Sveobuhvatni pregled strategija upravljanja i vođenja za bespilotna zemaljska vozila u primenama praćenja trake i praćenja lidera

  • Salem-bilal AMOKRANE Vojna Akadimija
  • Momir Stanković Vojna akademija u Beogradu
  • Stojadin Manojlović Vojna akademija u Beogradu
  • Ahmed Taki-Eddine Benyahia Vojna akademija u Beogradu
DOI: https://doi.org/10.5937/vojtehg73-59271
Ključne reči: Беспилотна земаљска возила (БЗВ), Системи управљања, Системи вођења, Праћење вође, Праћење траке, Избегавање препрека, Аутономна навигација.

Sažetak


Увод/циљ: Беспилотна земаљска возила (БЗВ) нуде значајне предности за различите операције, али њихово аутономно управљање и вођење представљају значајне потешкоће, посебно за различите типове локомоције (нпр. гусеничне, точкашке) на изазовним теренима, услед сложене динамике, нехолономних ограничења и интеракција са окружењем. Овај рад пружа свеобухватан преглед стратегија управљања и вођења за БЗВ, са посебним фокусом на примене праћења вође и праћења траке са избегавањем препрека. Циљ је да се синтетише тренутно стање технике, идентификују кључни изазови генерички за аутономију БЗВ у овим задацима, и размотре обећавајуће методологије управљања.

Методе: Спроведен је опсежан преглед литературе, анализирајући постојећа истраживања о историји БЗВ, нивоима аутономије, системским архитектурама, методологијама управљања (укључујући класичне, адаптивне, робусне и интелигентне приступе), као и специфичним доменима примене. Критички су испитане методологије за вођење и управљање релевантне за БГВ у задацима праћења вође и праћења траке.

Резултати: Преглед идентификује доминантне трендове, укључујући све већу употребу дубоког учења за перцепцију и растуће интересовање за робусне технике управљања способне да одговоре на оперативне изазове БЗВ-а. Значајни изазови и даље постоје у перцепцији за неструктурирана окружења, тачном динамичком моделирању за различите платформе БЗВ-а, беспрекорној интеграцији перцепције са робусним управљањем и опсежној валидацији у реалним условима. Стратегије управљања засноване на осматрачима, као што је Активно потискивање поремећаја (ADRC), показују значајан потенцијал у управљању нелинеарностима, несигурностима и поремећајима БЗВ-а са смањеном зависношћу од модела.

Закључак: Постизање робусне аутономије за БЗВ у сложеним реалним сценаријима захтева интегрисана решења која обухватају вођење и управљање. Напредне робусне методе управљања, укључујући ADRC, појављују се као снажни кандидати за управљање БЗВ-има, али њихов пуни потенцијал захтева даља истраживања њихове интеграције са напредним сензорским системима и темељну експерименталну валидацију у циљаним оперативним доменима БЗВ-а.

Reference

Aguiar, A.P. & Hespanha, J.P. 2007. Trajectory-Tracking and Path-Following of Underactuated Autonomous Vehicles With Parametric Modeling Uncertainty. IEEE Transactions on Automatic Control, 52(8), pp. 1362–1379. Available at: https://doi.org/10.1109/TAC.2007.902731.

Ahluwalia, V., Arents, J., Oraby, A. & Greitans, M. 2022. Construction and benchmark of an autonomous tracked mobile robot system. Robotic Systems and Applications, 2(1), pp. 15–28. Available at: https://doi.org/10.21595/rsa.2022.22336.

Al-Jarrah, A., Salah, M. & Almomani, F. 2019. Controlling a Skid-Steered Tracked Mobile Robot with Slippage Using Various Control Schemes. In: 2019 20th International Conference on Research and Education in Mechatronics (REM). IEEE, pp. 1–7. Available at: https://doi.org/10.1109/REM.2019.8744123.

Al-Zaher, T.S.A., Bayoumy, A.M., Sharaf, A.H.M. & El-din, Y.H.H. 2012. Lane tracking and obstacle avoidance for Autonomous Ground Vehicles. In: 2012 9th France-Japan & 7th Europe-Asia Congress on Mechatronics (MECATRONICS) / 13th Int'l Workshop on Research and Education in Mechatronics (REM). pp. 264–271. Available at: https://doi.org/10.1109/MECATRONICS.2012.6451019.

Alexa, O., Ciobotaru, T., Grigore, L.Ș., Grigorie, T.L., Ștefan, A., Oncioiu, I., Priescu, I. & Vlădescu, C. 2023. A Review of Mathematical Models Used to Estimate Wheeled and Tracked Unmanned Ground Vehicle Kinematics and Dynamics. Mathematics, 11(17), p. 3735. Available at: https://doi.org/10.3390/math11173735.

Aly, M. 2008. Real time detection of lane markers in urban streets. In: 2008 IEEE Intelligent Vehicles Symposium. pp. 7–12. Available at: https://doi.org/10.1109/IVS.2008.4621152.

Amaradi, P., Sriramoju, N., Dang, L., Tewolde, G.S. & Kwon, J. 2016. Lane following and obstacle detection techniques in autonomous driving vehicles. In: 2016 IEEE International Conference on Electro Information Technology (EIT). pp. 0674-0679. Available at: https://doi.org/10.1109/EIT.2016.7535320.

