Current autonomous mobile robots are able to navigate accurately through sparsely populated areas without bumping into things. However, they have more trouble in situations that commonly arise in public buildings, such as when passing people in narrow hallways, when moving through open, populated spaces, or when crossing a crowd of people exiting an auditorium. Thus, for autonomous robots to reach their full potential, in terms of positive impact on society, they will need to improve their navigational abilities to be more "human-aware." That is, they will explicitly need to take account the characteristics of the people with whom they need to interact in public spaces. With this motivation in mind, the goal of this research is to understand how best to enable mobile robots to navigate smoothly, robustly, and safely through human-populated indoor environments in pursuit of high-level goals, with varying levels of guidance from a human operator in a fully human-aware manner.
This project focuses on two complementary, high-risk, and potentially foundational research thrusts as being crucial to laying the groundwork for eventual development of a robust, human-aware navigation system. First, it aims to develop formal specifications for safe robot-operator-pedestrian interactions, using probabilistic temporal logics. Second, it aims to develop methods for generating learned models of operator preferences that can influence the robot's choice of paths with regards to, for example, trajectory smoothness, order of subgoal achievement, task completion time, travel speed, and proximity of trajectory to pedestrians and fixed objects, learning user preferences and determining how to combine them with task-achieving reward functions.
