Kriging Variance Informed Multi-Robot Path Planning and Task Allocation for Efficient Mapping of Soil Properties Article Swipe
Laurence Roberts-Elliott
,
Gautham P. Das
,
Grzegorz Cielniak
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202504.2480.v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202504.2480.v1
Soil property mapping is an essential step in precision agriculture for informed fertiliser application, tillage, and irrigation management to improve crop yields. The current practices involve manual soil property sampling and lab tests of the samples. Due to the slow manual sampling of large land areas and the high costs of the lab tests, these mappings are carried out at very low spatial resolution. A realistic approach to resolve this challenge is to use multiple robots with proximal soil sensors to parallelise the sampling process and to create soil property maps in high spatial resolutions. Multi-robot soil sampling is underexplored in the literature. Therefore, auction-based multi-robot task allocation approaches are proposed in this work to efficiently coordinate the sampling process. To reduce the necessary number of samples for accurate mapping, while maximising information gained per sample, a dynamic sampling strategy, informed by kriging variance from kriging interpolation of sampled soil compaction values, has been implemented. This is enhanced by insertion heuristics for task queuing, and thresholding of tasks which aren’t expected to offer significant information gain. The evaluation trials show the suitability of the proposed Distance Over Variance bid calculation, combined with the cheapest insertion heuristic and median kriging variance based task dropping, resulting in substantial improvements in key performance metrics. Although this work looks at soil compaction data, information-driven dynamic sampling of other soil properties from large areas can also utilise the kriging interpolation and kriging variance approach.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.20944/preprints202504.2480.v1
- https://www.preprints.org/frontend/manuscript/d4bad723ec2113683215f47e7913698e/download_pub
- OA Status
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- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409911668
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409911668Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.20944/preprints202504.2480.v1Digital Object Identifier
- Title
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Kriging Variance Informed Multi-Robot Path Planning and Task Allocation for Efficient Mapping of Soil PropertiesWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-04-29Full publication date if available
- Authors
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Laurence Roberts-Elliott, Gautham P. Das, Grzegorz CielniakList of authors in order
- Landing page
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https://doi.org/10.20944/preprints202504.2480.v1Publisher landing page
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https://www.preprints.org/frontend/manuscript/d4bad723ec2113683215f47e7913698e/download_pubDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.preprints.org/frontend/manuscript/d4bad723ec2113683215f47e7913698e/download_pubDirect OA link when available
- Concepts
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Kriging, Variance (accounting), Task (project management), Path (computing), Motion planning, Robot, Computer science, Mathematical optimization, Environmental science, Artificial intelligence, Mathematics, Machine learning, Engineering, Business, Systems engineering, Programming language, AccountingTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.strategy, | 139 |
| abstract_inverted_index.Therefore, | 103 |
| abstract_inverted_index.allocation | 107 |
| abstract_inverted_index.approaches | 108 |
| abstract_inverted_index.compaction | 150, 217 |
| abstract_inverted_index.coordinate | 116 |
| abstract_inverted_index.evaluation | 177 |
| abstract_inverted_index.fertiliser | 12 |
| abstract_inverted_index.heuristics | 160 |
| abstract_inverted_index.irrigation | 16 |
| abstract_inverted_index.management | 17 |
| abstract_inverted_index.maximising | 131 |
| abstract_inverted_index.properties | 225 |
| abstract_inverted_index.Multi-robot | 95 |
| abstract_inverted_index.agriculture | 9 |
| abstract_inverted_index.efficiently | 115 |
| abstract_inverted_index.information | 132, 174 |
| abstract_inverted_index.literature. | 102 |
| abstract_inverted_index.multi-robot | 105 |
| abstract_inverted_index.parallelise | 81 |
| abstract_inverted_index.performance | 209 |
| abstract_inverted_index.resolution. | 63 |
| abstract_inverted_index.significant | 173 |
| abstract_inverted_index.substantial | 205 |
| abstract_inverted_index.suitability | 181 |
| abstract_inverted_index.application, | 13 |
| abstract_inverted_index.calculation, | 189 |
| abstract_inverted_index.implemented. | 154 |
| abstract_inverted_index.improvements | 206 |
| abstract_inverted_index.resolutions. | 94 |
| abstract_inverted_index.thresholding | 165 |
| abstract_inverted_index.auction-based | 104 |
| abstract_inverted_index.interpolation | 146, 234 |
| abstract_inverted_index.underexplored | 99 |
| abstract_inverted_index.information-driven | 219 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.09946224 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |