Sliding Mode Controller navigation algorithm using tag-based fiducial marker detection and fuzzy logic system
Autonomous navigation of vehicles, especially drones, plays an essential role in Industrial Revolution 4.0. Maneuvering drone in complex path especially indoor environment requires stable and accurate navigation system. This paper investigates a navigation algorithm for maneuvering a drone by Slidin...
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| Format: | Article |
| Language: | English |
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Polish Academy of Sciences
2025-03-01
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| Series: | Archives of Control Sciences |
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| Online Access: | https://journals.pan.pl/Content/134364/PDF/art01.pdf |
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| author | Mohammad Soleimani Amiri Rizauddin Ramli Ahmad Barari |
| author_facet | Mohammad Soleimani Amiri Rizauddin Ramli Ahmad Barari |
| author_sort | Mohammad Soleimani Amiri |
| collection | DOAJ |
| description | Autonomous navigation of vehicles, especially drones, plays an essential role in Industrial Revolution 4.0. Maneuvering drone in complex path especially indoor environment requires stable and accurate navigation system. This paper investigates a navigation algorithm for maneuvering a drone by Sliding Mode Controller (SMC) combined by fuzzy logic system, model reference approach, and tag-based fiducial marker detection in an indoor environment. The SMC parameters are tuned by the fuzzy logic system and model reference approach. A drone model is simulated in a virtual indoor environment to validate the performance of the navigation system with different home points and trajectories. The desired set-points of the control system are obtained by AprilTag, which is a tag-based fiducial marker detection system. The stability of the SMC was verified using the Lyapunov stability theory. The performance of proposed SMC navigation algorithm validated by comparing to conventional controllers which represents the effectiveness of SMC. It can be ascertained that the proposed SMC navigation algorithm is applicable to maneuver the drone for various industrial tasks in indoor environment. |
| format | Article |
| id | doaj-art-65b1345685cd4c4484c2bd652d6810c0 |
| institution | DOAJ |
| issn | 1230-2384 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | Archives of Control Sciences |
| spelling | doaj-art-65b1345685cd4c4484c2bd652d6810c02025-08-20T02:56:33ZengPolish Academy of SciencesArchives of Control Sciences1230-23842025-03-01Vol. 35No 1117https://doi.org/10.24425/acs.2025.153956Sliding Mode Controller navigation algorithm using tag-based fiducial marker detection and fuzzy logic systemMohammad Soleimani Amiri0https://orcid.org/0000-0001-6364-6392Rizauddin Ramli1https://orcid.org/0000-0002-5907-3736Ahmad Barari2https://orcid.org/0000-0003-4273-4371Department of Manufacturing Engineering Technology, Faculty of Industrial and Manufacturing Technology and Engineering, Universiti Teknikal Malaysia Melaka, 76100 Durian Tunggal, Melaka, MalaysiaFaculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, MalaysiaDepartment of Mechanical and Manufacturing Engineering, University of OntarioInstitute of Technology, Oshawa, Ontario, L1H7K4, CanadaAutonomous navigation of vehicles, especially drones, plays an essential role in Industrial Revolution 4.0. Maneuvering drone in complex path especially indoor environment requires stable and accurate navigation system. This paper investigates a navigation algorithm for maneuvering a drone by Sliding Mode Controller (SMC) combined by fuzzy logic system, model reference approach, and tag-based fiducial marker detection in an indoor environment. The SMC parameters are tuned by the fuzzy logic system and model reference approach. A drone model is simulated in a virtual indoor environment to validate the performance of the navigation system with different home points and trajectories. The desired set-points of the control system are obtained by AprilTag, which is a tag-based fiducial marker detection system. The stability of the SMC was verified using the Lyapunov stability theory. The performance of proposed SMC navigation algorithm validated by comparing to conventional controllers which represents the effectiveness of SMC. It can be ascertained that the proposed SMC navigation algorithm is applicable to maneuver the drone for various industrial tasks in indoor environment.https://journals.pan.pl/Content/134364/PDF/art01.pdffuzzy logic, sliding mode controller, autonomous navigation, fiducial marker detection |
| spellingShingle | Mohammad Soleimani Amiri Rizauddin Ramli Ahmad Barari Sliding Mode Controller navigation algorithm using tag-based fiducial marker detection and fuzzy logic system Archives of Control Sciences fuzzy logic, sliding mode controller, autonomous navigation, fiducial marker detection |
| title | Sliding Mode Controller navigation algorithm using tag-based fiducial marker detection and fuzzy logic system |
| title_full | Sliding Mode Controller navigation algorithm using tag-based fiducial marker detection and fuzzy logic system |
| title_fullStr | Sliding Mode Controller navigation algorithm using tag-based fiducial marker detection and fuzzy logic system |
| title_full_unstemmed | Sliding Mode Controller navigation algorithm using tag-based fiducial marker detection and fuzzy logic system |
| title_short | Sliding Mode Controller navigation algorithm using tag-based fiducial marker detection and fuzzy logic system |
| title_sort | sliding mode controller navigation algorithm using tag based fiducial marker detection and fuzzy logic system |
| topic | fuzzy logic, sliding mode controller, autonomous navigation, fiducial marker detection |
| url | https://journals.pan.pl/Content/134364/PDF/art01.pdf |
| work_keys_str_mv | AT mohammadsoleimaniamiri slidingmodecontrollernavigationalgorithmusingtagbasedfiducialmarkerdetectionandfuzzylogicsystem AT rizauddinramli slidingmodecontrollernavigationalgorithmusingtagbasedfiducialmarkerdetectionandfuzzylogicsystem AT ahmadbarari slidingmodecontrollernavigationalgorithmusingtagbasedfiducialmarkerdetectionandfuzzylogicsystem |