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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
27.08.2024
DOI
https://doi.org/10.7307/ptt.v36i4.528
ISSUE
2024 (Vol 36), Issue 4
LICENSE
Copyright (c) 2024 Xiaofeng WENG, Fei LIU, Sheng ZHOU, Jiacheng MAI, Shaoxiang FENG
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Obstacle Avoidance Path Planning Strategy for Autonomous Vehicles Based on Genetic Algorithm

Authors:Xiaofeng WENG, Fei LIU, Sheng ZHOU, Jiacheng MAI, Shaoxiang FENG

Abstract

In order to enhance the driving ability of autonomous vehicles on structured roads and enable them to plan safe and comfortable paths, we propose an obstacle avoidance path strategy for autonomous vehicles based on genetic algorithm. The use of Frenet-Serret enhances the adaptability of the algorithm in complex environments. In order to improve the generation and optimisation of obstacle avoidance trajectory, we establish an anti-collision model. When the vehicle faces a potential collision with an obstacle, the genetic algorithm quickly iterates and selects the first nine genes to generate the rough solution and convex space of the path. Combined with convex space, the quadratic programming method will numerically optimise the generated rough solution to generate an accurate path that satisfies the constraints. In addition, in order to ensure the safety and comfort in the process of obstacle avoidance, based on the dynamic constraints of the vehicle, the speed planning is used to determine the speed curve. We simulate in various scenarios involving moving obstacles. The real-time simulation based on the HIL platform proves that the proposed path planning strategy is effective in various driving scenarios.

Keywords:autonomous vehicle, genetic algorithm, anti-collision model, path planning, sequential quadratic programming ( SQP )