Amokrane, S.B., Laidouni, M.Z., Adli, T., Madonski, R. & Stanković, M. 2024. Active disturbance rejection control for unmanned tracked vehicles in leader-follower scenarios: Discrete-time implementation and field test validation. Mechatronics, 97, p. 103114. Available at: https://doi.org/10.1016/j.mechatronics.2023.103114.

Andrade, D.C., Bueno, F., Franco, F.R., Silva, R.A., Neme, J.H.Z., Margraf, E., Omoto, W.T., Farinelli, F.A., Tusset, A.M., Okida, S., Santos, M.M.D., Ventura, A., Carvalho, S. & Amaral, R.d.S. 2019. A Novel Strategy for Road Lane Detection and Tracking Based on a Vehicle's Forward Monocular Camera. IEEE Transactions on Intelligent Transportation Systems, 20(4), pp. 1497–1507. Available at: https://doi.org/10.1109/TITS.2018.2856361.

Artuñedo, A., Moreno-Gonzalez, M. & Villagra, J. 2024. Lateral control for autonomous vehicles: A comparative evaluation. Annual Reviews in Control, 57, p. 100910. Available at: https://doi.org/10.1016/j.arcontrol.2023.100910.

Azam, S., Munir, F. & Jeon, M. 2020. Dynamic Control System Design for Autonomous Car. In: Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS. pp. 456-463. Available at: https://doi.org/10.5220/0009392904560463.

Badrloo, S., Varshosaz, M., Pirasteh, S. & Li, J. 2022. Image-Based Obstacle Detection Methods for the Safe Navigation of Unmanned Vehicles: A Review. Remote Sensing, 14(15), p. 3824. Available at: https://doi.org/10.3390/rs14153824.

Baek, S.W., Kim, M.J., Suddamalla, U., Wong, A., Lee, B.H. & Kim, J.H. 2022. Real-Time Lane Detection Based on Deep Learning. Journal of Electrical Engineering & Technology, 17(1), pp. 655–664. Available at: https://doi.org/10.1007/s42835-021-00902-6.

Balasubramaniam, A. & Pasricha, S. 2022. Object Detection in Autonomous Vehicles: Status and Open Challenges. arXiv preprint. Available at: https://doi.org/10.48550/arXiv.2201.07706.

Balestrieri, E., Daponte, P., De Vito, L. & Lamonaca, F. 2021. Sensors and Measurements for Unmanned Systems: An Overview. Sensors, 21(4), p. 1518. Available at: https://doi.org/10.3390/s21041518.

BaniHani, S., Hayajneh, M.R.M., Al-Jarrah, A. & Mutawe, S. 2021. New Control Approaches for Trajectory Tracking and Motion Planning of Unmanned Tracked Robot. Advances in Electrical and Electronic Engineering, 19(1), pp. 42–56. Available at: https://doi.org/10.15598/aeee.v19i1.4006.

Bar Hillel, A., Lerner, R., Levi, D. & Raz, G. 2014. Recent progress in road and lane detection: a survey. Machine Vision and Applications, 25(3), pp. 727–745. Available at: https://doi.org/10.1007/s00138-011-0404-2.

Benyahia, A.T.E., Stanković, M., Madonski, R., Babayomi, O. & Manojlović, S.M. 2025. Improving Control Performance by Cascading Observers: Case of ADRC With Cascade ESO. IEEE/CAA Journal of Automatica Sinica, 12(8), pp. 1702-1712. Available at: https://doi.org/10.1109/JAS.2024.124995.

Burke, M. & Brink, W. 2010. Estimating target orientation with a single camera for use in a human-following robot. In: 21st Annual Symposium of the Pattern Recognition Association of South Africa (PRASA). [online]. Available at: https://api.semanticscholar.org/CorpusID:129396257 [Accessed: 09 July 2025].

Cao, J., Song, C., Peng, S., Song, S., Zhang, X. & Xiao, F. 2020. Trajectory Tracking Control Algorithm for Autonomous Vehicle Considering Cornering Characteristics. IEEE Access, 8, pp. 59470-59484. Available at: https://doi.org/10.1109/ACCESS.2020.2982963.

Cao, M., Wang, J. & Ming, L. 2021. Multi-Templates Based Robust Tracking for Robot Person-Following Tasks. Applied Sciences, 11(18), p. 8698. Available at: https://doi.org/10.3390/app11188698.

Chen, B.C., Luan, B.C. & Lee, K. 2014. Design of lane keeping system using adaptive model predictive control. In: 2014 IEEE International Conference on Automation Science and Engineering (CASE). pp. 922-926. Available at: https://doi.org/10.1109/CoASE.2014.6899436.

Chen, E. 2018. “FOLO”: A vision-based human-following robot. In: 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018). Atlantis Press, pp. 224-232. Available at: https://doi.org/10.2991/amcce-18.2018.39.

Chen, Y. 2016. Autonomous Unmanned Ground Vehicle (UGV) Follower Design. Master's thesis, Ohio University. [online]. Available at: https://docslib.org/doc/8343660/autonomous-unmanned-ground-vehicle-ugv-follower-design [Accessed: 11 June 2025].

Chetan, N.B., Gong, J., Zhou, H., Bi, D., Lan, J. & Qie, L. 2020. An Overview of Recent Progress of Lane Detection for Autonomous Driving. In: 2019 6th International Conference on Dependable Systems and Their Applications (DSA). pp. 341-346. Available at: https://doi.org/10.1109/DSA.2019.00052.

