  This paper proposes a new structured method for a moving agent to predict the
paths of dynamically moving obstacles and avoid them using a risk-aware model
predictive control (MPC) scheme. Given noisy measurements of the a priori
unknown obstacle trajectory, a bootstrapping technique predicts a set of
obstacle trajectories. The bootstrapped predictions are incorporated in the MPC
optimization using a risk-aware methodology so as to provide probabilistic
guarantees on obstacle avoidance. We validate our methods using simulations of
a 3-dimensional multi-rotor drone that avoids various moving obstacles, such as
a thrown ball and a frisbee with air drag.
