面向分体式飞行汽车自主对接的自动驾驶底盘运动规划方法研究

doi:10.3901/JME.2024.10.235

机械工程学报 ›› 2024, Vol. 60 ›› Issue (10): 235-244.doi: 10.3901/JME.2024.10.235

扫码分享

面向分体式飞行汽车自主对接的自动驾驶底盘运动规划方法研究

郄天琪^1,2, 王伟达^1,2, 杨超^1,2, 李颖^1,2, 项昌乐^1,2

1. 北京理工大学机械与车辆学院北京 100081;
2. 北京理工大学重庆创新中心重庆 401122

收稿日期:2023-12-06 修回日期:2024-04-12 出版日期:2024-05-20 发布日期:2024-07-24
作者简介:郄天琪,女,1997年出生,博士研究生。主要研究方向为车辆轨迹预测与智能车辆决策规划方法。
E-mail:tianqi_qie@bit.edu.cn
王伟达(通信作者),男,1980年出生,博士,教授,博士研究生导师。主要研究方向为机电复合传动、飞行汽车总体技术、分布式电驱动技术、车辆动力学与控制、无人车感知决策与控制。
E-mail:wangwd0430@bit.edu.cn
基金资助:
国家自然科学基金(52275047，52102449，52394262)和重庆市自然科学基金(cstc2021jcyj-msxmX0879，CSTB2022NSCQ-MSX1056)资助项目。

Motion Planning Method of Autonomous Driving Chassis for Autonomous Docking of the Split-type Flying Vehicle

QIE Tianqi^1,2, WANG Weida^1,2, YANG Chao^1,2, LI Ying^1,2, XIANG Changle^1,2

1. School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081;
2. Chongqing Innovation Center, Beijing Institute of Technology, Chongqing 401122

Received:2023-12-06 Revised:2024-04-12 Online:2024-05-20 Published:2024-07-24

摘要/Abstract

摘要： 飞行汽车是引领汽车行业未来技术发展的战略新兴方向，分体式飞行汽车作为飞行汽车的主流构型之一，由自动驾驶底盘、智能操控座舱和垂直起降飞行器三部分组成，为完成各部分的自主对接和组合，自动驾驶底盘需要沿规划的路径精确行驶至起飞行器正下方。当前运动规划方法缺乏对来自传感器、控制器和执行器等诸多不确定因素的考虑，导致路径跟踪过程中底盘行驶轨迹偏离规划轨迹，难以精确行驶至预定位置，完成对接。为解决这一问题，提出一种基于长短期记忆网络(Long short-term memory, LSTM)车辆模型的自动驾驶底盘轨迹规划方法。采用LSTM网络表征自动驾驶底盘的运动学特性，在此基础上建立车辆运动学模型。基于该模型，构建模型预测控制架构下的滚动时域优化问题。进而采用基于向量加权平均值的优化方法求解该非线性优化问题，得到符合底盘运动学特性的行驶轨迹。基于团队研制的分体式飞行汽车样机，将提出的规划方法进行试验验证。在转弯场景中，采用提出的方法的底盘平均纵向位置偏差、最大纵向位置偏差与传统模型预测控制(Model predictive control, MPC)方法相比分别下降了78.89%、79.64%和86.67%。

关键词: 分体式飞行汽车, 自主对接, 运动规划, 模型预测控制

Abstract: Flying vehicles are a strategic emerging direction leading the future technological development of the automotive field. As one of the mainstream configurations of flying vehicles, split-type flying vehicles are composed of three parts: an autonomous driving chassis, an intelligent cockpit, and a vertical takeoff and landing aircraft. To complete the autonomous docking of the two parts, the autonomous driving chassis needs to track accurately along the planned path to the right below the aircraft. The current motion planning methods lack consideration for many uncertain factors such as sensors, controllers, and actuators, resulting in the chassis trajectory deviating from the planned trajectory during path tracking, making it difficult to accurately travel to the predetermined position and complete docking. To address this issue, an autonomous driving chassis trajectory planning method based on long short-term memory(LSTM) vehicle models is proposed. Using LSTM network to characterize the kinematic characteristics of the autonomous driving chassis of a split-type flying vehicle, a vehicle kinematic model is established based on this. Based on this model, a rolling horizon optimization problem under the model predictive control architecture is constructed. Furthermore, an optimization method based on weighted mean of vectors is used to solve the nonlinear optimization problem and obtain a driving trajectory that conforms to the kinematic characteristics of the chassis. Based on the split-type flying vehicle developed by the team, the proposed planning method is experimentally validated. In the turning scenario, the average longitudinal position deviation, maximum longitudinal position deviation, and longitudinal position docking deviation of the chassis using the proposed method are reduced by 78.89%, 79.64%, and 86.67% compared to traditional MPC methods, respectively.

Key words: split-type flying vehicle, autonomous docking, motion planning, model predictive control

中图分类号:

U469

郄天琪, 王伟达, 杨超, 李颖, 项昌乐. 面向分体式飞行汽车自主对接的自动驾驶底盘运动规划方法研究[J]. 机械工程学报, 2024, 60(10): 235-244.

