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Optimal Lane Merging for AGV

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New Trends in Robot Control

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 270))

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Abstract

This paper addresses the generation of an optimal constrained trajectory for lane merging tasks. The developed algorithm ensure a safe and a less fuel consumption for autonomous driving. The security is established with the restriction of both the lateral and the longitudinal AGV’s position inside a safe zone while accomplishing a lane change to overtake an obstacle or to follow a lead vehicle. However the path’s optimality is carried out by minimizing the lateral and the longitudinal cost functions. The generated trajectory is then tracked accurately with an adaptive computed torque controller. The whole approach is then validated with numerical simulations.

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References

  1. Arora, S., Raina, A.K., Mittal, A.K.: Collision avoidance among agvs at junctions. In: Proceedings of the IEEE on Intelligent Vehicles Symposium IV, pp. 585–589 (2000)

    Google Scholar 

  2. Awal, T., Kulik, L., Ramamohanrao, K.: Optimal traffic merging strategy for communication-and sensor-enabled vehicles. In: 16th International IEEE Conference on Intelligent Transportation Systems-(ITSC), pp. 1468–1474 (2013)

    Google Scholar 

  3. Broggi, A., Medici, P., Zani, P., Coati, A., Panciroli, M.: Autonomous vehicles control in the vislab intercontinental autonomous challenge. Annu. Rev. Control 36(1), 161–171 (2012)

    Article  Google Scholar 

  4. Dolk, V., Ouden, J.D., Steeghs, S., Devanesan, J.G., Badshah, I., Sudhakaran, A., Elferink, K., Chakraborty, D.: Cooperative automated driving for various traffic scenarios: experimental validation in the GCDC 2016. IEEE Trans. Intell. Transp. Syst. 19(4), 1308–1321 (2018)

    Article  Google Scholar 

  5. Ercan, Z., Carvalho, A., Tseng, H.E., Gökaşan, M., Borrelli, F.: A predictive control framework for torque-based steering assistance to improve safety in highway driving. Veh. Syst. Dyn. 56(5), 810–831 (2018)

    Article  Google Scholar 

  6. Hatipoglu, C., Ozguner, U., Redmill, K.A.: Automated lane change controller design. IEEE Trans. Intell. Transp. Syst. 4(1), 13–22 (2003)

    Article  Google Scholar 

  7. Isaksson-Hellman, I., Lindman, M.: An evaluation of the real-world safety effect of a lane change driver support system and characteristics of lane change crashes based on insurance claims data. Traffic Inj. Prev. 19(sup1), S104–S111 (2018)

    Article  Google Scholar 

  8. Jula, H., Kosmatopoulos, E.B., Ioannou, P.A.: Collision avoidance analysis for lane changing and merging. IEEE Trans. Veh. Technol. 49(6), 2295–2308 (2000)

    Article  Google Scholar 

  9. Kesting, A., Treiber, M., Helbing, D.: General lane-changing model mobil for car-following models. Transp. Res. Rec.: J. Transp. Res. Board 1999, 86–94 (2007)

    Article  Google Scholar 

  10. Kost, F.: Basic principles of vehicle dynamics. In: Fundamentals of Automotive and Engine Technology, pp. 114–129. Springer (2014)

    Google Scholar 

  11. Li, X., Sun, Z., He, Z., Zhu, Q., Liu, D.: A practical trajectory planning framework for autonomous ground vehicles driving in urban environments. In: IEEE Intelligent Vehicles Symposium (IV), pp. 1160–1166 (2015)

    Google Scholar 

  12. Lin, Li, Liang, Su, Chen, Shaozhen, Zhang, Yuanbiao: Optimization of the merging area after toll. Eng. Manag. Res. 7(1), 17 (2018)

    Article  Google Scholar 

  13. Menhour, L., d’Andréa Novel, B., Fliess, M., Gruyer, D., Mounier, H.: An efficient model-free setting for longitudinal and lateral vehicle control: validation through the interconnected pro-sivic/rtmaps prototyping platform. IEEE Trans. Intell. Transp. Syst. 19(2), 461–475 (2018)

    Google Scholar 

  14. Miley, J.: New algorithm allows autonomous cars to change lanes more like humans. Technical Report, MIT, https://interestingengineering.com/new-algorithm-allows-autonomous-cars-to-change-lanes-more-like-humans (2018)

  15. Naranjo, J.E., Gonzalez, C., Garcia, R., De Pedro, T.: Lane-change fuzzy control in autonomous vehicles for the overtaking maneuver. IEEE Trans. Intell. Transp. Syst. 9(3), 438–450 (2008)

