Anticipating Human Collision Avoidance Behavior for Safe Robot Reaction
Kelsey P. Hawkins, Panagiotis Tsiotras · 2018
For robots to effectively navigate in the presence of humans, they must safely leverage the human's perceived unwillingness to collide. Drawing on Viability Theory, we propose a novel approach to robustly anticipate human collision-avoiding behavior. We assume that rational humans try to optimally control their motion to avoid collision, but they are also prone to error, which makes their behavior suboptimal. We offer a robust control model which varies the level of optimality expected over time, assuming that humans may act unpredictably for a brief period of time, but their actions approach optimal collision-avoiding behavior as time progresses. We show how the proposed model can be used to produce a set of initial states for which a rational human will avoid collision. Further, we produce a robust policy which characterizes the set of control inputs expected by the human at any state. We illustrate our approach using two representative scenarios.