Multi-Objective and Model-Predictive Tree Search for Spatiotemporal Informative Planning Article Swipe
Weizhe Chen
,
Lantao Liu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.09608
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.09608
Adaptive sampling and planning in robotic environmental monitoring are challenging when the target environmental process varies over space and time. The underlying environmental dynamics require the planning module to integrate future environmental changes so that action decisions made earlier do not quickly become outdated. We propose a Monte Carlo tree search method which not only well balances the environment exploration and exploitation in space, but also catches up to the temporal environmental dynamics. This is achieved by incorporating multi-objective optimization and a look-ahead model-predictive rewarding mechanism. We show that by allowing the robot to leverage the simulated and predicted spatiotemporal environmental process, the proposed informative planning approach achieves a superior performance after comparing with other baseline methods in terms of the root mean square error of the environment model and the distance to the ground truth.
Related Topics
Concepts
Leverage (statistics)
Computer science
Tree (set theory)
Baseline (sea)
Monte Carlo method
Process (computing)
Sampling (signal processing)
Machine learning
Artificial intelligence
Data mining
Statistics
Mathematics
Geology
Operating system
Oceanography
Mathematical analysis
Computer vision
Filter (signal processing)
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2306.09608
- https://arxiv.org/pdf/2306.09608
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4381247906
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4381247906Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2306.09608Digital Object Identifier
- Title
-
Multi-Objective and Model-Predictive Tree Search for Spatiotemporal Informative PlanningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-16Full publication date if available
- Authors
-
Weizhe Chen, Lantao LiuList of authors in order
- Landing page
-
https://arxiv.org/abs/2306.09608Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2306.09608Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2306.09608Direct OA link when available
- Concepts
-
Leverage (statistics), Computer science, Tree (set theory), Baseline (sea), Monte Carlo method, Process (computing), Sampling (signal processing), Machine learning, Artificial intelligence, Data mining, Statistics, Mathematics, Geology, Operating system, Oceanography, Mathematical analysis, Computer vision, Filter (signal processing)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4381247906 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2306.09608 |
| ids.doi | https://doi.org/10.48550/arxiv.2306.09608 |
| ids.openalex | https://openalex.org/W4381247906 |
| fwci | |
| type | preprint |
| title | Multi-Objective and Model-Predictive Tree Search for Spatiotemporal Informative Planning |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10848 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9879999756813049 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1703 |
| topics[0].subfield.display_name | Computational Theory and Mathematics |
| topics[0].display_name | Advanced Multi-Objective Optimization Algorithms |
| topics[1].id | https://openalex.org/T10586 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9876000285148621 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1707 |
| topics[1].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[1].display_name | Robotic Path Planning Algorithms |
| topics[2].id | https://openalex.org/T10462 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9488999843597412 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Reinforcement Learning in Robotics |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C153083717 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8035879135131836 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q6535263 |
| concepts[0].display_name | Leverage (statistics) |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.6586540341377258 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C113174947 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5659822225570679 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q2859736 |
| concepts[2].display_name | Tree (set theory) |
| concepts[3].id | https://openalex.org/C12725497 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5566980242729187 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q810247 |
| concepts[3].display_name | Baseline (sea) |
| concepts[4].id | https://openalex.org/C19499675 |
| concepts[4].level | 2 |
| concepts[4].score | 0.45461615920066833 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q232207 |
| concepts[4].display_name | Monte Carlo method |
| concepts[5].id | https://openalex.org/C98045186 |
| concepts[5].level | 2 |
| concepts[5].score | 0.452033668756485 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q205663 |
| concepts[5].display_name | Process (computing) |
| concepts[6].id | https://openalex.org/C140779682 |
| concepts[6].level | 3 |
| concepts[6].score | 0.42727091908454895 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q210868 |
