  We present an end-to-end online motion planning framework that uses a
data-driven approach to navigate a heterogeneous robot team towards a global
goal while avoiding obstacles in uncertain environments. First, we use
stochastic model predictive control (SMPC) to calculate control inputs that
satisfy robot dynamics, and consider uncertainty during obstacle avoidance with
chance constraints. Second, recurrent neural networks are used to provide a
quick estimate of future state uncertainty considered in the SMPC finite-time
horizon solution, which are trained on uncertainty outputs of various
simultaneous localization and mapping algorithms. When two or more robots are
in communication range, these uncertainties are then updated using a
distributed Kalman filtering approach. Lastly, a Deep Q-learning agent is
employed to serve as a high-level path planner, providing the SMPC with target
positions that move the robots towards a desired global goal. Our complete
methods are demonstrated on a ground and aerial robot simultaneously (code
available at: https://github.com/AlexS28/SABER).
