MDP-based mission planning for multi-UAV persistent surveillance

This paper presents a methodology to generate task flow for conducting a surveillance mission using multiple UAVs, when the goal is to persistently maintain the uncertainty level of surveillance regions as low as possible. The mission planning problem is formulated as a Markov decision process (MDP), which is a infinite-horizon discrete stochastic optimal control formulation and often leads to a periodic task flows to be implemented in a persistent manner. The method specifically focuses on reducing the size of decision space without losing key feature of the problem in order to mitigate the curse of dimensionality of MDP; integrating a task allocator to identify admissible actions is demonstrate to effectively reduce the decision space. Numerical simulations verify the applicability of the proposed decision scheme.