TL;DR
This paper explores various methods to adapt Safe-Interval Path Planning (SIPP) for bounded-suboptimal solutions, balancing optimality and planning speed, and compares their performance through experiments.
Contribution
It introduces and evaluates different bounded-suboptimal variants of SIPP, providing insights into their advantages and suitable application scenarios.
Findings
No single method is best for all cases.
Different bounded-suboptimal SIPP variants perform variably depending on context.
Experimental results guide method selection based on specific needs.
Abstract
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.
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