Information-guided persistent monitoring under temporal logic constraints

We study the problem of planning the motion of an agent such that it maintains indefinitely a high-quality estimate of some a priori unknown feature, such as traffic levels in an urban environment. Persistent operation requires that the agent satisfy motion constraints, such as visiting charging stations infinitely often, which are readily described by rich linear temporal logic (LTL) specifications. We propose and evaluate via simulation a two-level dynamic programming algorithm that is guaranteed to satisfy given LTL constraints. The low-level path planner implements a receding horizon algorithm that maximizes the local information gathering rate. The high-level planner selects inputs to the low-level planner based on global performance considerations.