Multipath Adaptive A*: Factors That Influence Performance in Goal-Directed Navigation in Unknown Terrain Article Swipe
Carlos Hernández
,
Jorge A. Baier
,
Roberto Asín‐Achá
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.3003344
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.3003344
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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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2020.3003344
- https://ieeexplore.ieee.org/ielx7/6287639/8948470/09120005.pdf
- OA Status
- gold
- Cited By
- 2
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3036555975
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3036555975Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2020.3003344Digital Object Identifier
- Title
-
Multipath Adaptive A*: Factors That Influence Performance in Goal-Directed Navigation in Unknown TerrainWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-01Full publication date if available
- Authors
-
Carlos Hernández, Jorge A. Baier, Roberto Asín‐AcháList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2020.3003344Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/8948470/09120005.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/6287639/8948470/09120005.pdfDirect OA link when available
- Concepts
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Computer science, Heuristic, Benchmark (surveying), Path (computing), Machine learning, Artificial intelligence, Mathematical optimization, Algorithm, Mathematics, Geodesy, Geography, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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26Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.given | 112 |
| abstract_inverted_index.paper | 71 |
| abstract_inverted_index.shown | 36, 76 |
| abstract_inverted_index.these | 66 |
| abstract_inverted_index.which | 84, 108, 185 |
| abstract_inverted_index.choice | 179 |
| abstract_inverted_index.obtain | 57, 136 |
| abstract_inverted_index.paper, | 118 |
| abstract_inverted_index.search | 2, 9 |
| abstract_inverted_index.should | 174, 192 |
| abstract_inverted_index.which, | 150 |
| abstract_inverted_index.Indeed, | 68 |
| abstract_inverted_index.analyze | 120 |
| abstract_inverted_index.authors | 86 |
| abstract_inverted_index.factors | 122 |
| abstract_inverted_index.problem | 14 |
| abstract_inverted_index.results | 139 |
| abstract_inverted_index.whereas | 190 |
| abstract_inverted_index.Adaptive | 44 |
| abstract_inverted_index.Existing | 49 |
| abstract_inverted_index.compared | 164 |
| abstract_inverted_index.conclude | 171 |
| abstract_inverted_index.exhibits | 160 |
| abstract_inverted_index.factors. | 143 |
| abstract_inverted_index.identify | 145 |
| abstract_inverted_index.majority | 153 |
| abstract_inverted_index.possible | 134 |
| abstract_inverted_index.problem. | 29 |
| abstract_inverted_index.problems | 157 |
| abstract_inverted_index.relevant | 41 |
| abstract_inverted_index.specific | 114 |
| abstract_inverted_index.superior | 161 |
| abstract_inverted_index.Recently, | 30 |
| abstract_inverted_index.accurate, | 189 |
| abstract_inverted_index.algorithm | 109, 177 |
| abstract_inverted_index.benchmark | 156 |
| abstract_inverted_index.decisions | 106 |
| abstract_inverted_index.depending | 140 |
| abstract_inverted_index.different | 138 |
| abstract_inverted_index.empirical | 50 |
| abstract_inverted_index.existence | 94 |
| abstract_inverted_index.heuristic | 1, 8, 187, 197 |
| abstract_inverted_index.influence | 125 |
| abstract_inverted_index.preferred | 194 |
| abstract_inverted_index.published | 96 |
| abstract_inverted_index.regarding | 60 |
| abstract_inverted_index.scenarios | 183 |
| abstract_inverted_index.strengths | 62 |
| abstract_inverted_index.Multi-Path | 43 |
| abstract_inverted_index.algorithms | 3, 10, 26, 33 |
| abstract_inverted_index.applicable | 11 |
| abstract_inverted_index.benchmarks | 82 |
| abstract_inverted_index.explaining | 130 |
| abstract_inverted_index.meaningful | 58 |
| abstract_inverted_index.navigation | 182 |
| abstract_inverted_index.outperform | 38 |
| abstract_inverted_index.weaknesses | 64 |
| abstract_inverted_index.well-known | 25 |
| abstract_inverted_index.Incremental | 0 |
| abstract_inverted_index.algorithms. | 67 |
| abstract_inverted_index.benchmarks: | 42 |
| abstract_inverted_index.conclusions | 59 |
| abstract_inverted_index.inaccurate. | 199 |
| abstract_inverted_index.introducing | 72 |
| abstract_inverted_index.navigation. | 17 |
| abstract_inverted_index.outperforms | 79 |
| abstract_inverted_index.performance | 127, 162 |
| abstract_inverted_index.superiority | 90 |
| abstract_inverted_index.application. | 115 |
| abstract_inverted_index.evaluations, | 51 |
| abstract_inverted_index.A* | 45 |
| abstract_inverted_index.D* | 18 |
| abstract_inverted_index.configuration | 147 |
| abstract_inverted_index.contradictory | 97 |
| abstract_inverted_index.goal-directed | 16, 181 |
| abstract_inverted_index.practitioners | 103 |
| abstract_inverted_index.significantly | 124 |
| abstract_inverted_index.unfortunately | 99 |
| abstract_inverted_index.unfortunately, | 52 |
| abstract_inverted_index.MPAA* | 80, 88, 149, 173 |
| abstract_inverted_index.D*Lite | 20, 39, 78, 167 |
| abstract_inverted_index.MPAA*, | 129 |
| abstract_inverted_index.(MPAA*) | 46 |
| abstract_inverted_index.D*Lite. | 92 |
| abstract_inverted_index.D*ExtraLite | 191 |
| abstract_inverted_index.D*ExtraLite, | 73 |
| abstract_inverted_index.D*ExtraLite. | 48, 169 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.39739174 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |