变构型陆空平台动态起降轨迹规划研究

魏巍; 范康迪; 王若琳; 赵默雷; 赵之然; 王剑峰

doi:10.15918/j.tbit1001-0645.2021.325

变构型陆空平台动态起降轨迹规划研究

doi: 10.15918/j.tbit1001-0645.2021.325

魏巍^{1, 2,},
范康迪^{1, 2},
王若琳¹,
赵默雷¹,
赵之然²,
王剑峰³

1.
北京理工大学车辆传动国家重点实验室, 北京　100081
2.
北京理工大学重庆创新中心, 重庆　401120
3.
北京千方创新科技有限公司, 北京　102299

基金项目: 国家重点研发计划(2020YFC1512500)

详细信息

作者简介:
魏巍（1978—），男，博士，教授，E-mail：weiweibit@bit.edu.cn

中图分类号: TP242
计量
- 文章访问数: 109
- HTML全文浏览量: 66
- PDF下载量: 0
- 被引次数: 0
出版历程
- 收稿日期: 2021-11-30
- 录用日期: 2022-03-07
- 刊出日期: 2022-08-17

Trajectory Planning of Dynamic Take-off and Landing of Deformable Aerial-Ground Platform

1.
National Key Laboratory of Vehicular Transmission, Beijing Institute of Technology, Beijing 100081, China
2.
Chongqing Innovation Center, Beijing Institute of Technology, Chongqing 401120, China
3.
Beijing Qianfang Innovation Technology Co., Ltd., Beijing 102299, China

摘要

摘要: 陆空平台具有多域机动能力，通过陆空模式的转换能够适应各种复杂环境，但陆空模式转换多为静止起飞或悬停下降，这种静态起降方式不利于陆空平台机动性的充分发挥. 针对一种动力机构可偏转的变构型陆空两栖平台，基于牛顿-欧拉方程建立陆空平台的飞行动力学模型，规划偏转角的时间序列以获得动态动力学约束，确定相对时间最优目标函数；基于5次多项式拟合二维平面轨迹，根据PID控制方法设计轨迹跟踪控制器，并进行轨迹规划和控制仿真. 结果表明，动态切换时间相比静态切换时间缩短了23.02%，动态切换规划轨迹平滑，高度方向无超调，控制器能较好地跟踪目标飞行轨迹.
- 陆空平台 /
- 变构型 /
- 动态切换 /
- 轨迹规划 /
- 轨迹跟踪控制
Abstract: Aerial-ground platform has multi-domain maneuverability and can adapt to various complex environments through the conversion of land and air mode, but the land and air mode conversion is mostly static take-off or hovering descent, which is not conducive to the full display of the maneuverability of the aerial-ground platform. Aiming at a deformable aerial-ground amphibious platform with deflectable power mechanism, the flight dynamics model of the aerial-ground platform was established based on the Newton-Euler equation, the time sequence of the deflection angle was planned to obtain dynamic constraints, and the relative time optimal objective function was determined. The fifth-order polynomial was used to fit the two-dimensional trajectory, and the trajectory tracking controller was designed according to the PID control method, and the trajectory planning and control simulation were carried out. The results show that the dynamic switching time is shortened by 23.02% compared with the static switching time, the dynamic switching planning trajectory is smooth, there is no overshoot in the altitude direction, and the controller can better track the target flight trajectory.
- aerial-ground platforms /
- deformable /
- dynamic switching /
- trajectory planning /
- trajectory tracking control

