Considering the widespread use of mobile robots in different parts of society, it is important to provide them with the capability to behave in a socially acceptable manner. Therefore, a research topic of great importance recently has been the study of Human-Robot Interaction. Autonomous navigation is a fundamental task in Robotics, and several different strategies that produce paths that are either length or time optimized can be found in the literature. However, considering the recent use of mobile robots in a more social context, the use of such classical techniques is restricted. Therefore, in this article we present a social navigation approach considering environments with groups of people. The proposal uses a density function to efficiently represent groups of people, and modify the navigation architecture in order to include the social behaviour of the robot during its motion. This architecture is based on the combined use of the Probabilistic Road Mapping (PRM) and the Rapidly-exploring Random Tree (RRT) path planners and an adaptation of the elastic band algorithm. Experimental evaluation was carried out in different simulated environments, providing insight on the performance of the proposed technique, which surpasses classical techniques with no proxemics awareness in terms of social impact.
|Title of host publication||2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)|
|Publication status||Published - 14 Dec 2017|
|Name||2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)|