  Critical for the coexistence of humans and robots in dynamic environments is
the capability for agents to understand each other's actions, and anticipate
their movements. This paper presents Stochastic Process Anticipatory Navigation
(SPAN), a framework that enables nonholonomic robots to navigate in
environments with crowds, while anticipating and accounting for the motion
patterns of pedestrians. To this end, we learn a predictive model to predict
continuous-time stochastic processes to model future movement of pedestrians.
Anticipated pedestrian positions are used to conduct chance constrained
collision-checking, and are incorporated into a time-to-collision control
problem. An occupancy map is also integrated to allow for probabilistic
collision-checking with static obstacles. We demonstrate the capability of SPAN
in crowded simulation environments, as well as with a real-world pedestrian
dataset.