    Article  Google Scholar 

  16. Norouzi, A., Kazemi, R., Azadi, S.: Vehicle lateral control in the presence of uncertainty for lane change maneuver using adaptive sliding mode control with fuzzy boundary layer. Proc. Inst. Mech. Eng. Part I: J. Syst. Control. Eng. 232(1), 12–28 (2018)

    Google Scholar 

  17. Okuda, H., Harada, K., Suzuki, T., Saigo, S., Inoue, S.: Design of automated merging control by minimizing decision entropy of drivers on main lane. In: IEEE Intelligent Vehicles Symposium (IV), pp. 640–646 (2017)

    Google Scholar 

  18. Osman, K., Ghommam, J., Saad, M.: Combined road following control and automatic lane keeping for automated guided vehicles. In: 14th IEEE International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 1–6 (2016)

    Google Scholar 

  19. Raja, P., Pugazhenthi, S.: Optimal path planning of mobile robots: a review. Int. J. Phys. Sci. 7(9), 1314–1320 (2012)

    Article  Google Scholar 

  20. Rajamani, R.: Vehicle Dynamics and Control. Springer Science & Business Media (2011)

    Google Scholar 

  21. Rios-Torres, J., Malikopoulos, A., Pisu, P.: Online optimal control of connected vehicles for efficient traffic flow at merging roads. In: 18th IEEE International Conference on Intelligent Transportation Systems (ITSC), pp. 2432–2437 (2015)

    Google Scholar 

  22. Shamir, T.: How should an autonomous vehicle overtake a slower moving vehicle: design and analysis of an optimal trajectory. IEEE Trans. Autom. Control 49(4), 607–610 (2004)

    Article  MathSciNet  Google Scholar 

  23. Shang, W.-W., Cong, S., Ge, Y.: Adaptive computed torque control for a parallel manipulator with redundant actuation. Robotica 30(3), 457–466 (2012)

    Article  Google Scholar 

  24. Tawari, A., Sivaraman, S., Trivedi, M.M., Shannon, T., Tippelhofer, M.: Looking-in and looking-out vision for urban intelligent assistance: Estimation of driver attentive state and dynamic surround for safe merging and braking. In: IEEE Intelligent Vehicles Symposium, pp. 115–120 (2014)

    Google Scholar 

  25. Wang, X.: Solving optimal control problems with matlab: indirect methods. https://www.researchgate.net/publication/239575747_Solving_optimal_control_problems_with_MATLAB_Indirect_methods (2009)

  26. Werling, M., Kammel, S., Ziegler, J., Gröll, L.: Optimal trajectories for time-critical street scenarios using discretized terminal manifolds. Int. J. Robot. Res. 31(3), 346–359 (2012)

    Article  Google Scholar 

  27. Yao, W., Zhao, X., Yu, Y., Fang, Y., Wang, C., Yang, T.: A leader-path-following formation system for agvs with multi-sensor data fusion based vehicle tracking. In: IOP Conference Series: Materials Science and Engineering, vol. 235, pp. 012006. IOP Publishing (2017)

    Google Scholar 

  28. Ye, E., Ramezani, M.: Lane distribution optimisation of autonomous vehicles for highway congestion control. Technical Report (2018)

    Google Scholar 

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Author information

Authors and Affiliations

  1. CEM-Lab and the National School of Engineering of Sousse, University of Sousse, Sousse, Tunisia

    Kawther Osman

  2. Departement of Electrical and Computer Engineering, College of Engineering, Sultan Qaboos University, Al Khoudh, Mascut, Oman

    Jawhar Ghommam

  3. Department of Electrical Engineering, Ecole de Technologie Superieure, Montreal, Canada

    Maarouf Saad

Authors
  1. Kawther Osman
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  2. Jawhar Ghommam
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  3. Maarouf Saad
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Corresponding author

Correspondence to Kawther Osman .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Sultan Qaboos University, Al Khoudh, Oman

    Jawhar Ghommam

  2. National School of Engineers of Sfax, University of Sfax, Sfax, Tunisia

    Nabil Derbel

  3. Department of Engineering Design and Mathematics, University of the West of England, Bristol, UK

    Quanmin Zhu

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Osman, K., Ghommam, J., Saad, M. (2020). Optimal Lane Merging for AGV. In: Ghommam, J., Derbel, N., Zhu, Q. (eds) New Trends in Robot Control. Studies in Systems, Decision and Control, vol 270. Springer, Singapore. https://doi.org/10.1007/978-981-15-1819-5_10

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