HTML全文

图 1 变构型陆空平台示意图

Figure 1. Schematic of deformable aerial-ground platform

下载: 全尺寸图片幻灯片

图 2 坐标系

Figure 2. Coordinate system

下载: 全尺寸图片幻灯片

图 3 随偏转角变化的转动惯量曲线

Figure 3. Moment of inertia curve varying with deflection angle

下载: 全尺寸图片幻灯片

图 4 陆空动态切换与静态切换示意图

Figure 4. Schematic diagram of dynamic and static switching between land and air

下载: 全尺寸图片幻灯片

图 5 轨迹规划流程图

Figure 5. Trajectory planning flowchart

下载: 全尺寸图片幻灯片

图 6 J随x、h变化的关系图

Figure 6. The relationship diagram of J with x and h

下载: 全尺寸图片幻灯片

图 7 飞行路径

Figure 7. Flight path

下载: 全尺寸图片幻灯片

图 8 动态切换时z向加速度曲线

Figure 8. z-direction acceleration curve during dynamic switching

下载: 全尺寸图片幻灯片

图 9 轨迹跟踪控制框图

Figure 9. Trajectory tracking control block diagram

下载: 全尺寸图片幻灯片

图 10 PID轨迹跟踪控制

Figure 10. PID trajectory tracking control

下载: 全尺寸图片幻灯片

表 1 关键参数

Table 1. Key parameters

参数	数值
$ m $/kg	1.4
$ g $/(m·s⁻²)	9.8
${c_{\rm{T}}}$	1.105×10⁻⁵
${c_{\rm{M}} }$	3.558×10⁻⁷
$ {x_{\max }} $/m	50
$ {v_x} $/(m·s⁻¹)	1
$ \alpha (t = 0) $/(°)	30
$ p $	3
${a_{\textit{z}\max } }$/(m·s⁻²)	0.2g
${v_{\textit{z}\max } }$/(m·s⁻¹)	2
$a_{x\min }'$/(m·s⁻²)	−5
${t_{{\rm{j}}2} }$/s	0.1

下载: 导出CSV

表 2 仿真结果

Table 2. Simulation results

参数	数值
静态切换时间/s	0.93
动态切换时间/s	0.6
目标函数$ J $/%	35.73
$ x $/m	0.6
$ h $/m	0.1

下载: 导出CSV

表 3 h=1.5 m时的仿真结果

Table 3. Simulation results when h=1.5 m

切换方式	静态切换	动态切换
时间/s	3.12	2.4

下载: 导出CSV

表 4 控制器参数

Table 4. Controller parameters

姿态控制器参数	数值	位置控制器参数	数值
${k_{\rm{P} } }\_Pitch\_Angle$	6.5	${k_{\rm{P} } }\_x$	1.2
${k_{\rm{P} } }\_Pitch\_AngleRate$	0.1	${k_{\rm{P} } }\_{v_x}$	0.2
${k_{\rm{I} } }\_Pitch\_AngleRate$	0.02	${k_{\rm{I} } }\_{v_x}$	0.01
${k_{\rm{D} } }\_Pitch\_AngleRate$	0.001	${k_{\rm{D} } }\_{v_x}$	0.01
${k_{\rm{P} } }\_Roll\_Angle$	6.5	${k_{\rm{P} } }\_y$	1.2
${k_{\rm{P} } }\_Roll\_AngleRate$	0.1	${k_{\rm{P} } }\_{v_y}$	1.5
${k_{\rm{I} } }\_Roll\_AngleRate$	0.02	${k_{\rm{I} } }\_{v_y}$	0.4
${k_{\rm{D} } }\_Roll\_AngleRate$	0.001	${k_{\rm{D} } }\_{v_y}$	0.01
${k_{\rm{P} } }\_Yaw\_Angle$	4	${k_{\rm{P} } }\_ {\textit{z} }$	4
${k_{\rm{P} } }\_Yaw\_AngleRate$	0.3	${k_{\rm{P} } }\_{v_ {\textit{z} } }$	12
${k_{\rm{I} } }\_Yaw\_AngleRatetete$	0.01	${k_{\rm{I} } }\_{v_{\textit{z} } }$	4
${k_{\rm{D} } }\_Yaw\_AngleRate$	0	${k_{\rm{D} } }\_{v_{\textit{z} } }$	2.5

下载: 导出CSV

参考文献(11)

