Due to the high complexity of probabilistic planning algorithms, roboticists often opt for deterministic replanning paradigms, which can quickly adapt the current plan to the environment's changes. However, probabilistic planning suffers in practice from the common misconception that it is needed to generate complete or closed policies, which would not require to be adapted on-line. In this work, we propose an intermediate approach, which generates incomplete partial policies taking into account mid-term probabilistic uncertainties, continually improving them on a gliding horizon or regenerating them when they fail. Our algorithm is a configurable anytime meta-planner that drives any sub-(PO)MDP standard planner, dealing with all pending and time-bounded planning requests sent by the execution framework from many reachable possible future execution states, in anticipation of the probabilistic evolution of the system. We assess our approach on generic robotic problems and on combinatorial UAVs (PO)MDP missions, which we tested during real flights: emergency landing with discrete and continuous state variables, and target detection and recognition in unknown environments.