  We introduce a parallel offline algorithm for computing hybrid conditional
plans, called HCP-ASP, oriented towards robotics applications. HCP-ASP relies
on modeling actuation actions and sensing actions in an expressive nonmonotonic
language of answer set programming (ASP), and computation of the branches of a
conditional plan in parallel using an ASP solver. In particular, thanks to
external atoms, continuous feasibility checks (like collision checks) are
embedded into formal representations of actuation actions and sensing actions
in ASP; and thus each branch of a hybrid conditional plan describes a feasible
execution of actions to reach their goals. Utilizing nonmonotonic constructs
and nondeterministic choices, partial knowledge about states and
nondeterministic effects of sensing actions can be explicitly formalized in
ASP; and thus each branch of a conditional plan can be computed by an ASP
solver without necessitating a conformant planner and an ordering of sensing
actions in advance. We apply our method in a service robotics domain and report
experimental evaluations. Furthermore, we present performance comparisons with
other compilation based conditional planners on standardized benchmark domains.
This paper is under consideration for acceptance in TPLP.
