  Planning for multi-robot coverage seeks to determine collision-free paths for
a fleet of robots, enabling them to collectively observe points of interest in
an environment. Persistent coverage is a variant of traditional coverage where
coverage-levels in the environment decay over time. Thus, robots have to
continuously revisit parts of the environment to maintain a desired
coverage-level. Facilitating this in the real world demands we tackle numerous
subproblems. While there exist standard solutions to these subproblems, there
is no complete framework that addresses all of their individual challenges as a
whole in a practical setting. We adapt and combine these solutions to present a
planning framework for persistent coverage with multiple unmanned aerial
vehicles (UAVs). Specifically, we run a continuous loop of goal assignment and
globally deconflicting, kinodynamic path planning for multiple UAVs. We
evaluate our framework in simulation as well as the real world. In particular,
we demonstrate that (i) our framework exhibits graceful coverage given
sufficient resources, we maintain persistent coverage; if resources are
insufficient (e.g., having too few UAVs for a given size of the enviornment),
coverage-levels decay slowly and (ii) planning with global deconfliction in our
framework incurs a negligibly higher price compared to other weaker, more local
collision-checking schemes. (Video: https://youtu.be/aqDs6Wymp5Q)

Robotics;Artificial Intelligence;Systems and Control;Systems and Control