Academic Journal
Fully differentiable sensor placement and informative path planning.
| Τίτλος: | Fully differentiable sensor placement and informative path planning. |
|---|---|
| Συγγραφείς: | Jakkala, Kalvik1 kalvik@tamu.edu, Akella, Srinivas2 |
| Πηγή: | International Journal of Robotics Research. Jul2026, Vol. 45 Issue 8, p1247-1284. 38p. |
| Θεματικοί όροι: | *Robotics, *Mathematical optimization, Sensor placement, Robotic path planning, Environmental monitoring, Autonomous robots, Gaussian processes, Variational approach (Mathematics) |
| Περίληψη: | Sensor placement (SP) and informative path planning (IPP) problems are prevalent in environmental monitoring. These problems require gathering the most informative data from a limited number of sensing locations, but existing solutions face a difficult trade-off. Existing methods are often either computationally efficient but less informative, or more informative but too computationally expensive for practical use, especially on resource-constrained robots. Furthermore, many approaches are limited by requiring discretization of the environment or relying on slow, derivative-free optimization techniques. This paper introduces a novel, computationally efficient variational formulation for the SP problem. Our approach is differentiable with respect to the sensing locations, enabling fast gradient-based optimization in continuous spaces and delivering performance comparable to MI-based methods at a fraction of the computational cost. We establish our formulation as a special case of sparse Gaussian processes (SGPs). This connection allows us to generalize the method to solve the IPP problem for single and multi-robot systems, efficiently incorporating differentiable path constraints and diverse sensor types. The approach is validated through extensive benchmarks and field experiments with an autonomous surface vehicle (ASV) and an autonomous underwater vehicle (AUV). We also provide SGP-Tools—an open-source Python library—and a companion ROS 2 package for Ardupilot-based mobile robots. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Robotics Research is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Items | – Name: Title Label: Title Group: Ti Data: Fully differentiable sensor placement and informative path planning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jakkala%2C+Kalvik%22">Jakkala, Kalvik</searchLink><relatesTo>1</relatesTo><i> kalvik@tamu.edu</i><br /><searchLink fieldCode="AR" term="%22Akella%2C+Srinivas%22">Akella, Srinivas</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Robotics+Research%22">International Journal of Robotics Research</searchLink>. Jul2026, Vol. 45 Issue 8, p1247-1284. 38p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Robotics%22">Robotics</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Sensor+placement%22">Sensor placement</searchLink><br /><searchLink fieldCode="DE" term="%22Robotic+path+planning%22">Robotic path planning</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+monitoring%22">Environmental monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Autonomous+robots%22">Autonomous robots</searchLink><br /><searchLink fieldCode="DE" term="%22Gaussian+processes%22">Gaussian processes</searchLink><br /><searchLink fieldCode="DE" term="%22Variational+approach+%28Mathematics%29%22">Variational approach (Mathematics)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Sensor placement (SP) and informative path planning (IPP) problems are prevalent in environmental monitoring. These problems require gathering the most informative data from a limited number of sensing locations, but existing solutions face a difficult trade-off. Existing methods are often either computationally efficient but less informative, or more informative but too computationally expensive for practical use, especially on resource-constrained robots. Furthermore, many approaches are limited by requiring discretization of the environment or relying on slow, derivative-free optimization techniques. This paper introduces a novel, computationally efficient variational formulation for the SP problem. Our approach is differentiable with respect to the sensing locations, enabling fast gradient-based optimization in continuous spaces and delivering performance comparable to MI-based methods at a fraction of the computational cost. We establish our formulation as a special case of sparse Gaussian processes (SGPs). This connection allows us to generalize the method to solve the IPP problem for single and multi-robot systems, efficiently incorporating differentiable path constraints and diverse sensor types. The approach is validated through extensive benchmarks and field experiments with an autonomous surface vehicle (ASV) and an autonomous underwater vehicle (AUV). We also provide SGP-Tools—an open-source Python library—and a companion ROS 2 package for Ardupilot-based mobile robots. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Robotics Research is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/02783649251384993 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 38 StartPage: 1247 Subjects: – SubjectFull: Robotics Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Sensor placement Type: general – SubjectFull: Robotic path planning Type: general – SubjectFull: Environmental monitoring Type: general – SubjectFull: Autonomous robots Type: general – SubjectFull: Gaussian processes Type: general – SubjectFull: Variational approach (Mathematics) Type: general Titles: – TitleFull: Fully differentiable sensor placement and informative path planning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jakkala, Kalvik – PersonEntity: Name: NameFull: Akella, Srinivas IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02783649 Numbering: – Type: volume Value: 45 – Type: issue Value: 8 Titles: – TitleFull: International Journal of Robotics Research Type: main |
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