| concepts[6].display_name | Sampling (signal processing) |
| concepts[7].id | https://openalex.org/C119857082 |
| concepts[7].level | 1 |
| concepts[7].score | 0.42182695865631104 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[7].display_name | Machine learning |
| concepts[8].id | https://openalex.org/C154945302 |
| concepts[8].level | 1 |
| concepts[8].score | 0.40194523334503174 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[8].display_name | Artificial intelligence |
| concepts[9].id | https://openalex.org/C124101348 |
| concepts[9].level | 1 |
| concepts[9].score | 0.3380521535873413 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[9].display_name | Data mining |
| concepts[10].id | https://openalex.org/C105795698 |
| concepts[10].level | 1 |
| concepts[10].score | 0.14117908477783203 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[10].display_name | Statistics |
| concepts[11].id | https://openalex.org/C33923547 |
| concepts[11].level | 0 |
| concepts[11].score | 0.13666868209838867 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[11].display_name | Mathematics |
| concepts[12].id | https://openalex.org/C127313418 |
| concepts[12].level | 0 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[12].display_name | Geology |
| concepts[13].id | https://openalex.org/C111919701 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[13].display_name | Operating system |
| concepts[14].id | https://openalex.org/C111368507 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q43518 |
| concepts[14].display_name | Oceanography |
| concepts[15].id | https://openalex.org/C134306372 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[15].display_name | Mathematical analysis |
| concepts[16].id | https://openalex.org/C31972630 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[16].display_name | Computer vision |
| concepts[17].id | https://openalex.org/C106131492 |
| concepts[17].level | 2 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q3072260 |
| concepts[17].display_name | Filter (signal processing) |
| keywords[0].id | https://openalex.org/keywords/leverage |
| keywords[0].score | 0.8035879135131836 |
| keywords[0].display_name | Leverage (statistics) |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.6586540341377258 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/tree |
| keywords[2].score | 0.5659822225570679 |
| keywords[2].display_name | Tree (set theory) |
| keywords[3].id | https://openalex.org/keywords/baseline |
| keywords[3].score | 0.5566980242729187 |
| keywords[3].display_name | Baseline (sea) |
| keywords[4].id | https://openalex.org/keywords/monte-carlo-method |
| keywords[4].score | 0.45461615920066833 |
| keywords[4].display_name | Monte Carlo method |
| keywords[5].id | https://openalex.org/keywords/process |
| keywords[5].score | 0.452033668756485 |
| keywords[5].display_name | Process (computing) |
| keywords[6].id | https://openalex.org/keywords/sampling |
| keywords[6].score | 0.42727091908454895 |
| keywords[6].display_name | Sampling (signal processing) |
| keywords[7].id | https://openalex.org/keywords/machine-learning |
| keywords[7].score | 0.42182695865631104 |
| keywords[7].display_name | Machine learning |
| keywords[8].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[8].score | 0.40194523334503174 |
| keywords[8].display_name | Artificial intelligence |
| keywords[9].id | https://openalex.org/keywords/data-mining |
| keywords[9].score | 0.3380521535873413 |
| keywords[9].display_name | Data mining |
| keywords[10].id | https://openalex.org/keywords/statistics |
| keywords[10].score | 0.14117908477783203 |
| keywords[10].display_name | Statistics |
| keywords[11].id | https://openalex.org/keywords/mathematics |
| keywords[11].score | 0.13666868209838867 |
| keywords[11].display_name | Mathematics |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2306.09608 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://arxiv.org/pdf/2306.09608 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2306.09608 |
| locations[1].id | doi:10.48550/arxiv.2306.09608 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | cc-by |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2306.09608 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5103236436 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-9068-4247 |
| authorships[0].author.display_name | Weizhe Chen |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Chen, Weizhe |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5101917996 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-6796-6817 |
| authorships[1].author.display_name | Lantao Liu |
| authorships[1].author_position | last |
| authorships[1].raw_author_name | Liu, Lantao |
| authorships[1].is_corresponding | False |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2306.09608 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Multi-Objective and Model-Predictive Tree Search for Spatiotemporal Informative Planning |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10848 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9879999756813049 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1703 |
| primary_topic.subfield.display_name | Computational Theory and Mathematics |
