Residual dynamics learning for trajectory tracking for multi-rotor aerial vehicles
Abstract This paper presents a technique to model the residual dynamics between a high-level planner and a low-level controller by considering reference trajectory tracking in a cluttered environment as an example scenario. We focus on minimising residual dynamics that arise due to only the kinemati...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-51822-0 |
| _version_ | 1797276624576577536 |
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| author | Geesara Kulathunga Hany Hamed Alexandr Klimchik |
| author_facet | Geesara Kulathunga Hany Hamed Alexandr Klimchik |
| author_sort | Geesara Kulathunga |
| collection | DOAJ |
| description | Abstract This paper presents a technique to model the residual dynamics between a high-level planner and a low-level controller by considering reference trajectory tracking in a cluttered environment as an example scenario. We focus on minimising residual dynamics that arise due to only the kinematical modelling of high-level planning. The kinematical modelling is sufficient for such scenarios due to safety constraints and aggressive manoeuvres that are difficult to perform when the environment is cluttered and dynamic. We used a simplified motion model to represent the motion of the quadrotor when formulating the high-level planner. The Sparse Gaussian Process Regression-based technique is proposed to model the residual dynamics. Recently proposed Data-Driven MPC is targeting aggressive manoeuvres without considering obstacle constraints. The proposed technique is compared with Data-Driven MPC to estimate the residual dynamics error without considering obstacle constraints. The comparison results yield that the proposed technique helps to reduce the nominal model error by a factor of 2 on average. Further, the proposed complete framework was compared with four other trajectory-tracking approaches in terms of tracking the reference trajectory without colliding with obstacles. The proposed approach outperformed the others with less flight time without losing computational efficiency. |
| first_indexed | 2024-03-07T15:30:54Z |
| format | Article |
| id | doaj.art-9b2410a1b77b4d48b63cfcdf1054cbb3 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:30:54Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-9b2410a1b77b4d48b63cfcdf1054cbb32024-03-05T16:26:13ZengNature PortfolioScientific Reports2045-23222024-01-0114111510.1038/s41598-024-51822-0Residual dynamics learning for trajectory tracking for multi-rotor aerial vehiclesGeesara Kulathunga0Hany Hamed1Alexandr Klimchik2Centre for Robotics and Mechatronics Components, Innopolis UniversityAdvanced Institute of Science and Technology (KAIST)School of Computer Science, University of LincolnAbstract This paper presents a technique to model the residual dynamics between a high-level planner and a low-level controller by considering reference trajectory tracking in a cluttered environment as an example scenario. We focus on minimising residual dynamics that arise due to only the kinematical modelling of high-level planning. The kinematical modelling is sufficient for such scenarios due to safety constraints and aggressive manoeuvres that are difficult to perform when the environment is cluttered and dynamic. We used a simplified motion model to represent the motion of the quadrotor when formulating the high-level planner. The Sparse Gaussian Process Regression-based technique is proposed to model the residual dynamics. Recently proposed Data-Driven MPC is targeting aggressive manoeuvres without considering obstacle constraints. The proposed technique is compared with Data-Driven MPC to estimate the residual dynamics error without considering obstacle constraints. The comparison results yield that the proposed technique helps to reduce the nominal model error by a factor of 2 on average. Further, the proposed complete framework was compared with four other trajectory-tracking approaches in terms of tracking the reference trajectory without colliding with obstacles. The proposed approach outperformed the others with less flight time without losing computational efficiency.https://doi.org/10.1038/s41598-024-51822-0 |
| spellingShingle | Geesara Kulathunga Hany Hamed Alexandr Klimchik Residual dynamics learning for trajectory tracking for multi-rotor aerial vehicles Scientific Reports |
| title | Residual dynamics learning for trajectory tracking for multi-rotor aerial vehicles |
| title_full | Residual dynamics learning for trajectory tracking for multi-rotor aerial vehicles |
| title_fullStr | Residual dynamics learning for trajectory tracking for multi-rotor aerial vehicles |
| title_full_unstemmed | Residual dynamics learning for trajectory tracking for multi-rotor aerial vehicles |
| title_short | Residual dynamics learning for trajectory tracking for multi-rotor aerial vehicles |
| title_sort | residual dynamics learning for trajectory tracking for multi rotor aerial vehicles |
| url | https://doi.org/10.1038/s41598-024-51822-0 |
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