  Robots need task planning algorithms to sequence actions toward accomplishing
goals that are impossible through individual actions. Off-the-shelf task
planners can be used by intelligent robotics practitioners to solve a variety
of planning problems. However, many different planners exist, each with
different strengths and weaknesses, and there are no general rules for which
planner would be best to apply to a given problem.
  In this article, we empirically compare the performance of state-of-the-art
planners that use either the Planning Domain Description Language (PDDL), or
Answer Set Programming (ASP) as the underlying action language. PDDL is
designed for task planning, and PDDL-based planners are widely used for a
variety of planning problems. ASP is designed for knowledge-intensive
reasoning, but can also be used for solving task planning problems. Given
domain encodings that are as similar as possible, we find that PDDL-based
planners perform better on problems with longer solutions, and ASP-based
planners are better on tasks with a large number of objects or in which complex
reasoning is required to reason about action preconditions and effects. The
resulting analysis can inform selection among general purpose planning systems
for particular robot task planning domains.