| primary_topic.display_name | Advanced Multi-Objective Optimization Algorithms |
| related_works | https://openalex.org/W2383111961, https://openalex.org/W2365952365, https://openalex.org/W2352448290, https://openalex.org/W2380820513, https://openalex.org/W2913146933, https://openalex.org/W2372385138, https://openalex.org/W4296359239, https://openalex.org/W2101155126, https://openalex.org/W2043093291, https://openalex.org/W2363545964 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2306.09608 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2306.09608 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2306.09608 |
| primary_location.id | pmh:oai:arXiv.org:2306.09608 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://arxiv.org/pdf/2306.09608 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2306.09608 |
| publication_date | 2023-06-16 |
| publication_year | 2023 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 46, 81, 108 |
| abstract_inverted_index.We | 44, 86 |
| abstract_inverted_index.by | 76, 89 |
| abstract_inverted_index.do | 39 |
| abstract_inverted_index.in | 4, 62, 117 |
| abstract_inverted_index.is | 74 |
| abstract_inverted_index.of | 119, 125 |
| abstract_inverted_index.so | 33 |
| abstract_inverted_index.to | 28, 68, 93, 132 |
| abstract_inverted_index.up | 67 |
| abstract_inverted_index.The | 20 |
| abstract_inverted_index.and | 2, 18, 60, 80, 97, 129 |
| abstract_inverted_index.are | 8 |
| abstract_inverted_index.but | 64 |
| abstract_inverted_index.not | 40, 53 |
| abstract_inverted_index.the | 11, 25, 57, 69, 91, 95, 102, 120, 126, 130, 133 |
| abstract_inverted_index.This | 73 |
| abstract_inverted_index.also | 65 |
| abstract_inverted_index.made | 37 |
| abstract_inverted_index.mean | 122 |
| abstract_inverted_index.only | 54 |
| abstract_inverted_index.over | 16 |
| abstract_inverted_index.root | 121 |
| abstract_inverted_index.show | 87 |
| abstract_inverted_index.that | 34, 88 |
| abstract_inverted_index.tree | 49 |
| abstract_inverted_index.well | 55 |
| abstract_inverted_index.when | 10 |
| abstract_inverted_index.with | 113 |
| abstract_inverted_index.Carlo | 48 |
| abstract_inverted_index.Monte | 47 |
| abstract_inverted_index.after | 111 |
| abstract_inverted_index.error | 124 |
| abstract_inverted_index.model | 128 |
| abstract_inverted_index.other | 114 |
| abstract_inverted_index.robot | 92 |
| abstract_inverted_index.space | 17 |
| abstract_inverted_index.terms | 118 |
| abstract_inverted_index.time. | 19 |
| abstract_inverted_index.which | 52 |
| abstract_inverted_index.action | 35 |
| abstract_inverted_index.become | 42 |
| abstract_inverted_index.future | 30 |
| abstract_inverted_index.ground | 134 |
| abstract_inverted_index.method | 51 |
| abstract_inverted_index.module | 27 |
| abstract_inverted_index.search | 50 |
| abstract_inverted_index.space, | 63 |
| abstract_inverted_index.square | 123 |
| abstract_inverted_index.target | 12 |
| abstract_inverted_index.truth. | 135 |
| abstract_inverted_index.varies | 15 |
| abstract_inverted_index.catches | 66 |
| abstract_inverted_index.changes | 32 |
| abstract_inverted_index.earlier | 38 |
| abstract_inverted_index.methods | 116 |
| abstract_inverted_index.process | 14 |
| abstract_inverted_index.propose | 45 |
| abstract_inverted_index.quickly | 41 |
| abstract_inverted_index.require | 24 |
| abstract_inverted_index.robotic | 5 |
| abstract_inverted_index.Adaptive | 0 |
| abstract_inverted_index.achieved | 75 |
| abstract_inverted_index.achieves | 107 |
| abstract_inverted_index.allowing | 90 |
| abstract_inverted_index.approach | 106 |
| abstract_inverted_index.balances | 56 |
| abstract_inverted_index.baseline | 115 |
| abstract_inverted_index.distance | 131 |
| abstract_inverted_index.dynamics | 23 |
| abstract_inverted_index.leverage | 94 |
| abstract_inverted_index.planning | 3, 26, 105 |
| abstract_inverted_index.process, | 101 |
| abstract_inverted_index.proposed | 103 |
| abstract_inverted_index.sampling | 1 |
| abstract_inverted_index.superior | 109 |
| abstract_inverted_index.temporal | 70 |
| abstract_inverted_index.comparing | 112 |
| abstract_inverted_index.decisions | 36 |
| abstract_inverted_index.dynamics. | 72 |
| abstract_inverted_index.integrate | 29 |
| abstract_inverted_index.outdated. | 43 |
| abstract_inverted_index.predicted | 98 |
| abstract_inverted_index.rewarding | 84 |
| abstract_inverted_index.simulated | 96 |
| abstract_inverted_index.look-ahead | 82 |
| abstract_inverted_index.mechanism. | 85 |
| abstract_inverted_index.monitoring | 7 |
| abstract_inverted_index.underlying | 21 |
| abstract_inverted_index.challenging | 9 |
| abstract_inverted_index.environment | 58, 127 |
| abstract_inverted_index.exploration | 59 |
| abstract_inverted_index.informative | 104 |
| abstract_inverted_index.performance | 110 |
| abstract_inverted_index.exploitation | 61 |
| abstract_inverted_index.optimization | 79 |
| abstract_inverted_index.environmental | 6, 13, 22, 31, 71, 100 |
| abstract_inverted_index.incorporating | 77 |
| abstract_inverted_index.spatiotemporal | 99 |
| abstract_inverted_index.multi-objective | 78 |
| abstract_inverted_index.model-predictive | 83 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.6800000071525574 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile |