  Replanning in temporal logic tasks is extremely difficult during the online
execution of robots. This study introduces an effective path planner that
computes solutions for temporal logic goals and instantly adapts to non-static
and partially unknown environments. Given prior knowledge and a task
specification, the planner first identifies an initial feasible solution by
growing a sampling-based search tree. While carrying out the computed plan, the
robot maintains a solution library to continuously enhance the unfinished part
of the plan and store backup plans. The planner updates existing plans when
meeting unexpected obstacles or recognizing flaws in prior knowledge. Upon a
high-level path is obtained, a trajectory generator tracks the path by dividing
it into segments of motion primitives. Our planner is integrated into an
autonomous mobile robot system, further deployed on a multicopter with limited
onboard processing power. In simulation and real-world experiments, our planner
is demonstrated to swiftly and effectively adjust to environmental
uncertainties.