Chu, Z., Sun, Y., Wu, C. & Sepehri, N. 2018a. Active disturbance rejection control applied to automated steering for lane keeping in autonomous vehicles. Control Engineering Practice, 74, pp. 13–21. Available at: https://doi.org/10.1016/j.conengprac.2018.02.002.

Chu, Z., Wu, C. & Sepehri, N. 2018b. Automated steering controller design for vehicle lane keeping combining linear active disturbance rejection control and quantitative feedback theory. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 232(7), pp. 937–948. Available at: https://doi.org/10.1177/0959651818770344.

Chung, W., Kim, H., Yoo, Y., Moon, C.B. & Park, J. 2012. The Detection and Following of Human Legs Through Inductive Approaches for a Mobile Robot With a Single Laser Range Finder. IEEE Transactions on Industrial Electronics, 59(8), pp. 3156-3166. Available at: https://doi.org/10.1109/TIE.2011.2170389.

Dawam, E.S. & Feng, X. 2020. Smart City Lane Detection for Autonomous Vehicle. In: 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). pp. 334–338. Available at: https://doi.org/10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00065.

Dewangan, D.K., Sahu, S.P., Sairam, B. & Agrawal, A. 2021. VLDNet: Vision-based lane region detection network for intelligent vehicle system using semantic segmentation. Computing, 103(12), pp. 2867–2892. Available at: https://doi.org/10.1007/s00607-021-00974-2.

Dhanakshirur, R.R., Pillai, P., Tabib, R.A., Patil, U. & Mudenagudi, U. 2019. A Framework for Lane Prediction on Unstructured Roads. In: Advances in Signal Processing and Intelligent Recognition Systems. pp. 178–189. Available at: https://doi.org/10.1007/978-981-13-5758-9_16.

Dima, C., Vandapel, N. & Hebert, M. 2004. Classifier fusion for outdoor obstacle detection. In: IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004, vol. 1. pp. 665-671. Available at: https://doi.org/10.1109/ROBOT.2004.1307225.

Durst, P.J., Monroe, G., Bethel, C.L., Anderson, D.T. & Carruth, D.W. 2018. A history and overview of mobility modeling for autonomous unmanned ground vehicles. In: Autonomous Systems: Sensors, Vehicles, Security, and the Internet of Everything, vol. 10643. SPIE, pp. 103–113. Available at: https://doi.org/10.1117/12.2309570.

Fakhfakh, M., Chaari, L. & Fakhfakh, N. 2020. Bayesian curved lane estimation for autonomous driving. Journal of Ambient Intelligence and Humanized Computing, 11(10), pp. 4133–4143. Available at: https://doi.org/10.1007/s12652-020-01688-7.

Fan, J., Yan, P., Li, R., Liu, Y., Wang, F., Liu, Y. & Chen, C. 2024. Decoupled Adaptive Motion Control for Unmanned Tracked Vehicles in the Leader-Following Task. World Electric Vehicle Journal, 15(6). Available at: https://doi.org/10.3390/wevj15060239.

Fareh, R., Khadraoui, S., Abdallah, M.Y., Baziyad, M. & Bettayeb, M. 2021. Active disturbance rejection control for robotic systems: A review. Mechatronics, 80, p. 102671. Available at: https://doi.org/10.1016/j.mechatronics.2021.102671.

Feng, Z., Zhang, S., Kunert, M. & Wiesbeck, W. 2019. Applying Neural Networks with a High-Resolution Automotive Radar for Lane Detection. In: AmE 2019 - Automotive meets Electronics; 10th GMM-Symposium. pp. 1-6. Available at: https://ieeexplore.ieee.org/document/8727838.

Gadekar, A., Fulsundar, S., Deshmukh, P., Aher, J., Kataria, K., Patel, V. & Barve, S. 2023. Rakshak: A modular unmanned ground vehicle for surveillance and logistics operations. Cognitive Robotics, 3, pp. 23–33. Available at: https://doi.org/10.1016/j.cogr.2023.02.001.

Gao, H., Yang, H., Zhang, X., Ren, X., Liang, F., Yan, R., Liu, Q., Hu, M., Zhang, F., Gao, J., Bao, S., Li, K., Li, D. & Wang, D. 2025. Longitudinal velocity control of autonomous driving based on extended state observer. CAAI Transactions on Intelligence Technology, 10(1), pp. 36–46. Available at: https://doi.org/10.1049/cit2.12397.

Gao, Z. 2006. Active disturbance rejection control: a paradigm shift in feedback control system design. In: 2006 American Control Conference. pp. 7 pp.-. Available at: https://doi.org/10.1109/ACC.2006.1656579.

Gao, Z., Hu, S. & Jiang, F. 2001. A novel motion control design approach based on active disturbance rejection. In: Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228), vol. 5. IEEE, pp. 4877-4882. Available at: https://doi.org/10.1109/CDC.2001.980980.

Ghanem, S., Kanungo, P., Panda, G., Satapathy, S.C. & Sharma, R. 2023. Lane detection under artificial colored light in tunnels and on highways: an IoT-based framework for smart city infrastructure. Complex & Intelligent Systems, 9(4), pp. 3601-3612. Available at: https://doi.org/10.1007/s40747-021-00381-2.

MediaPipe. [online]. Available at: https://developers.google.com/mediapipe/solutions/vision/pose_landmarker/ [Accessed: 09 July 2025].

Guo, B.Z. & Zhao, Z.L. 2015. Active disturbance rejection control: Theoretical perspectives. Communications in Information and Systems, 15(3), pp. 361-421. Available at: https://doi.org/10.4310/CIS.2015.v15.n3.a3.

