  This paper addresses a new semantic multi-robot planning problem in uncertain
and dynamic environments. Particularly, the environment is occupied with
non-cooperative, mobile, uncertain labeled targets. These targets are governed
by stochastic dynamics while their current and future positions as well as
their semantic labels are uncertain. Our goal is to control mobile sensing
robots so that they can accomplish collaborative semantic tasks defined over
the uncertain current/future positions and labels of these targets. We express
these tasks using Linear Temporal Logic (LTL). We propose a sampling-based
approach that explores the robot motion space, the mission specification space,
as well as the future configurations of the labeled targets to design optimal
paths. These paths are revised online to adapt to uncertain perceptual
feedback. To the best of our knowledge, this is the first work that addresses
semantic mission planning problems in uncertain and dynamic semantic
environments. We provide extensive experiments that demonstrate the efficiency
of the proposed method
