Traditionally robots are mostly known by society due to the wide use of manipulators, which are generally placed in controlled environments such as factories. However, with the advances in the area of mobile robotics, they are increasingly inserted into social contexts, i.e., in the presence of people. The adoption of socially acceptable behaviours demands a trade-off between social comfort and other metrics of efficiency. For navigation tasks, for example, humans must be differentiated from other ordinary objects in the scene. In this work, we propose a novel human-aware navigation strategy built upon the use of an adaptive spatial density function that efficiently cluster groups of people according to their spatial arrangement. Space affordances are also used for defining potential activity spaces considering the objects in the scene. The proposed function defines regions where navigation is either discouraged or forbidden. To implement a socially acceptable navigation, the navigation architecture combines a probabilistic roadmap and rapidly-exploring random tree path planners, and an adaptation of the elastic band algorithm. Trials in real and simulated environments carried out demonstrate that the use of the clustering algorithm and social rules in the navigation architecture do not hinder the navigation performance.
Bibliographical note© 2018 Published by Elsevier B.V. Licenced under CC BY NC ND.
Funding: MICINN Project TIN2015-65686-C5-5-R, by the Extremaduran Goverment project GR15120, by the Red de Excelencia ”Red de Agentes Físicos” TIN2015-71693-REDT, FEDER project 0043-EUROAGE-4-E (Interreg V-A Portugal-Spain (POCTEP) Program), MEC project PHBP14/00083 and by CAPES-DGPU 7523/14-9.
- Social Navigation