Improving Tracking of Trajectories through Tracking Rate Regulation: Application to UAVs
The tracking problem (that is, how to follow a previously memorized path) is one of the most important problems in mobile robots. Several methods can be formulated depending on the way the robot state is related to the path. “Trajectory tracking” is the most common method, with the controller aiming...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/24/9795 |
| _version_ | 1797455335657570304 |
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| author | Fernando Diaz-del-Rio Pablo Sanchez-Cuevas Pablo Iñigo-Blasco J. L. Sevillano-Ramos |
| author_facet | Fernando Diaz-del-Rio Pablo Sanchez-Cuevas Pablo Iñigo-Blasco J. L. Sevillano-Ramos |
| author_sort | Fernando Diaz-del-Rio |
| collection | DOAJ |
| description | The tracking problem (that is, how to follow a previously memorized path) is one of the most important problems in mobile robots. Several methods can be formulated depending on the way the robot state is related to the path. “Trajectory tracking” is the most common method, with the controller aiming to move the robot toward a moving target point, like in a real-time servosystem. In the case of complex systems or systems under perturbations or unmodeled effects, such as UAVs (Unmanned Aerial Vehicles), other tracking methods can offer additional benefits. In this paper, methods that consider the dynamics of the path’s descriptor parameter (which can be called “error adaptive tracking”) are contrasted with trajectory tracking. A formal description of tracking methods is first presented, showing that two types of error adaptive tracking can be used with the same controller in any system. Then, it is shown that the selection of an appropriate tracking rate improves error convergence and robustness for a UAV system, which is illustrated by simulation experiments. It is concluded that error adaptive tracking methods outperform trajectory tracking ones, producing a faster and more robust convergence tracking, while preserving, if required, the same tracking rate when convergence is achieved. |
| first_indexed | 2024-03-09T15:52:56Z |
| format | Article |
| id | doaj.art-ccde2e994d824636a4f0a57d9ca78c39 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T15:52:56Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ccde2e994d824636a4f0a57d9ca78c392023-11-24T17:55:27ZengMDPI AGSensors1424-82202022-12-012224979510.3390/s22249795Improving Tracking of Trajectories through Tracking Rate Regulation: Application to UAVsFernando Diaz-del-Rio0Pablo Sanchez-Cuevas1Pablo Iñigo-Blasco2J. L. Sevillano-Ramos3ETS Ingeniería Informática, Universidad de Sevilla, Av. Reina Mercedes s/n, 41012 Sevilla, SpainETS Ingeniería Informática, Universidad de Sevilla, Av. Reina Mercedes s/n, 41012 Sevilla, SpainETS Ingeniería Informática, Universidad de Sevilla, Av. Reina Mercedes s/n, 41012 Sevilla, SpainETS Ingeniería Informática, Universidad de Sevilla, Av. Reina Mercedes s/n, 41012 Sevilla, SpainThe tracking problem (that is, how to follow a previously memorized path) is one of the most important problems in mobile robots. Several methods can be formulated depending on the way the robot state is related to the path. “Trajectory tracking” is the most common method, with the controller aiming to move the robot toward a moving target point, like in a real-time servosystem. In the case of complex systems or systems under perturbations or unmodeled effects, such as UAVs (Unmanned Aerial Vehicles), other tracking methods can offer additional benefits. In this paper, methods that consider the dynamics of the path’s descriptor parameter (which can be called “error adaptive tracking”) are contrasted with trajectory tracking. A formal description of tracking methods is first presented, showing that two types of error adaptive tracking can be used with the same controller in any system. Then, it is shown that the selection of an appropriate tracking rate improves error convergence and robustness for a UAV system, which is illustrated by simulation experiments. It is concluded that error adaptive tracking methods outperform trajectory tracking ones, producing a faster and more robust convergence tracking, while preserving, if required, the same tracking rate when convergence is achieved.https://www.mdpi.com/1424-8220/22/24/9795UAVmobile robotspath followingtrajectory trackingerror adaptive trackingLyapunov stability theory |
| spellingShingle | Fernando Diaz-del-Rio Pablo Sanchez-Cuevas Pablo Iñigo-Blasco J. L. Sevillano-Ramos Improving Tracking of Trajectories through Tracking Rate Regulation: Application to UAVs Sensors UAV mobile robots path following trajectory tracking error adaptive tracking Lyapunov stability theory |
| title | Improving Tracking of Trajectories through Tracking Rate Regulation: Application to UAVs |
| title_full | Improving Tracking of Trajectories through Tracking Rate Regulation: Application to UAVs |
| title_fullStr | Improving Tracking of Trajectories through Tracking Rate Regulation: Application to UAVs |
| title_full_unstemmed | Improving Tracking of Trajectories through Tracking Rate Regulation: Application to UAVs |
| title_short | Improving Tracking of Trajectories through Tracking Rate Regulation: Application to UAVs |
| title_sort | improving tracking of trajectories through tracking rate regulation application to uavs |
| topic | UAV mobile robots path following trajectory tracking error adaptive tracking Lyapunov stability theory |
| url | https://www.mdpi.com/1424-8220/22/24/9795 |
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