QIE Tianqi, WANG Weida, YANG Chao, LI Ying, XIANG Changle. Motion Planning Method of Autonomous Driving Chassis for Autonomous Docking of the Split-type Flying Vehicle[J]. Journal of Mechanical Engineering, 2024, 60(10): 235-244.

参考文献

[1] 中共中央国务院印发《交通强国建设纲要》[EB/OL]. [2019-09-19]. https://www.gov.cn/zhengce/2019-09/19/content_5431432.htm. The Central Committee of the Communist Party of China and the State Council have issued the “Outline for building a strong transportation country”[EB/OL]. [2019-09-19]. https://www.gov.cn/zhengce/2019-09/19/content_5431432.htm.
[2] 俞玉树，王凯迪，杜健睿，等. 多飞行器集联平台的设计与轨迹线性化几何控制[J]. 机械工程学报，2022，58(21):16-26. YU Yushu，WANG Kaidi，DU Jianrui, et al. Design and trajectory linearization geometric control of multiple aerial vehicles assembly[J]. Journal of Mechanical Engineering，2022，58(21):16-26.
[3] 张扬军，钱煜平，诸葛伟林，等. 飞行汽车的研究发展与关键技术[J]. 汽车安全与节能学报，2020，11(1):1-16. ZHANG Yangjun，QIAN Yuping，ZHUGE Weilin，et al. Progress and key technologies of flying cars[J]. Journal of Automotive Safety and Energy，2020，11(1):1-16.
[4] SWAMINATHAN N，REDDY S，RAJASHEKARA K，et al. Flying cars and eVTOLs-technology advancements，powertrain architectures，and design[J]. IEEE Transactions on Transportation Electrification，2022，8(4):4105-4117.
[5] YANG Chao，LU Zhexi，WANG Weida，et al. An efficient intelligent energy management strategy based on deep reinforcement learning for hybrid electric flying car[J]. Energy，2023，280:128118.
[6] RAJASHEKARA K，WANG Q，MATSUSE K. Flying cars:Challenges and propulsion strategies[J]. IEEE Electrification Magazine，2016，4(1):46-57.
[7] 奥迪飞行汽车“Pop.Up Next”试飞成功[EB/OL]. [2018-12-07]. http://www.autor.com.cn/index/technology/smartcar/8857.html. Audi flying car “Pop. Up Next” successfully tested [EB/OL]. [2018-12-07]. http://www.autor.com.cn/index/technology/smartcar/8857.html.
[8] 飞行汽车来了!全球首款载人级两座智能分体式飞行汽车工程样车研制成功[EB/OL]. [2022-11-19]. http://www.news.cn/politics/2022-11/19/c_1129141690.htm. The flying car is here! The world's first manned two seat intelligent split type flying vehicle engineering prototype has been successfully developed[EB/OL]. [2022-11-19]. http://www.news.cn/politics/2022-11/19/c_1129141690.htm.
[9] 李荣粲，庄伟超，殷国栋，等. 自动驾驶赛车路径与车速协同规划方法[J]. 机械工程学报，2022，58(10):200-208. LI Rongcan，ZHUANG Weichao，YIN Guodong，et al. Simultaneous path and speed planning of driverless racing car[J]. Journal of Mechanical Engineering，2022，58(10):200-208.
[10] 关海杰，王博洋，龚建伟，等. 面向异构车辆的统一运动规划方法[J/OL]. 机械工程学报:1-10[2024-02-29]. http://kns.cnki.net/kcms/detail/11.2187.TH.20230608.0914.002.html. GUAN Haijie，WANG Boyang，GONG Jianwei, et al. Unified motion planning method for heterogeneous vehicles[J/OL]. Journal of Mechanical Engineering:1-10[2024-02-29]. http://kns.cnki.net/kcms/detail/11.2187.TH.20230608.0914.002.html.
[11] LI Xiaohui，SUN Zhenping，CAO Dongpu, 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.
[12] 张利鹏，苏泰，严勇. 基于采样区域优化的智能车辆轨迹规划方法[J]. 机械工程学报，2022，58(14):276-287. ZHANG Lipeng，SU Tai，YAN Yong. Trajectory planning method of intelligent vehicle based on sampling area optimization[J]. Journal of Mechanical Engineering，2022，58(14):276-287.
[13] LI Hongcai，LIU Wenjie，YANG Chao，et al. An optimization-based path planning approach for autonomous vehicles using the DynEFWA-artificial potential field[J]. IEEE Transactions on Intelligent Vehicle，2022，7(2):263-272.
[14] QIE Tianqi，WANG Weida，YANG Chao，et al. An improved model predictive control-based trajectory planning method for automated driving vehicles under uncertainty environments[J]. IEEE Transactions on Intelligent Transportation Systems，2023，21(4):3999-4015.
[15] LIU C，LEES，VARNHAGEN S，et al. Path planning for autonomous vehicles using model predictive control [C]//28th IEEE Intelligent Vehicles Symposium. Redondo Beach:IEEE，2017:174-179.
[16] DIXIT S，MONTANARO U，DIANATI M，et al. Trajectory planning for autonomous high-speed overtaking in structured environments using robust MPC[J]. IEEE Transactions on Intelligent Vehicle，2020，21(6):2310-2323.
[17] WANG Zhuping，LI Gangbin，JIANG Houjie，et al. Collision-free navigation of autonomous vehicles using convex quadratic programming-based model predictive control[J]. IEEE/ASME Transactions on Mechatronics，2018，23(3):1103-1113.
[18] WANG Weida，QIE Tianqi，YANG Chao，et al. An intelligent lane-changing behavior prediction and decision-making strategy for an autonomous vehicle[J]. IEEE Transactions on Industrial Electronics，2022，69(3):2927-2937.
[19] PASZKE A，GROSS S，MASSA F，et al. PyTorch:An imperative style，high-performance deep learning library [EB/OL]. [2019-12-03]. https://www.semanticscholar.org/reader/3c8a456509e6c0805354bd40a35e3f2dbf8069b1.
[20] KINGMA D，BA J. Adam:A method for stochastic optimization[EB/OL]. [2014-12-22]. https://arxiv.org/abs/1412.6980.
[21] AHMADIANFAR I，HEIDARI A，NOSHADIAN S，et al. INFO:An efficient optimization algorithm based on weighted mean of vectors[J]. Expert System with Applications，2022，195(1):116516.

