people's intentions in order to
successfully build a human prediction model that generates the corresponding human trajectories and considers their interactions with the environment, such as other pedestrians, static obstacles or even robots.

It is of great interest that service robots can navigate successfully in typical urban environments, which are dynamic and constrained. In addition, people's behavior should not be conditioned by the presence and the maneuvering of robots. To this end, the robot navigation should seek to minimize its impact on the environment, in our case, on people.

This thesis proposes new robot navigation methods that contemplate the social interactions taking place in the scene.
In order to procure more intelligence to the navigation algorithm, we propose to integrate seamlessly the human motion prediction information into a new robot planning approach.

Real experimentation is essential for the validation of the navigation algorithms. As there are real people involved, we must validate the results in real settings since simulation environments have limitations. In this thesis, we have implemented all the prediction and navigation algorithms in our robotic platform and we have provided plenty of evaluations and testings of our algorithms in real settings.

The work is under the scope of the following projects:

• MIPRCV: CONSOLIDER-INGENIO 2010 Multimodal interaction in pattern recognition and computer vision (web)