Abstract
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.
Highlights
We focus on social robots that operate in human-populated environments and can interact with them
This paper is structured as follows: In Section 2, we present all the works we consider relevant in the area of social robotics
This paper focuses on improving the comfort [17] of people creating dynamic proxemic zones that are modified by different parameters, i.e., attitude towards the robot, age, or presenting behavior that indicates the intention of interact with it
Summary
We focus on social robots that operate in human-populated environments and can interact with them. It is not yet common to live with a robot every day, this will soon be a reality. There is still much to do to make these robots acceptable in our daily life. The main objective of a navigation system is to allow a robot to move from one point to another in the environment safely and efficiently. We focus on cases where the environment is known a priori, and coded on a map. Having a reliable and stable location is critical to the success of navigation. Most of the successful navigation approaches divide the navigation modules into two levels: global planner and local planner
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