[1]	OZAWA R, CHAUMETTE F. Dynamic visual servoing with image moments for a quadrotor using a virtual spring approach[C]//Proceedings of 2011 IEEE International Conference on Robotics and Automation. [S. l.]: IEEE, 2011: 5670 − 5676.
[2]	清华大学. 清华大学发明无人驾驶陆空两栖飞车[R]. 机器人技术与应用, 2020(4): 9. Tsinghua University. Tsinghua University invented driverless land-air amphibious flying car[R]. Robot Technique and Application, 2020(4): 9. (in Chinese)
[3]	KALANTARI A, SPENKO M. Design and experimental validation of hytaq, a hybrid terrestrial and aerial quadrotor[C]//Proceedings of 2013 IEEE International Conference on Robotics and Automation. [S. l.]: IEEE, 2013: 4445 − 4450.
[4]	罗庆生, 陈胤霏, 刘星栋, 等. 一种小型陆空两栖机器人的选型分析与结构设计[J]. 计算机测量与控制, 2019, 27(4):213 − 217. LUO Qingsheng, CHEN Yinfei, LIU Xingdong, et al. Selection analysis and structure design of a small amphibian robot[J]. Computer Measurement & Control, 2019, 27(4):213 − 217. (in Chinese)
[5]	冯宜明, 王建中, 施家栋. 基于自适应的变形式陆空机器人转域过程飞行控制[J]. 航空学报, 2019, 40(6):235 − 245. FENG Yiming, WANG Jianzhong, SHI Jiadong. Area transfer flight control of metamorphic air-land amphibious vehicles based on adaptive control[J]. Acta Aeronautica et Astronautica Sinica, 2019, 40(6):235 − 245. (in Chinese)
[6]	张佳林, 熊大顺, 毛子夏, 等. 新型陆空两栖平台技术发展与趋势[J]. 汽车工程学报, 2019, 9(5):332 − 341. ZHANG Jialin, XIONG Dashun, MAO Zixia, et al. Technology development and trends of state-of-the-art air-ground amphibious platforms[J]. Chinese Journal of Automotive Engineering, 2019, 9(5):332 − 341. (in Chinese)
[7]	牛国臣, 李文帅, 魏洪旭. 基于双五次多项式的智能汽车换道轨迹规划[J]. 汽车工程, 2021, 43(7):978 − 986+1004. NIU Guochen, LI Wenshuai, WEI Hongxu. Intelligent vehicle lane changing trajectory planning based on double quintic polynomials[J]. Automotive Engineering, 2021, 43(7):978 − 986+1004. (in Chinese)
[8]	王莹, 卫翀, 马路. 基于二次规划的智能车辆动态换道轨迹规划研究[J]. 中国公路学报, 2021, 34(7):79 − 94. doi: 10.3969/j.issn.1001-7372.2021.07.007 WANG Ying, WEI Chong, MA Lu. Dynamic lane-changing trajectory planning model for intelligent vehicle based on quadratic programming[J]. China Journal of Highway and Transport, 2021, 34(7):79 − 94. (in Chinese) doi: 10.3969/j.issn.1001-7372.2021.07.007
[9]	唐刚, 侯志鹏, 胡雄. 基于最小化snap方法的无人机多项式轨迹优化[J]. 计算机应用研究, 2021, 38(5):1455 − 1458. TANG Gang, HOU Zhipeng, HU Xiong. Polynomial trajectory optimization of UAV based on minimum-snap method[J]. Application Research of Computers, 2021, 38(5):1455 − 1458. (in Chinese)
[10]	张洲宇, 曹云峰, 范彦铭. 基于局部方位信息的无人机避障轨迹规划[J]. 中国科学:技术科学, 2021, 51(9):1075 − 1087. doi: 10.1360/SST-2020-0225 ZHANG Zhouyu, CAO Yunfeng, FAN Yanming. Local-bearing-information-based unmanned aerial vehicle collision avoidance trajectory planning[J]. Scientia Sinica: Technologica, 2021, 51(9):1075 − 1087. (in Chinese) doi: 10.1360/SST-2020-0225
[11]	陈万明. 四涵道无人陆空车辆前飞姿态控制研究[D]. 北京: 北京理工大学, 2016. CHEN Wanming. Research on forward flight attitude control of four ducted fan vehicle[D]. Beijing: Beijing Institute of Technology, 2016. (in Chinese)