Han, J. 2009. From PID to Active Disturbance Rejection Control. IEEE Transactions on Industrial Electronics, 56(3), pp. 900–906. Available at: https://doi.org/10.1109/TIE.2008.2011621.

Horri, N., Holderbaum, W. & Giulietti, F. 2024. Challenges in the Guidance, Navigation and Control of Autonomous and Transport Vehicles. Applied Sciences, 14(15). Available at: https://doi.org/10.3390/app14156635.

Hu, J.w., Zheng, B.y., Wang, C., Zhao, C.h., Hou, X.l., Pan, Q. & Xu, Z. 2020. A survey on multi-sensor fusion based obstacle detection for intelligent ground vehicles in off-road environments. Frontiers of Information Technology & Electronic Engineering, 21(5), pp. 675–692. Available at: https://doi.org/10.1631/FITEE.1900518.

Hung, N., Rego, F., Quintas, J., Cruz, J., Jacinto, M., Souto, D., Potes, A., Sebastiao, L. & Pascoal, A. 2023. A review of path following control strategies for autonomous robotic vehicles: Theory, simulations, and experiments. Journal of Field Robotics, 40(3), pp. 747–779. Available at: https://doi.org/10.1002/rob.22142.

Hussain, M. 2024. YOLOv1 to v8: Unveiling Each Variant-A Comprehensive Review of YOLO. IEEE Access, 12, pp. 42816-42833. Available at: https://doi.org/10.1109/ACCESS.2024.3378568.

Islam, F., Nabi, M. & Ball, J.E. 2022. Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review. Sensors, 22(21), p. 8463. Available at: https://doi.org/10.3390/s22218463.

Islam, M.J., Hong, J. & Sattar, J. 2019. Person-following by autonomous robots: A categorical overview. The International Journal of Robotics Research, 38(14), pp. 1581-1618. Available at: https://doi.org/10.1177/0278364919881683.

Jin, X., Lv, H., He, Z., Li, Z., Wang, Z. & Ikiela, N.V.O. 2023. Design of Active Disturbance Rejection Controller for Trajectory-Following of Autonomous Ground Electric Vehicles. Symmetry, 15(9), p. 1786. Available at: https://doi.org/10.3390/sym15091786.

Kang, N., Han, Y., Guan, T. & Wang, S. 2022. Improved ADRC-Based Autonomous Vehicle Path-Tracking Control Study Considering Lateral Stability. Applied Sciences, 12(9). Available at: https://doi.org/10.3390/app12094660.

Kayacan, E., Ramon, H. & Saeys, W. 2015. Robust Trajectory Tracking Error Model-Based Predictive Control for Unmanned Ground Vehicles. IEEE/ASME Transactions on Mechatronics, 21(2), pp. 806–814. Available at: https://doi.org/10.1109/TMECH.2015.2492984.

Kebbati, Y., Ait-Oufroukh, N., Ichalal, D. & Vigneron, V. 2023. Lateral control for autonomous wheeled vehicles: A technical review. Asian Journal of Control, 25(4), pp. 2539-2563. Available at: https://doi.org/10.1002/asjc.2980.

Kulkarni, S., Deshmukh, S., Fernandes, F., Patil, A. & Jabade, V. 2023. PoseAnalyser: A Survey on Human Pose Estimation. SN Computer Science, 4(2), p. 136. Available at: https://doi.org/10.1007/s42979-022-01567-2.

Kumar, G.A., Lee, J.H., Hwang, J., Park, J., Youn, S.H. & Kwon, S. 2020. LiDAR and Camera Fusion Approach for Object Distance Estimation in Self-Driving Vehicles. Symmetry, 12(2), p. 324. Available at: https://doi.org/10.3390/sym12020324.

Lee, C. & Moon, J.H. 2018. Robust Lane Detection and Tracking for Real-Time Applications. IEEE Transactions on Intelligent Transportation Systems, 19(12), pp. 4043-4048. Available at: https://doi.org/10.1109/TITS.2018.2791572.

Li, D., Wu, S., Zhao, Y., Li, Z. & Gong, J. 2021. A Hierarchical Path Tracking Method for High-speed Unmanned Tracked Vehicle. In: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC). IEEE, pp. 38–43. Available at: https://doi.org/10.1109/ITSC48978.2021.9564774.

Li, Y. & Xu, L. 2024. Panoptic perception for autonomous driving: A survey. arXiv preprint, abs/2408.15388. Available at: https://doi.org/10.48550/arXiv.2408.15388.

Li, Z. 2013. Guidance, control and estimation of autonomous vehicle systems. University of Illinois at Urbana-Champaign. [online]. Available at: https://www.ideals.illinois.edu/items/46890 [Accessed: 15 June 2025].

Liu, L., Chen, X., Zhu, S. & Tan, P. 2021a. CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV). pp. 3753-3762. Available at: https://doi.org/10.1109/ICCV48922.2021.00375.

Liu, M., Deng, X., Lei, Z., Jiang, C. & Piao, C. 2021b. Autonomous Lane Keeping System: Lane Detection, Tracking and Control on Embedded System. Journal of Electrical Engineering & Technology, 16(1), pp. 569–578. Available at: https://doi.org/10.1007/s42835-020-00570-y.

Liu, M., Xu, Y., Lin, X., Tan, Y., Pu, Y., Li, W. & Oetomo, D. 2024. On Active Disturbance Rejection Control for Unmanned Tracked Ground Vehicles with Nonsmooth Disturbances. Unmanned Systems, 12(06), pp. 1023–1037. Available at: https://doi.org/10.1142/S2301385024500353.

