Path-finding in dynamic environments with PDDL-planners

Abstract

The standardization of planning problems by their descriptions in the PDDL has resulted in clear benchmarking of planners, and thus, in significant advances in reliable and efficient planning packages. The output of these classical planners is a plan as sequence of actions for the controllable robots in the environment. We show here that, provided that the adversaries follow a deterministic behavior, PDDL-planners can also be used in dynamic environments where uncontrollable adversaries may obstruct paths at some time in the future. Therefore, these environments can be used by mobile robots without the need to use more sophisticated planners where environments are modeled by Markov Decision Processes (MDPs). We created a planning API for integrating any PDDL-solver and use it to elaborate platform independent planning behavior. We also have the ability of switching between PDDL-solvers or to change the integration cycle of the planner. We show that these two features are essential for the dynamic environments considered here.