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Research on Path Tracking Control Based on Optimal Look-Ahead Points

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Abstract

Pure pursuit tracking algorithms are a popular control method in the field of autonomous navigation, where the selection of a look-ahead point plays a crucial role in tracking performance. However, the computation of the look-ahead point involves issues that are challenging to describe precisely using mathematics. To enhance the tracking precision of vehicles on curved trajectories, we propose an improved optimal look-ahead point path tracking algorithm. This algorithm primarily seeks the optimal look-ahead point by considering both longitudinal look-ahead distance and lateral position offset. To begin, we employ the Deep Deterministic Policy Gradient (DDPG) algorithm to train vehicles to determine the optimal longitudinal look-ahead distance under various constant curvature and velocity conditions. Subsequently, by utilizing the optimal longitudinal look-ahead distance and the front-wheel steering angle, we construct a lateral deviation search region. Finally, we use an evaluation function to search for the optimal look-ahead point within this region. Simulation tests demonstrate that the proposed algorithm significantly improves tracking accuracy under varying curvature trajectory conditions.

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Data availability

The data that support the findings of this study are available on request from the corresponding author，upon reasonable request.

References

  • Ahn, J., Shin, S., Kim, M., & Park, J. (2021). Accurate path tracking by adjusting look-ahead point in pure pursuit method. International Journal of Automotive Technology, 22(1), 119–129.

    Article  Google Scholar 

  • Amidi, O., & Thorpe, C. E. (1991). Integrated mobile robot control. In: Mobile robots V; Proceedings of the Meeting, Boston, MA, Nov. 8, 9, 1990 (A93–19070 05–63). International Society for Optics and Photonics.

  • Andersen H, Chong Z J, Eng Y H, et al. (2016). Geometric path tracking algorithm for autonomous driving in pedestrian environment[C]//2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM). IEEE, 1669–1674.

  • Bu, X., Hou, Z., & Chi, R. (2013). Model free adaptive iterative learning control for farm vehicle path tracking. IFAC Proceedings Volumes, 46(20), 153–158.

    Article  Google Scholar 

  • Campbell, S. F. (2007). Steering control of an autonomous ground vehicle with application to the darpa urban challenge. massachusetts institute of technology.

  • Chen, I. M., & Chan, C. Y. (2021). Deep reinforcement learning based path tracking controller for autonomous vehicle. Proceedings of the Institution of Mechanical Engineers, Part d: Journal of Automobile Engineering, 235(2–3), 095440702095459.

    Google Scholar 

  • Coulter R C.(1992). Implementation of the pure pursuit path tracking algorithm. Carnegie Mellon University, The Robotics Institute.

  • Guo, K., & Guan, H. (1993). Modelling of driver/vehicle directional control system. Vehicle System Dynamics: International Journal of Vehicle Mechanics and Mobility, 22(3–4), 141–184.

    Article  Google Scholar 

  • Heredia, G., & Ollero, A. (2007). Stability of autonomous vehicle path tracking with pure delays in the control loop. Advanced Robotics, 21(1–2), 23–50.

    Article  Google Scholar 

  • Huang, P., Luo, X., & Zhang, Z. (2009). Headland turning control method simulation of autonomous agricultural machine based on improved pure pursuit model. Computer and Computing Technologies in Agriculture III, Third IFIP TC 12 International Conference, CCTA 2009, Bei**g, China, October 14–17, 2009, Revised Selected Papers. Springer, Berlin, Heidelberg.

  • Huaqiang, Z., Guodong, W., Yunfei, L. Ü., et al. (2020). Agricultural machinery automatic navigation control system based on improved pure tracking model. Nongye Jixie Xuebao/transactions of the Chinese Society of Agricultural Machinery, 51, 9.

    Google Scholar 

  • Kondo, M. , & Ajimine, A. (1968). Driver’s sight point and dynamics of the driver-vehicle-system related to it. 1

  • Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., & Tassa, Y., Silver, D., Wierstra, D. (2015). Continuous control with deep reinforcement learning. Computerence.

  • Loof, J., Besselink, I., & Nijmeijer, H. (2019). Automated lane changing with a controlled steering-wheel feedback torque for low lateral acceleration purposes. IEEE Transactions on Intelligent Vehicles, 4(4), 578–587.

    Article  Google Scholar 

  • Manav, A. C., Lazoglu, I., & Aydemir, E. (2022). Adaptive path-following control for autonomous semi-trailer docking. IEEE Transactions on Vehicular Technology, 71(1), 69–85.

    Article  Google Scholar 

  • Milani, S., Khayyam, H., Marzbani, H., Melek, W., Azad, N. L., & Jazar, R. N. (2020). Smart autodriver algorithm for real-time autonomous vehicle trajectory control. IEEE Transactions on Intelligent Transportation Systems, 23(3), 1984–1995.

