Robots have begun to populate the everyday environments of human beings. These social robots must perform their tasks without disturbing the people with whom they share their environment. This paper proposes a navigation algorithm for robots that is acceptable to people. Robots will detect the personal areas of humans, to carry out their tasks, generating navigation routes that have less impact on human activities. The main novelty of this work is that the robot will perceive the moods of people to adjust the size of proxemic areas. This work will contribute to making the presence of robots in human-populated environments more acceptable. As a result, we have integrated this approach into a cognitive architecture designed to perform tasks in human-populated environments. The paper provides quantitative experimental results in two scenarios: controlled, including social navigation metrics in comparison with a traditional navigation method, and non-controlled, in robotic competitions where different studies of social robotics are measured.
This paper presents the evolution of a robotic architecture intended for controlling autonomous social robots. The first instance of this architecture was originally designed according to behavior-based principles. The building blocks of this architecture were behaviors designed as a finite state machine and organized in an ethological inspired way. However, the need of managing explicit symbolic knowledge in human–robot interaction required the integration of planning capabilities into the architecture and a symbolic representation of the environment and the internal state of the robot. A major contribution of this paper is the description of the working memory that integrates these two approaches. This working memory has been implemented as a distributed graph. Another contribution is the use of behavior trees instead of state machine for implementing the behavior-based part of the architecture. This late version of the architecture has been tested in robotic competitions (RoboCup or European Robotics League, among others), whose performance is also discussed in this paper.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.