Liu, Q., Li, Z., Yuan, S., Zhu, Y. & Li, X. 2021c. Review on Vehicle Detection Technology for Unmanned Ground Vehicles. Sensors, 21(4), p. 1354. Available at: https://doi.org/10.3390/s21041354.

Madonski, R., Ramirez-Neria, M., Stanković, M., Yang, J. & Li, S. 2019. On vibration suppression and trajectory tracking in largely uncertain torsional system: An error-based ADRC approach. Mechanical Systems and Signal Processing, 134, p. 106300. Available at: https://doi.org/10.1016/j.ymssp.2019.106300.

Man, C.K.Y.L.L., Koonjul, Y. & Nagowah, L. 2018. A low cost autonomous unmanned ground vehicle. Future Computing and Informatics Journal, 3(2), pp. 304-320. Available at: https://doi.org/10.1016/j.fcij.2018.10.001.

Marino, R., Scalzi, S., Orlando, G. & Netto, M. 2009. A nested PID steering control for lane keeping in vision based autonomous vehicles. In: 2009 American Control Conference. pp. 2885-2890. Available at: https://doi.org/10.1109/ACC.2009.5160343.

Munir, F., Azam, S., Jeon, M., Lee, B.G. & Pedrycz, W. 2022. LDNet: End-to-End Lane Marking Detection Approach Using a Dynamic Vision Sensor. IEEE Transactions on Intelligent Transportation Systems, 23(7), pp. 9318–9334. Available at: https://doi.org/10.1109/TITS.2021.3102479.

Ni, J., Chen, Y., Chen, Y., Zhu, J., Ali, D. & Cao, W. 2020. A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods. Applied Sciences, 10(8), p. 2749. Available at: https://doi.org/10.3390/app10082749.

Ni, J., Hu, J. & Xiang, C. 2018. Unmanned Ground Vehicles: An Introduction. In: Design and Advanced Robust Chassis Dynamics Control for X-by-Wire Unmanned Ground Vehicle. Springer, pp. 1–19. Available at: https://doi.org/10.1007/978-3-031-01496-3_1.

Ni, J., Hu, J. & Xiang, C. 2021. A review for design and dynamics control of unmanned ground vehicle. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 235(4), pp. 1084-1100. Available at: https://doi.org/10.1177/0954407020912097.

Oğuz, E., Küçükmanisa, A., Duvar, R. & Urhan, O. 2022. A deep learning based fast lane detection approach. Chaos, Solitons & Fractals, 155, p. 111722. Available at: https://doi.org/10.1016/j.chaos.2021.111722.

Parmar, Y., Natarajan, S. & Sobha, G. 2019. Deeprange: deep-learning-based object detection and ranging in autonomous driving. IET Intelligent Transport Systems, 13(8), pp. 1256–1264. Available at: https://doi.org/10.1049/iet-its.2018.5144.

Philion, J. 2019. FastDraw: Addressing the Long Tail of Lane Detection by Adapting a Sequential Prediction Network. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 11574-11583. Available at: https://doi.org/10.1109/CVPR.2019.01185.

Ramírez-Neria, M., González-Sierra, J., Madonski, R., Ramírez-Juárez, R., Hernandez-Martinez, E. & Fernández-Anaya, G. 2023a. Leader-Follower Formation and Disturbance Rejection Control for Omnidirectional Mobile Robots. Robotics, 12(5), p. 122. Available at: https://doi.org/10.3390/robotics12050122.

Ramírez-Neria, M., Luviano-Juárez, A., Madonski, R., Ramírez-Juárez, R., Lozada-Castillo, N. & Gao, Z. 2023b. Leader-Follower ADRC Strategy for Omnidirectional Mobile Robots without Time-Derivatives in the Tracking Controller. In: 2023 American Control Conference (ACC). IEEE, pp. 405–410. Available at: https://doi.org/10.23919/ACC55779.2023.10156648.

Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. 2016. You Only Look Once: Unified, Real-Time Object Detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 779-788. Available at: https://doi.org/10.1109/CVPR.2016.91.

Rizk, H., Chaibet, A. & Kribèche, A. 2023. Model-Based Control and Model-Free Control Techniques for Autonomous Vehicles: A Technical Survey. Applied Sciences, 13(11), p. 6700. Available at: https://doi.org/10.3390/app13116700.

Rubio, F., Valero, F. & Llopis-Albert, C. 2019. A review of mobile robots: Concepts, methods, theoretical framework, and applications. International Journal of Advanced Robotic Systems, 16(2), p. 1729881419839596. Available at: https://doi.org/10.1177/1729881419839596.

Ruslan, N.A.I., Amer, N.H., Hudha, K., Kadir, Z.A., Ishak, S.A.F.M. & Dardin, S.M.F.S. 2023. Modelling and control strategies in path tracking control for autonomous tracked vehicles: A review of state of the art and challenges. Journal of Terramechanics, 105, pp. 67–79. Available at: https://doi.org/10.1016/j.jterra.2022.10.003.

Sabiha, A.D., Kamel, M.A., Said, E. & Hussein, W.M. 2022. ROS-based trajectory tracking control for autonomous tracked vehicle using optimized backstepping and sliding mode control. Robotics and Autonomous Systems, 152, p. 104058. Available at: https://doi.org/10.1016/j.robot.2022.104058.