    Article  Google Scholar 

  • Netto, M., Blosseville, J. M., Lusetti, B., & Mammar, S. (2006). A new robust control system with optimized use of the lane detection data for vehicle full lateral control under strong curvatures. 2006 IEEE Intelligent Transportation Systems Conference. IEEE.

  • Nguyen, P.T.-T., Yan, S.-W., Liao, J.-F., & Kuo, C.-H. (2021). Autonomous mobile robot navigation in sparse lidar feature environments. Applied Sciences, 11(13), 5963.

    Article  Google Scholar 

  • Qi, L., Jiao, X., & Wang, Z. (2021). Trajectory tracking control of intelligent vehicle based on ddpg method of reinforcement learning. China Journal of Highway and Transport, 34, 335–348.

    Google Scholar 

  • Sidhu, A. (2010). Development of an autonomous test driver and strategies for vehicle dynamics testing and lateral motion control. Fortschritte der Kieferorthopdie, 45(45), 428–434.

    Google Scholar 

  • Snider, J.M. (2009). Automatic steering methods for autonomous automobile path tracking.

  • Ungoren, A. Y., & Peng, H. (2005). An adaptive lateral preview driver model. Vehicle System Dynamics, 43(4), 245–259.

    Article  Google Scholar 

  • Wei, L., RuochenChenyang, W. X., Qing, Y., et al. (2019). Investigation on adaptive preview distance path tracking control with directional error compensation. Proceedings of the Institution of Mechanical Engineers, Part i: Journal of Systems and Control Engineering, 234(4), 484–500.

    Google Scholar 

  • Wenli, L., Fanke, Q., Daming L., et al. (2022). Highway lane change decision control model based on deep reinforcement learning. Automotive Safety and Energy, 13(4), 750–759.

    Google Scholar 

  • Wit, J. S. (2000). Vector pursuit path tracking for autonomous ground vehicles. (Doctoral dissertation, University of Florida.).

  • Wu, S. J., Chiang, H. H., Perng, J. W., Chen, C. J., Wu, B. F., & Lee, T. T. (2008). The heterogeneous systems integration design and implementation for lane kee** on a vehicle. IEEE Transactions on Intelligent Transportation Systems, 9(2), 246–263.

    Article  Google Scholar 

  • **e, J., Xu, X., Wang, F., & Chen, L. (2022). Modeling adaptive preview time of driver model for intelligent vehicles based on deep learning. Proceedings of the Institution of Mechanical Engineers, Part i. Journal of Systems and Control Engineering, 236(2), 355–369.

    Article  Google Scholar 

  • Yang, Y., Li, Y., Wen, X., et al. (2022). An optimal goal point determination algorithm for automatic navigation of agricultural machinery: Improving the tracking accuracy of the Pure Pursuit algorithm. Computers and Electronics in Agriculture, 194, 106760. https://doi.org/10.1016/j.compag.2022.106760

    Article  Google Scholar 

  • Yao, J., & Ge, Z. (2022). Path-tracking control strategy of unmanned vehicle based on DDPG algorithm. Sensors, 22(20), 7881.

    Article  Google Scholar 

  • Zhang, W., Bai, W., Wang, J., et al. (2018). Research on path tracking of intelligent vehicle based on optimal deviation control. Integrated Ferroelectrics, 191(1), 80–91.

    Article  Google Scholar 

  • Zhang, W., Gai, J., Zhang, Z., Tang, L., Liao, Q., & Ding, Y. (2019). Double-DQN based path smoothing and tracking control method for robotic vehicle navigation. Computers and Electronics in Agriculture, 166, 104985.

    Article  Google Scholar 

  • Zhao, Z., Zhou, L., & Zhu, Q. (2018). Preview distance adaptive optimization for the path tracking control of unmanned vehicle. Journal of Mechanical Engineering, 54(24), 166–173.

    Article  Google Scholar 

Download references

Acknowledgements

This research work was supported by the National Nature Science Foundation of China under Grant No. 62163014.

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Authors and Affiliations

  1. School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, 330013, China

    Yong Guan

  2. School of Intelligent Manufacturing, Taizhou University, Taizhou, 318000, China

    Ning Li, Pengzhan Chen & Yongchao Zhang

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  1. Yong Guan
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  2. Ning Li
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  3. Pengzhan Chen
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  4. Yongchao Zhang
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Correspondence to Pengzhan Chen.

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Guan, Y., Li, N., Chen, P. et al. Research on Path Tracking Control Based on Optimal Look-Ahead Points. Int.J Automot. Technol. (2024). https://doi.org/10.1007/s12239-024-00117-4

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  • DOI: https://doi.org/10.1007/s12239-024-00117-4

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