无人驾驶汽车局部路径规划算法研究*

doi:10.19562/j.chinasae.qcgc.2020.01.001

摘要/Abstract

摘要： 无人驾驶汽车局部路径规划算法应确保避障的安全性、实时性和路径的平滑性等。本文提出了一种基于离散优化的局部路径规划算法,即采用代价函数分别评估离散生成的候选路径的安全性、平滑性等,再根据各代价函数加权计算获得局部最佳路径。针对障碍物移动随机性,设计了一种基于运动估计结合高斯卷积的移动障碍安全性代价函数;考虑候选路径曲率的变化及其连续性,设计了路径平滑性代价函数。使用了一种新的坐标转换计算方法将路径从s-ρ坐标系转换到大地笛卡尔坐标系,提高了实时性。最后,利用PreScan和Matlab软件进行联合仿真,并在“远飞”无人车实验平台上进行了真实道路场景的实车实验。实验结果表明:提出的路径规划算法不仅能使无人车安全、合理地规避静止和移动障碍,且完全满足局部路径规划算法对实时性的要求。

关键词: 无人驾驶汽车, 避障, 路径规划, 代价函数, 实车实验

Abstract: The local path planning algorithm of unmanned vehicle has certain requirements for the safety and real-time performance of obstacle avoidance, and the smoothness of obstacle avoidance path. In this paper, a local path planning algorithm based on discrete optimization is proposed, which uses cost function to evaluate the safety and smoothness of discretely generated candidate paths, and then obtains the local optimal path through the weighted calculation of each cost function. Aiming at the randomness of obstacles movement, a moving obstacles safety cost function is designed based on motion estimation combined with Gaussian convolution. Considering the curvature and its continuity of path, a path smoothness cost function is designed. A new coordinate transformation calculation method is adopted to convert the path from the s-ρ coordinate system to the earth Cartesian coordinate system, enhancing real-time performance. Finally, a PreScan / Matlab co-simulation and a real vehicle experiment on “Yuan Fei” unmanned vehicle experimental platform are both carried out. The results show that the path planning algorithm proposed not only enables the unmanned vehicle to safely and reasonably avoid the static and moving obstacles, but also fully meets the real-time requirements of local path planning algorithm

Key words: unmanned vehicle, obstacle avoidance, path planning, cost function, real vehicle experiment

彭晓燕, 谢浩, 黄晶. 无人驾驶汽车局部路径规划算法研究^*[J]. 汽车工程, 2020, 42(1): 1-10.

Peng Xiaoyan, Xie Hao, Huang Jing. Research on Local Path Planning Algorithm for Unmanned Vehicles[J]. Automotive Engineering, 2020, 42(1): 1-10.

