Propeller damage detection, classification and estimation in multirotor vehicles
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866929531266269184 |
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| author | Pose, Claudio Giribet, Juan Torre, Gabriel |
| author_facet | Pose, Claudio Giribet, Juan Torre, Gabriel |
| contents | This manuscript details an architecture and training methodology for a data-driven framework aimed at detecting, identifying, and quantifying damage in the propeller blades of multirotor Unmanned Aerial Vehicles. By substituting one propeller with a damaged counterpart-encompassing three distinct damage types of varying severity-real flight data was collected. This data was then used to train a composite model, comprising both classifiers and neural networks, capable of accurately identifying the type of failure, estimating damage severity, and pinpointing the affected rotor. The data employed for this analysis was exclusively sourced from inertial measurements and control command inputs, ensuring adaptability across diverse multirotor vehicle platforms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_05447 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Propeller damage detection, classification and estimation in multirotor vehicles Pose, Claudio Giribet, Juan Torre, Gabriel Robotics Systems and Control This manuscript details an architecture and training methodology for a data-driven framework aimed at detecting, identifying, and quantifying damage in the propeller blades of multirotor Unmanned Aerial Vehicles. By substituting one propeller with a damaged counterpart-encompassing three distinct damage types of varying severity-real flight data was collected. This data was then used to train a composite model, comprising both classifiers and neural networks, capable of accurately identifying the type of failure, estimating damage severity, and pinpointing the affected rotor. The data employed for this analysis was exclusively sourced from inertial measurements and control command inputs, ensuring adaptability across diverse multirotor vehicle platforms. |
| title | Propeller damage detection, classification and estimation in multirotor vehicles |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2410.05447 |