Optimal Path Planning Algorithm for Visiting Multiple Mission Points in Dynamic Environments

Lee, Hohyeong; Chang, Won; Jang, Hwanchol

doi:10.5139/jksas.2019.47.5.379

Cited by 2 publications

(1 citation statement)

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“…Salient among graph-based algorithms for goal-directed navigation are the D* [4] and D*Lite [5] algorithms, which have recently been applied to Transportation Networks [7], Multi-Agent Systems [8], and Multiple Mission Points [9]. Both of them are incremental heuristic search algorithms [10], which means that (1) they use heuristic function to guide search at their core, and (2) reuse information gathered in a search episode in subsequent search episodes.…”

Section: Introductionmentioning

confidence: 99%

Multipath Adaptive A*: Factors That Influence Performance in Goal-Directed Navigation in Unknown Terrain

Hernández¹,

Baier

Asin-Acha³

2020

IEEE Access

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Incremental heuristic search algorithms are a class of heuristic search algorithms applicable to the problem of goal-directed navigation. D* and D*Lite are among the most well-known algorithms for this problem. Recently, two new algorithms have been shown to outperform D*Lite in relevant benchmarks: Multi-Path Adaptive A* (MPAA*) and D*ExtraLite. Existing empirical evaluations, unfortunately, do not allow to obtain meaningful conclusions regarding the strengths and weaknesses of these algorithms. Indeed, in the paper introducing D*ExtraLite, it is shown that D*Lite outperforms MPAA* in benchmarks in which the authors of MPAA* claim superiority over D*Lite. The existence of published contradictory data unfortunately does not allow practitioners to make decisions over which algorithm to use given a specific application. In this paper, we analyze two factors that significantly influence the performance of MPAA*, explaining why it is possible to obtain very different results depending on such factors. We identify a configuration of MPAA* which, in the majority of the benchmark problems we use, exhibits superior performance when compared to both D*Lite and D*ExtraLite. We conclude that MPAA* should be the algorithm of choice in goal-directed navigation scenarios in which the heuristic is accurate, whereas D*ExtraLite should be preferred when the heuristic is inaccurate.

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Section: Introductionmentioning

confidence: 99%