Samak, C.V., Samak, T.V. & Kandhasamy, S. 2021. Control strategies for autonomous vehicles. In: Autonomous Driving and Advanced Driver-Assistance Systems (ADAS). CRC Press, pp. 37–86. Available at: https://doi.org/10.1201/9781003048381-2.

Sang, N. & Chen, L. 2020. Design of an active front steering system for a vehicle using an active disturbance rejection control method. Science Progress, 103(1), p. 0036850419883565. Available at: https://doi.org/10.1177/0036850419883565.

Santoso, F., Garratt, M.A. & Anavatti, S.G. 2020. State-of-the-Art Integrated Guidance and Control Systems in Unmanned Vehicles: A Review. IEEE Systems Journal, 15(3), pp. 3312–3323. Available at: https://doi.org/10.1109/JSYST.2020.3007428.

Sarcinelli-Filho, M. & Carelli, R. 2023. Control of Ground and Aerial Robots. Springer. Available at: https://doi.org/10.1007/978-3-031-23088-2.

Schulte, J., Kocherovsky, M., Paul, N., Pleune, M. & Chung, C.J. 2022. Autonomous Human-Vehicle Leader-Follower Control Using Deep-Learning-Driven Gesture Recognition. Vehicles, 4(1), pp. 243–258. Available at: https://doi.org/10.3390/vehicles4010016.

Sebastian, B. & Ben-Tzvi, P. 2019a. Active Disturbance Rejection Control for Handling Slip in Tracked Vehicle Locomotion. Journal of Mechanisms and Robotics, 11(2), p. 021003. Available at: https://doi.org/10.1115/1.4042347.

Sebastian, B. & Ben-Tzvi, P. 2019b. Physics Based Path Planning for Autonomous Tracked Vehicle in Challenging Terrain. Journal of Intelligent & Robotic Systems, 95, pp. 511-526. Available at: https://doi.org/10.1007/s10846-018-0851-3.

Sethi, I.K. 2024. Autonomous Vehicles and Systems: A Technological and Societal Perspective. CRC Press. Available at: https://doi.org/10.1201/9781032629537.

Shafaei, S. & Mousazadeh, H. 2023. On the power characteristics of an unmanned tracked vehicle for autonomous transportation of agricultural payloads. Journal of Terramechanics, 109, pp. 21–36. Available at: https://doi.org/10.1016/j.jterra.2023.05.006.

Singhal, A., Mohta, V., Jha, A., Khandelwal, Y., Agrawal, D., Kowshik, S., Agarwal, S., Shrivastava, S., Lodhi, V. & Chakravarty, D. 2019. Real-time lane detection, fitting and navigation for unstructured environments. In: 2019 International Conference on Image and Video Processing, and Artificial Intelligence, vol. 11321. SPIE, pp. 146-151. Available at: https://doi.org/10.1117/12.2547333.

Sira-Ramírez, H., Castro-Linares, R. & Puriel-Gil, G. 2014. An Active Disturbance Rejection Approach to Leader-Follower Controlled Formation. Asian Journal of Control, 16(2), pp. 382–395. Available at: https://doi.org/10.1002/asjc.714.

Sridevi, M. & Harish, M. 2024. Computer Vision based Panoptic Driving Perception under Various Weather Conditions. Procedia Computer Science, 237, pp. 803-810. Available at: https://doi.org/10.1016/j.procs.2024.05.168.

Stanković, M., Madonski, R. & Manojlović, S. 2024. Systematic design of ADRC-based unmanned tracked vehicle trajectory tracking with FPGA-in-the-loop validation. Military Technical Courier, 72(4), pp. 1700-1725. Available at: https://doi.org/10.5937/vojtehg72-49983.

Stanković, M.R., Rapaić, M.R., Manojlović, S.M., Mitrović, S.T., Simić, S.M. & Naumović, M.B. 2019. Optimised active disturbance rejection motion control with resonant extended state observer. International Journal of Control, 92(8), pp. 1815-1826. Available at: https://doi.org/10.1080/00207179.2017.1414308.

Sultana, S., Ahmed, B., Paul, M., Islam, M.R. & Ahmad, S. 2023. Vision-Based Robust Lane Detection and Tracking in Challenging Conditions. IEEE Access, 11, pp. 67938-67955. Available at: https://doi.org/10.1109/ACCESS.2023.3292128.

Sun, Y., Wang, L., Chen, Y. & Liu, M. 2019. Accurate Lane Detection with Atrous Convolution and Spatial Pyramid Pooling for Autonomous Driving. In: 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO). pp. 642-647. Available at: https://doi.org/10.1109/ROBIO49542.2019.8961705.

Tagliavini, L., Colucci, G., Botta, A., Cavallone, P., Baglieri, L. & Quaglia, G. 2022. Wheeled mobile robots: state of the art overview and kinematic comparison among three omnidirectional locomotion strategies. Journal of Intelligent & Robotic Systems, 106(3), p. 57. Available at: https://doi.org/10.1007/s10846-022-01745-7.

Teji, M.D., Zou, T. & Zeleke, D.S. 2023. A Survey of Off-Road Mobile Robots: Slippage Estimation, Robot Control, and Sensing Technology. Journal of Intelligent & Robotic Systems, 109(2), p. 38. Available at: https://doi.org/10.1007/s10846-023-01968-2.

Terven, J., Córdova-Esparza, D.M. & Romero-González, J.A. 2023. A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas. Machine Learning and Knowledge Extraction, 5(4), pp. 1680-1716. Available at: https://doi.org/10.3390/make5040083.