面向分体式飞行汽车自主对接的自动驾驶底盘运动规划方法研究

Motion Planning Method of Autonomous Driving Chassis for Autonomous Docking of the Split-type Flying Vehicle

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	郭景华, 李文昌, 王班, 王靖瑶. 基于深度强化学习的网联混合动力汽车队列控制[J]. 机械工程学报, 2024, 60(2): 262-271.
[2]	杜国锋, 赵萌, 武建昫, 张东. 基于视觉的闭链多足机器人自主运动控制方法[J]. 机械工程学报, 2024, 60(19): 62-70.
[3]	姚建均, 张宜坤, 柯运, 钱琛, 王超. 基于模型预测控制的机器人姿态控制策略研究[J]. 机械工程学报, 2024, 60(19): 88-100.
[4]	关海杰, 王博洋, 龚建伟, 陈慧岩. 面向异构车辆的统一运动规划方法[J]. 机械工程学报, 2024, 60(18): 288-298.
[5]	王翔宇, 任帆, 刘冲, 许思昂, 王龙昕, 方勇纯, 于宁波, 韩建达. 面向NOTES手术的软镜操作机器人技术进展[J]. 机械工程学报, 2024, 60(17): 40-62.
[6]	李昊, 宾洋, 胡杰, 岳肖, 金庭安, 张凯. 无偏显式模型预测控制及其在质子交换膜燃料电池净输出功率跟踪中的应用[J]. 机械工程学报, 2024, 60(14): 282-297.
[7]	李颖, 王荣煊, 万成麟, 王伟达, 杨超, 徐彬. 分体式飞行汽车立体环境感知系统设计及试验研究[J]. 机械工程学报, 2024, 60(10): 102-111.
[8]	高镇海, 于桐, 孙天骏. 考虑社会性行为的自动驾驶运动规划研究综述[J]. 机械工程学报, 2024, 60(10): 112-128.
[9]	张寒, 刘开华, 王春燕, 赵万忠, 栾众楷. 四轮分布式线控转向系统稳定与协调控制研究[J]. 机械工程学报, 2024, 60(10): 425-438.
[10]	李兵兵, 庄伟超, 刘昊吉, 张昊, 殷国栋, 张建润. 基于自学习型MPC的网联电动汽车生态驾驶控制策略研究[J]. 机械工程学报, 2024, 60(10): 453-462.
[11]	王峰, 张健, 徐兴, 王春海, 阙红波, 高扬. 行星耦合PHEV模式切换过程全频段瞬态扭振特性分析与主动抑制[J]. 机械工程学报, 2023, 59(4): 173-189.
[12]	金贤球, 雷涛, 闵志豪, 宋丽娜, 张星雨, 张晓斌. 分布式电推进飞机能量优化动态管理技术研究^*[J]. 电气工程学报, 2023, 18(3): 315-331.
[13]	吴馥云, 程卫, 李高宁, 吕建国. 基于模型预测的光伏参与电网调频方法研究^*[J]. 电气工程学报, 2023, 18(3): 348-357.
[14]	赵锦涛, 李亮, 薛仲瑾, 张志煌. 基于混合A星的停车场内巡航分层运动规划方法[J]. 机械工程学报, 2023, 59(24): 290-298.
[15]	魏中宝, 钟浩, 何洪文. 基于多物理过程约束的锂离子电池优化充电方法[J]. 机械工程学报, 2023, 59(2): 223-232.