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Towards Energy Consumption Prediction with Safety Margins for Multicopter Systems

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Published:05 December 2017Publication History

ABSTRACT

Multicopters are robotic systems with a remarkable degree of freedom and applicability. A significant limitation of all mobile robotic vehicles is the restricted on-board energy storage capacity and consequential limited operation time. Uninterrupted mission execution of multicopter swarms thus requires to predict energy depletion and plan maintenance and replacement processes accordingly. This paper presents and discusses challenges of a realistic prediction model for battery energy consumption. To cope with the inherent uncertainty we are interested not only in the expected values, but also safety margins. The paper discusses common expectations first and presents a solution idea for the underlying key issue.

References

  1. T. Dietrich, S. Krug, and A. Zimmermann. 2017. An Empirical Study on Generic Multicopter Energy Consumption Profiles. In IEEE Int. Systems Conference (SysCon 2017). Montreal, Canada, 1--6.Google ScholarGoogle Scholar
  2. R. Lyman Ott and Michael Longnecker. 2010. An Introduction to Statistical Methods and Data Analysis (6 ed.). Cengage Learning Inc. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Towards Energy Consumption Prediction with Safety Margins for Multicopter Systems

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      • Published in

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        VALUETOOLS 2017: Proceedings of the 11th EAI International Conference on Performance Evaluation Methodologies and Tools
        December 2017
        268 pages
        ISBN:9781450363464
        DOI:10.1145/3150928

        Copyright © 2017 Owner/Author

        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 5 December 2017

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        Overall Acceptance Rate90of196submissions,46%
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