  We present a context classification pipeline to allow a robot to change its
navigation strategy based on the observed social scenario. Socially-Aware
Navigation considers social behavior in order to improve navigation around
people. Most of the existing research uses different techniques to incorporate
social norms into robot path planning for a single context. Methods that work
for hallway behavior might not work for approaching people, and so on. We
developed a high-level decision-making subsystem, a model-based context
classifier, and a multi-objective optimization-based local planner to achieve
socially-aware trajectories for autonomously sensed contexts. Using a context
classification system, the robot can select social objectives that are later
used by Pareto Concavity Elimination Transformation (PaCcET) based local
planner to generate safe, comfortable, and socially-appropriate trajectories
for its environment. This was tested and validated in multiple environments on
a Pioneer mobile robot platform; results show that the robot was able to select
and account for social objectives related to navigation autonomously.

Robotics