A data-driven learning method for online prediction of drone battery discharge Article Swipe
Claudia Conte
,
Giancarlo Rufino
,
Giorgio de Alteriis
,
Verdiana Bottino
,
Domenico Accardo
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.ast.2022.107921
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.ast.2022.107921
This paper describes an adaptive method to predict the battery discharge of a multirotor drone over a generic path. A proper assessment of battery state of discharge trend is critical to ensure a safe operation of battery-powered aerial vehicles in critical environments, such as the urban ones. Several standard paths were executed by a commercial, battery-powered drone to acquire the data needed to train a Deep Learning based method; telemetry files and ground-collected data were processed to train the proposed method according to a trajectory segmentation strategy. Two learning configurations were trained to predict the time-of-flight and the integral of the battery current needed to fly the standard path segments. The current integral for each standard path segment is exploited to estimate the corresponding reduction of the battery state of charge. Based on path segmentation into predefined standard sections, the presented solution allows to predict time of flight and battery consumption along any generic path. This can be exploited to plan a complete path at strategic stage, as well as to estimate the remaining available power resource at any intermediate point along a generic trajectory both at strategic stage and during mission execution to be exploited by the drone operator or by a traffic management service. To validate the technique, a scaled package delivery mission is presented as an example of contingency management application. The maximum distance that the drone can reach from selected points along the mission path was computed according to the remaining battery level. Considering a random distribution of safe-landing areas around the planned path, the computed maximum distance was used to determine which safe-landing areas can be reached by the drone from a generic point of the path after an unexpected event.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.ast.2022.107921
- OA Status
- hybrid
- Cited By
- 20
- References
- 37
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4300772750Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.ast.2022.107921Digital Object Identifier
- Title
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A data-driven learning method for online prediction of drone battery dischargeWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-10-03Full publication date if available
- Authors
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Claudia Conte, Giancarlo Rufino, Giorgio de Alteriis, Verdiana Bottino, Domenico AccardoList of authors in order
- Landing page
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https://doi.org/10.1016/j.ast.2022.107921Publisher landing page
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.ast.2022.107921Direct OA link when available
- Concepts
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Drone, Battery (electricity), Trajectory, Computer science, Path (computing), Battery pack, Real-time computing, State of charge, Simulation, Power (physics), Biology, Physics, Genetics, Quantum mechanics, Programming language, AstronomyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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20Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 10, 2023: 4, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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37Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| primary_location.is_published | True |
| primary_location.raw_source_name | Aerospace Science and Technology |
| primary_location.landing_page_url | https://doi.org/10.1016/j.ast.2022.107921 |
| publication_date | 2022-10-03 |
| publication_year | 2022 |
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