It is widely accepted that in the future, robots will cooperate with humans in everyday tasks. Robots interacting with humans will require social awareness when performing their tasks which will require navigation. While navigating, robots should aim to avoid distressing people in order to maximize their chance of social acceptance. For instance, avoiding getting too close to people or disrupting interactions. Most research approaches these problems by planning socially accepted paths, however, in everyday situations, there are many examples where a simple path planner cannot solve all of the predicted robots’ navigation problems. For instance, requesting permission to interrupt a conversation if an alternative path cannot be determined requires deliberative skills. This article presents the Social Navigation framework for Autonomous robots in Populated Environments (SNAPE), where different software agents are integrated within a robotics cognitive architecture. SNAPE addresses action planning aimed at social-awareness navigation in realistic situations: it plans socially accepted paths and conversations to negotiate its trajectory to reach targets. In this article, the framework is evaluated in different use-cases where the robot, during its navigation, has to interact with different people in order to reach its goal. The results show that participants report that the robot’s behavior was realistic and human-like.
|Title of host publication||2021 IEEE International Conference on Robotics and Automation (ICRA)|
|Number of pages||6|
|Publication status||Published - 18 Oct 2021|
|Event||2021 IEEE International Conference on Robotics and Automation (ICRA) - Xi'an, China|
Duration: 30 May 2021 → 5 Jun 2021
|Conference||2021 IEEE International Conference on Robotics and Automation (ICRA)|
|Period||30/05/21 → 5/06/21|
Bibliographical noteFunding: This work has been partially supported by the Extremaduran Goverment project GR15120, IB18056, and by the MICINN project RTI2018-099522-B-C42.
- Computer architecture
- Software agents
- Task analysis