References

  1. [1] Paden B, et al. A survey of motion planning and control techniques for self-driving urban vehicles. IEEE Transactions on Intelligent Vehicles. 2016;1(1):33-55. DOI: 10.1109/TIV.2016.2578706.
  2. [2] Vagale A, et al. Path planning and collision avoidance for autonomous surface vehicles I: A review. Journal of Marine Science and Technology. 2021;26(4):1292-1306. DOI: 10.1007/s00773-020-00787-6.
  3. [3] Jeon G. A literature review of the path planning problem for unmanned aerial vehicle. Journal of the Military Operations Research Society of Korea. 2021;37(2):115-128.
  4. [4] Chen ZZ, Xiao JZ, Wang GF. An effective path planning of intelligent mobile robot using improved genetic algorithm. Wireless Communications & Mobile Computing. 2022. DOI: 10.1155/2022/9590367.
  5. [5] Maw AA, et al. IADA*: Improved anytime path planning and replanning algorithm for autonomous vehicle. Journal of Intelligent and Robotic Systems. 2020;100(3-4):1391-1405. DOI: 10.1177/09544070221088364.
  6. [6] Yao TT, et al. Review of path planning for autonomous underwater vehicles. The 2019 International Conference On Robotics, Intelligent Control And Artificial Intelligence. 2019;482-487. DOI: 10.1145/3366194.3366280.
  7. [7] Qin H, et al. Review of autonomous path planning algorithms for mobile robots. Drones. 2023;7(3). DOI: 10.3390/drones7030211.
  8. [8] Lee SH, Chung CC. Autonomous-driving vehicle control with composite velocity profile planning. IEEE Transactions on Control Systems Technology. 2021;29(5):2079-2091. DOI: 10.1109/TCST.2020.3029713.
  9. [9] Wang M, et al. Velocity planning method base on fuzzy neural network for autonomous vehicle. IEEE Access. 2021;9:19111-19126. DOI: 10.1109/ACCESS.2021.3054124.
  10. [10] Wang M, et al. Improved genetic algorithms based on data-driven operators for path planning of unmanned surface vehicle. International Journal of Robotics & Automation. 2019;34(6):713-722. DOI: 10.2316/J.2019.206-0315.
  11. [11] Maw AA, et al. IADA*: Improved anytime path planning and replanning algorithm for autonomous vehicle. Journal of Intelligent and Robotic Systems. 2020;100(3-4):1391-1405. DOI: 10.1177/09544070221088364.
  12. [12] Gugan G, Haque A. Path planning for autonomous drones: Challenges and future directions. DRONES. 2023;7(3):169. DOI: 10.3390/drones7030169.
  13. [13] Sun LY, et al. APF-bug-based intelligent path planning for autonomous vehicle with high precision in complex environment. International Journal of Robotics & Automation. 2023;38(4):277-283. DOI: 10.2316/J.2023.206-0741.
  14. [14] Zhang YJ, et al. Optimal vehicle path planning using quadratic optimization for Baidu Apollo open platform. 2020 IEEE Intelligent Vehicles Symposium (IV). 2021;18(5). DOI: 10.1177/17298814211042730.
  15. [15] Wu HX, et al. Research on vehicle obstacle avoidance path planning based on APF-PSO. Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering. 2022;237(6):1391-1405. DOI: 10.1177/09544070221088364.
  16. [16] Ji J, et al. Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints. IEEE Transactions on Vehicular Technology. 2017;66(2):952-964. DOI: 10.1109/TVT.2016.2555853.
  17. [17] Duan YJ, et al. Active obstacle avoidance method of autonomous vehicle based on improved artificial potential field. International Journal of Advanced Robotic Systems. 2022;19(4). DOI: 10.1177/17298806221115984.
  18. [18] Zheng L, et al. Bezier curve-based trajectory planning for autonomous vehicles with collision avoidance. IET Intelligent Transport Systems. 2020;14(13):1882-1893. DOI: 10.1049/iet-its.2020.0355.
  19. [19] Yang WL, et al. Improved artificial potential field and dynamic window method for amphibious robot fish path planning. Applied Sciences-Basel. 2021;11(5). DOI: 10.3390/app11052114.
  20. [20] Borges Lopes R, et al. A simple and effective evolutionary algorithm for the capacitated location–routing problem. Computers & Operations Research. 70(2016):155–162.
  21. [21] Liu J, Lu Cao, Sun H. Improved genetic algorithm for fast path planning of USV. MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications. 2015; 9815:981529. DOI: 10.1117/12.2210736.
  22. [22] Chen ZZ, Xiao JZ, Wang GF. An effective path planning of intelligent mobile robot using improved genetic algorithm, Wireless Communications and Mobile Computing, 2022;8. DOI: 10.1155/2022/9590367.
  23. [23] Hou WB, et al. Enhanced ant colony algorithm with communication mechanism for mobile robot path planning. Robotics and Autonomous Systems, 2022;148:103949. DOI: 10.1016/j.robot.2021.103949.
  24. [24] Liu JY, Wei XX, Huang HJ. An improved grey wolf optimization algorithm and its application in path planning. IEEE Access, 2021;9:121944 –121956. DOI: 10.1109/ACCESS.2021.3108973.
  25. [25] Kumar S, Sikander A. Optimum mobile robot path planning using improved artificial bee colony algorithm and evolutionary programming. Arabian Journal for Science and Engineering, 2022;47(3):3519-3539. DOI: 10.1007/s13369-021-06326-8.
  26. [26] Xu Xing, et al. Global path planning for mobile robots based on improved genetic algorithm. Computer Integrated Manufacturing System, 2022;28(6):1659-1672.
  27. [27] Orozco-Rosas U, et al. Mobile robot path planning using membrane evolutionary artificial potential field. Applied Soft Computing. 2019;77:235-251. DOI: 10.1016/j.asoc.2019.01.036.
  28. [28] Zhu WW, et al. Local motion planning and tracking on autonomous driving vehicles. IEEE Chinese Automation Congress (CAC 2020). 2020;7679-7684. DOI: 10.1109/CAC51589.2020.9327148.
  29. [29] Su SC, et al. Collaborative motion planning based on the improved ant colony algorithm for multiple autonomous vehicles. IEEE Transactions on Intelligent Transportation Systems. 2023. DOI: 10.1109/TITS.2023.3250756.
  30. [30] Shang EK, et al. An improved A-Star based path planning algorithm for autonomous land vehicles. International Journal of Advanced Robotic Systems. 2020;17(5). DOI: 10.1177/1729881420962263.
  31. [31] Zhang J, et al. Autonomous land vehicle path planning algorithm based on improved heuristic function of A-Star. International Journal of Advanced Robotic Systems. 2021;18(5). DOI: 10.1177/17298814211042730.
  32. [32] Zhu S, Aksun-Guvenc B. Trajectory planning of autonomous vehicles based on parameterized control optimization in dynamic on-road environments. Journal of Intelligent & Robotic Systems. 100(2020):1055-1067. DOI: 10.1007/s10846-020-01215-y.
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