Revisiting Bounded-Suboptimal Safe Interval Path Planning
This work addresses the need for faster path planning in robotics by offering incremental improvements to SIPP, though it is incremental as it builds on existing bounded-suboptimal approaches without a clear universal winner.
The paper tackled the problem of reducing planning time in safe-interval path planning (SIPP) by exploring bounded-suboptimal versions, finding that no single method universally outperforms others but providing insights for method selection based on experimental comparisons.
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.