  Safe-interval path planning (SIPP) is a powerful algorithm for finding a path
in the presence of dynamic obstacles. SIPP returns provably optimal solutions.
However, in many practical applications of SIPP such as path planning for
robots, one would like to trade-off optimality for shorter planning time. In
this paper we explore different ways to build a bounded-suboptimal SIPP and
discuss their pros and cons. We compare the different bounded-suboptimal
versions of SIPP experimentally. While there is no universal winner, the
results provide insights into when each method should be used.
