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This paper addresses a safe planning and control problem for mobile robotsoperating in communication- and sensor-limited dynamic environments. In thiscase the robots cannot sense the objects around them and must instead rely onintermittent, external information about the environment, as e.g., inunderwater applications. The challenge in this case is that the robots mustplan using only this stale data, while accounting for any noise in the data oruncertainty in the environment. To address this challenge we propose acompositional technique which leverages neural networks to quickly plan andcontrol a robot through crowded and dynamic environments using onlyintermittent information. Specifically, our tool uses reachability analysis andpotential fields to train a neural network that is capable of generating safecontrol actions. We demonstrate our technique both in simulation with anunderwater vehicle crossing a crowded shipping channel and with realexperiments with ground vehicles in communication- and sensor-limitedenvironments.

Learning Enabled Fast Planning and Control in Dynamic Environments with Intermittent Information