A new human-aware robot navigation framework based on time-dependent social interaction spaces: An application to assistive robots in caregiving centers

One of the most critical problems to be addressed by future generation socially-assistive robots working in semi-organized social environments, such as shopping centers, nursing homes, airports, hospitals, or assisted living centers, is the capability of human-aware navigation. Autonomous navigation in a complex environment with people, staff with different roles, timetables, and restrictions to access, among others, requires adapting to socially accepted rules. Consequently, the path-planner must consider concepts related to proxemics and personal spaces of interaction that include human- human, human-robot, or human-object combinations. Likewise, the speed of approaching people, both to initiate communication or to navigate nearby, must be adapted to social conventions. Some of these situations have already been studied in the literature with varying degrees of success. However, the concept of time dependency or chronemics in the robot social navigation has been poorly explored. Current algorithms do not take into account the social complexity of real environments and their relationship with the time of day or the activities performed in these scenarios. This article presents a new framework for robot social navigation in human environments, introducing the concept of time dependent social mapping. The main novelty is that the social route planned by the robot considers variables that depend on the time and the scheduled center activities. The article describes how the areas of interaction vary over time and how they affect human-aware navigation. To this end, the proposed navigation stack defines a new function for time-dependent social interaction space that takes continuous values and is configurable by the center's staff. The global path-planner uses this function to choose dynamically a socially accepted path to the target. Then, the framework uses an elastic band path optimizer as a local planner, adapting the robot's navigation to possible changes during the trajectory. Several use cases in simulated caregiving centers have been explored to validate the robot's social navigation improvements using these temporal variables. (c) 2021 Elsevier B.V. All rights reserved.