  This paper addresses a safe planning and control problem for mobile robots
operating in communication- and sensor-limited dynamic environments. In this
case the robots cannot sense the objects around them and must instead rely on
intermittent, external information about the environment, as e.g., in
underwater applications. The challenge in this case is that the robots must
plan using only this stale data, while accounting for any noise in the data or
uncertainty in the environment. To address this challenge we propose a
compositional technique which leverages neural networks to quickly plan and
control a robot through crowded and dynamic environments using only
intermittent information. Specifically, our tool uses reachability analysis and
potential fields to train a neural network that is capable of generating safe
control actions. We demonstrate our technique both in simulation with an
underwater vehicle crossing a crowded shipping channel and with real
experiments with ground vehicles in communication- and sensor-limited
environments.