参考文献

[1] 胡林,易平,黄晶,等.基于真实事故案例的自动紧急制动系统两轮车测试场景研究[J].汽车工程,2018,40(12):1435-1446.
[2] GUO H, SHEN C, ZHANG H, et al. Simultaneous trajectory planning and tracking using an MPC method for cyber-physical systems: a case study of obstacle avoidance for an intelligent vehicle[J]. IEEE Transactions on Industrial Informatics,2018,14(9):4273-4283.
[3] LEE J, NAM Y Y, HONG S J. Random force based algorithm for local minima escape of potential filed method[C].11^th International Conference on Control Automation Robotics & Vision. Singapore: Nanyang Technological University,2010:827-832.
[4] 安林芳,陈涛,成艾国,等.基于人工势场算法的智能车辆路径规划仿真[J].汽车工程,2017,39(12):1451-1456.
[5] 修彩靖,陈慧.基于改进人工势场法的无人驾驶车辆局部路径规划的研究[J].汽车工程,2013,35(9):808-811.
[6] 唐志荣,冀杰,吴明阳,等.基于改进人工势场法的车辆路径规划与跟踪[J].西南大学学报(自然科学版),2018,40(6):174-182.
[7] 辛煜,梁华为,杜名博,等.一种可搜索无限个邻域的改进A^*算法[J].机器人,2014,36(5):627-633.
[8] 齐尧,徐友春,李华,等.一种基于改进混合A^*的智能车路径规划算法[J].军事交通学院学报,2018,20(8):85-90.
[9] BOROUJENI Z, GOEHRING D, ULBRICH F, et al. Flexible unit A-star trajectory planning for autonomous vehicles on structured road maps[C]. IEEE International Conference on Vehicular Electronics & Safety, IEEE,2017.
[10] HU L, YANG J, HUANG J. The real-time shortest path algorithm with a consideration of traffic-light[J]. Journal of Intelligent & Fuzzy Systems,2016,31(4):2403-2410.
[11] 胡林,钟远兴,黄晶,等.考虑信号交叉口延时的最优车辆路径规划算法[J].汽车工程,40(10):1223-1229.
[12] HU L, ZHONG Y, HAO W, et al. Optimal route algorithm considering traffic light and energy consumption[J]. IEEE ACCESS,2018,6:59695-59704.
[13] MAJUMDER S, PRASAD M S. Three dimensional D^* algorithm for incremental path planning in uncooperative environment[C]. 2016 3rd International Conference on Signal Processing & Integrated Networks, IEEE,2016.
[14] AKBARIPOUR H, MASEHIAN E. Semi-lazy probabilistic roadmap: a parameter-tuned, resilient and robust path planning method for manipulator robots[J]. International Journal of Advanced Manufacturing Technology,2016,89(5-8):1-30.
[15] TAHERI E, FERDOWSI M H, DANESH M. Fuzzy greedy RRT path planning algorithm in a complex configuration space[J]. International Journal of Control, Automation and Systems,2018,16(6):3026-3035.
[16] 宋晓琳,周南,黄正瑜,等.改进RRT在汽车避障局部路径规划中的应用[J].湖南大学学报(自然科学版),2017,44(4):30-37.
[17] 杜明博,梅涛,陈佳佳,等.复杂环境下基于RRT的智能车辆运动规划算法[J].机器人,2015,37(4):443-450.
[18] CHU K, LEE M, SUNWOO M. Local path planning for off-road autonomous driving with avoidance of static obstacles[J]. IEEE Transactions on Intelligent Transportation Systems,2012,13(4):1599-1616.
[19] WERLING M, ZIEGLER J, KAMMEL S, et al. Optimal trajectory generation for dynamic street scenarios in a Frenét frame[C]. IEEE International Conference on Robotics and Automation, IEEE,2010:987-993.
[20] HU X, CHEN L, TANG B, et al. Dynamic path planning for autonomous driving on various roads with avoidance of static and moving obstacles[J]. Mechanical Systems and Signal Processing,2018,100:482-500.
[21] 周慧子,胡学敏,陈龙,等.面向自动驾驶的动态路径规划避障算法[J].计算机应用,2017,37(3):883-888.
[22] 陈成,何玉庆,卜春光,等.基于四阶贝塞尔曲线的无人车可行轨迹规划[J].自动化学报,2015,41(3):486-496.
[23] 张琳,章新杰,郭孔辉,等.未知环境下智能汽车轨迹规划滚动窗口优化[J].吉林大学学报(工学版),2018,48(3):652-660.
[24] LI X, SUN Z, CAO D, et al. Real-time trajectory planning for autonomous urban driving: framework, algorithms, and verifications[J]. IEEE/ASME Transactions on Mechatronics,2016,21(2):740-753.
[25] LIM W, LEE S, SUNWOO M, et al. Hierarchical trajectory planning of an autonomous car based on the integration of a sampling and an optimization method[J]. IEEE Transactions on Intelligent Transportation Systems,2018,19(2):613-626.
[26] ZHANG Y, CHEN H, WASLANDER S L, et al. Hybrid trajectory planning for autonomous driving in highly constrained environments[J]. IEEE Access,2018,6:32800-32819.
[27] WANG H, KEARNEY J, ATKINSON K. Arc-length parameterized spline curves for real-time simulation[C]. Proc. 5th International Conference on Curves and Surfaces,2002:387-396.
[28] 黄晶,李勇,胡林.基于数据关联和改进统计模型的激光雷达目标跟踪研究[J].汽车工程,2018,40(3):356-362.
[29] 谢德胜,徐友春,王任栋,等.基于三维激光雷达的无人车障碍物检测与跟踪[J].汽车工程,2018,40(8):952-959.
[30] 段建民,杨晨,石慧.基于Pure Pursuit算法的智能车路径跟踪[J]. 北京工业大学学报, 2016, 42(9):1301-1306.

无人驾驶汽车局部路径规划算法研究^*

Research on Local Path Planning Algorithm for Unmanned Vehicles

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