Thrun, S., Montemerlo, M., Dahlkamp, H., Stavens, D., Aron, A., Diebel, J., Fong, P., Gale, J., Halpenny, M., Hoffmann, G., Lau, K., Oakley, C., Palatucci, M., Pratt, V., Stang, P., Strohband, S., Dupont, C., Jendrossek, L.E., Koelen, C., Markey, C., Rummel, C., van Niekerk, J., Jensen, E., Alessandrini, P., Bradski, G., Davies, B., Ettinger, S., Kaehler, A., Nefian, A. & Mahoney, P. 2006. Stanley: The robot that won the DARPA Grand Challenge. Journal of Field Robotics, 23(9), pp. 661-692. Available at: https://doi.org/10.1002/rob.20147.

Tian, D., Gao, J., Liu, C. & Shi, X. 2021. Simulation of Upward Jump Control for One-Legged Robot Based on QP Optimization. Sensors, 21(5), p. 1893. Available at: https://doi.org/10.3390/s21051893.

van der Sande, T. & Nijmeijer, H. 2017. From cooperative to autonomous vehicles. In: Sensing and Control for Autonomous Vehicles: Applications to Land, Water and Air Vehicles. Springer, pp. 435-452. Available at: https://doi.org/10.1007/978-3-319-55372-6_20.

Vera-Yanez, D., Pereira, A., Rodrigues, N., Molina, J.P., García, A.S. & Fernández-Caballero, A. 2024. Optical Flow-Based Obstacle Detection for Mid-Air Collision Avoidance. Sensors, 24(10), p. 3016. Available at: https://doi.org/10.3390/s24103016.

Vijayakumar, A. & Vairavasundaram, S. 2024. Yolo-based object detection models: A review and its applications. Multimedia Tools and Applications, 83(35), pp. 83535-83574. Available at: https://doi.org/10.1007/s11042-024-18872-y.

Wang, H., Zuo, Z., Wang, Y., Yang, H. & Chang, S. 2021. Composite nonlinear extended state observer and its application to unmanned ground vehicles. Control Engineering Practice, 109, p. 104731. Available at: https://doi.org/10.1016/j.conengprac.2021.104731.

Wang, H., Zuo, Z., Wang, Y., Yang, H. & Hu, C. 2022. Estimator-Based Turning Control for Unmanned Ground Vehicles: An Anti-Peak Extended State Observer Approach. IEEE Transactions on Vehicular Technology, 71(12), pp. 12489–12498. Available at: https://doi.org/10.1109/TVT.2022.3195637.

Wang, H., Zuo, Z., Xue, W., Wang, Y. & Yang, H. 2024a. Switching Longitudinal and Lateral Semi-decoupled Active Disturbance Rejection Control for Unmanned Ground Vehicles. IEEE Transactions on Industrial Electronics, 71(3), pp. 3034-3043. Available at: https://doi.org/10.1109/TIE.2023.3265048.

Wang, X., Wang, Y., Sun, Q., Chen, Y. & Al-Zahran, A. 2024b. Adaptive robust control of unmanned tracked vehicles for trajectory tracking based on constraint modeling and analysis. Nonlinear Dynamics, 112(11), pp. 9117-9135. Available at: https://doi.org/10.1007/s11071-024-09514-x.

Wang, Z. 2024. A survey on convex optimization for guidance and control of vehicular systems. Annual Reviews in Control, 57, p. 100957. Available at: https://doi.org/10.1016/j.arcontrol.2024.100957.

Wu, D., Liao, M.W., Zhang, W.T., Wang, X.G., Bai, X., Cheng, W.Q. & Liu, W.Y. 2022. YOLOP: You Only Look Once for Panoptic Driving Perception. Machine Intelligence Research, 19(6), pp. 550–562. Available at: https://doi.org/10.1007/s11633-022-1339-y.

Wu, Z., Qiu, K., Yuan, T. & Chen, H. 2021. A method to keep autonomous vehicles steadily drive based on lane detection. International Journal of Advanced Robotic Systems, 18(2). Available at: https://doi.org/10.1177/17298814211002974.

Xia, Y., Pu, F., Li, S. & Gao, Y. 2016. Lateral Path Tracking Control of Autonomous Land Vehicle Based on ADRC and Differential Flatness. IEEE Transactions on Industrial Electronics, 63(5), pp. 3091-3099. Available at: https://doi.org/10.1109/TIE.2016.2531021.

Xing, Y., Lv, C., Chen, L., Wang, H., Wang, H., Cao, D., Velenis, E. & Wang, F.Y. 2018. Advances in Vision-Based Lane Detection: Algorithms, Integration, Assessment, and Perspectives on ACP-Based Parallel Vision. IEEE/CAA Journal of Automatica Sinica, 5(3), pp. 645-661. Available at: https://doi.org/10.1109/JAS.2018.7511063.

Yang, Y. 2024. A Review of Lane Detection in Autonomous Vehicles. Journal of Advances in Engineering and Technology, 1(4), pp. 30–36. Available at: https://doi.org/10.62177/jaet.v1i4.130.

Yao, Q., Tian, Y., Wang, Q. & Wang, S. 2020. Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges. IEEE Access, 8, pp. 211-222. Available at: https://doi.org/10.1109/ACCESS.2020.3020075.

Yu, X. & Marinov, M. 2020. A Study on Recent Developments and Issues with Obstacle Detection Systems for Automated Vehicles. Sustainability, 12(8), p. 3281. Available at: https://doi.org/10.3390/su12083281.

Zakaria, N.J., Shapiai, M.I., Ghani, R.A., Yassin, M.N.M., Ibrahim, M.Z. & Wahid, N. 2023. Lane Detection in Autonomous Vehicles: A Systematic Review. IEEE Access, 11, pp. 3729–3765. Available at: https://doi.org/10.1109/ACCESS.2023.3234442.

Zhan, J., Luo, Y., Guo, C., Wu, Y., Meng, J. & Liu, J. 2024. YOLOPX: Anchor-free multi-task learning network for panoptic driving perception. Pattern Recognition, 148, p. 110152. Available at: https://doi.org/10.1016/j.patcog.2023.110152.

Zhang, J., Yang, X., Wang, W., Guan, J., Ding, L. & Lee, V.C. 2023. Automated guided vehicles and autonomous mobile robots for recognition and tracking in civil engineering. Automation in Construction, 146, p. 104699. Available at: https://doi.org/10.1016/j.autcon.2022.104699.

Zhang, L., Jiang, F., Kong, B., Yang, J. & Wang, C. 2021a. Real-Time Lane Detection by Using Biologically Inspired Attention Mechanism to Learn Contextual Information. Cognitive Computation, 13, pp. 1333-1344. Available at: https://doi.org/10.1007/s12559-021-09935-5.

Zhang, R., Wu, Y., Gou, W. & Chen, J. 2021b. RS-Lane: A Robust Lane Detection Method Based on ResNeSt and Self-Attention Distillation for Challenging Traffic Situations. Journal of Advanced Transportation, 2021(1), p. 7544355. Available at: https://doi.org/10.1155/2021/7544355.

Zhang, X., Zhou, M., Qiu, P., Huang, Y. & Li, J. 2019. Radar and vision fusion for the real-time obstacle detection and identification. Industrial Robot-an International Journal, 46(3), pp. 391–395. Available at: https://doi.org/10.1108/IR-06-2018-0113.

Zhao, Y., Wang, J., Cao, G., Yuan, Y., Yao, X. & Qi, L. 2023. Intelligent Control of Multilegged Robot Smooth Motion: A Review. IEEE Access, 11, pp. 86645-86685. Available at: https://doi.org/10.1109/ACCESS.2023.3304992.

Zheng, C., Wu, W., Chen, C., Yang, T., Zhu, S., Shen, J., Kehtarnavaz, N. & Shah, M. 2023. Deep Learning-based Human Pose Estimation: A Survey. ACM Computing Surveys, 56(1), pp. 1–37. Available at: https://doi.org/10.1145/3603618.

Zheng, Q. & Gao, Z. 2010. On practical applications of active disturbance rejection control. In: Proceedings of the 29th Chinese Control Conference. IEEE, pp. 6095-6100. Available at: https://ieeexplore.ieee.org/abstract/document/5572922.

Zheng, Q. & Gao, Z. 2016. Active disturbance rejection control: between the formulation in time and the understanding in frequency. Control Theory and Technology, 14, pp. 250–259. Available at: https://doi.org/10.1007/s11768-016-6059-9.

Zou, T., Angeles, J. & Hassani, F. 2018. Dynamic modeling and trajectory tracking control of unmanned tracked vehicles. Robotics and Autonomous Systems, 110, pp. 102–111. Available at: https://doi.org/10.1016/j.robot.2018.09.008.

Zuo, Z., Yang, M., Wang, H., Wang, Y., Wang, L. & Luo, X. 2021. A lateral control strategy for unmanned ground vehicles with model predictive control and active disturbance rejection control. Transactions of the Institute of Measurement and Control, 43(15), pp. 3473–3482. Available at: https://doi.org/10.1177/01423312211025337.

Objavljeno
2025/10/16
Broj časopisa
Vol. 73 Br. 3 (2025): jul-septembar
Rubrika
Pregledni radovi

Sva prava zadržana (c) 2025 Salem-bilal AMOKRANE, Momir Stanković, Stojadin Manojlović, Ahmed Taki-Eddine Benyahia

Creative Commons License

Ovaj rad je pod Creative Commons Autorstvo 4.0 međunarodnom licencom.

Vojnotehnički glasnik omogućava otvoreni pristup i, u skladu sa preporukom CEON-a, primenjuje Creative Commons odredbe o autorskim pravima:

Autori koji objavljuju u Vojnotehničkom glasniku pristaju na sledeće uslove:

  1. Autori zadržavaju autorska prava i pružaju časopisu pravo prvog objavljivanja rada i licenciraju ga Creative Commons licencom koja omogućava drugima da dele rad uz uslov navođenja autorstva i izvornog objavljivanja u ovom časopisu.
  2. Autori mogu izraditi zasebne, ugovorne aranžmane za neekskluzivnu distribuciju rada objavljenog u časopisu (npr. postavljanje u institucionalni repozitorijum ili objavljivanje u knjizi), uz navođenje da je rad izvorno objavljen u ovom časopisu.
  3. Autorima je dozvoljeno i podstiču se da postave objavljeni rad onlajn (npr. u institucionalnom repozitorijumu ili na svojim internet stranicama) pre i tokom postupka prijave priloga, s obzirom da takav postupak može voditi produktivnoj razmeni ideja i ranijoj i većoj citiranosti objavljenog rada (up. Efekat otvorenog pristupa).