Autonomous Landing of UAVs based on Spatio-temporal Decomposition Planning

Yang XU; Chao WEI; Fuyong FENG; Leyun HU

doi:10.12382/bgxb.2024.0653

您当前的位置：

首页 >

文章列表页 >

Autonomous Landing of UAVs based on Spatio-temporal Decomposition Planning

更新时间：2025-09-05

- Autonomous Landing of UAVs based on Spatio-temporal Decomposition Planning
- Acta Armamentarii Vol. 46, Issue 7, Pages: 240653(2025)
- 作者机构：
  
  北京理工大学机械与车辆学院, 北京 100081
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2024.0653
  CLC：
- Received：31 July 2024，
  
  Published Online：12 August 2025，
  
  Published：31 July 2025
- 稿件说明：
移动端阅览
Yang XU, Chao WEI, Fuyong FENG, et al. Autonomous Landing of UAVs based on Spatio-temporal Decomposition Planning[J]. Acta Armamentarii, 2025, 46(7): 240653.
DOI：

Yang XU, Chao WEI, Fuyong FENG, et al. Autonomous Landing of UAVs based on Spatio-temporal Decomposition Planning[J]. Acta Armamentarii, 2025, 46(7): 240653. DOI： 10.12382/bgxb.2024.0653.

摘要

空地协同系统中的无人车-无人机协同降落对拓展异构智能体集群的任务场景具有重要意义。目前基于轨迹优化的自主降落方法将时间与空间耦合在一起

通过设计最优控制律进行联合航迹优化

但存在优化目标函数设计较为复杂

无法发挥致动器最佳效能的问题。针对传统轨迹优化方法中时空维度过度耦合的问题

提出一种基于时空解耦的轨迹优化(Spatio-Temporal Decomposition Planning

STDP)方法

通过在空间与时间维度分别对降落轨迹进行优化

使无人机在复杂场景下采取更为激进的飞行策略。同时在设计目标函数时

综合考虑无人机的降落耗时以及电机功耗模型

以最优时间、能耗为控制目标构建2阶锥规划问题以加速求解

确保求解质量与效率。仿真结果表明

相较于时空耦合的规划方法

STDP方法规划出了更逼近运动学约束边界的轨迹

使得任务耗时大大缩减

提高了任务效率

同时实际场景中的测试结果也证明了STDP方法在实际应用中的可靠性。

Abstract

In air-ground collaborative systems

the coordinated landing of unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) is of paramount importance for extending the task scenarios of heterogeneous intelligent agent clusters.Current trajectory optimization-based autonomous landing methods couple the temporal and spatial dimensions by designing the optimal control laws for joint trajectory optimization.However

the optimization objective function design is relatively complex

and it cannot fully utilize the actuator’s optimal performance.A novel spatio-temporal decomposition planning (STDP) method is proposed to address the excessive coupling of time and space in traditional trajectory optimization methods.The STDP method optimizes the landing trajectories separately in spatial and temporal dimensions

enabling UAVs to adopt more aggressive flight strategies in complex scenarios.Furthermore

the objective function is meticulously designed to account for the UAV’s landing time and motor power consumption model

formulating a second-order cone programming problem to expedite the solution process while ensuring high-quality and efficient solutions.Simulated results indicate that

compared to spatio-temporal coupled planning methods

the STDP method generates the trajectories that closely adhere to kinematic constraints

substantially reducing the task completion time and enhancing the mission efficiency.Additionally

the empirical tests in real-world scenarios confirm the reliability and efficacy of STDP method in practical application.

关键词

Keywords

references

叶立威 , 吴钧皓 , 戚远航 , 等 . 改进混合粒子群算法求解带时间窗的无人机与车辆协同路径调度问题 [J ] . 计算机应用研究 , 2024 , 41 ( 8 ): 2336 - 2342 .

YE L W , WU J H , QI Y H , et al. Improved hybrid particle swarm optimization algorithm for vehicle routing problem with drone and time window [J ] . Application Research of Computers , 2024 , 41 ( 8 ): 2336 - 2342 . (in Chinese)

EDYTA L K , ANNA S M , CZUBAJ S , et al. Take-off and landing magnetic system for UAV carriers [J ] . Journal of Marine Engineering & Technology , 2017 ,16: 298 - 304 .

张云飞 , 林德福 , 郑多 , 等 . 多目标时空同步协同攻击无人机任务分配与轨迹优化 [J ] . 兵工学报 , 2021 , 42 ( 7 ): 1482 - 1495 .

ZHANG Y F , LIN D F , ZHENG D , et al. Task allocation and trajectory optimization of UAV for multi-target time-space synchronization cooperative attack [J ] . Acta Armamentarii , 2021 , 42 ( 7 ): 1482 - 1495 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2021.07.016 http://doi.org/10.3969/j.issn.1000-1093.2021.07.016 A task allocation and trajectory optimization algorithm for multi-target attack is proposed for the coordinated attack task of distributed UAVs attacking multiple targets in a complex battlefield environment. The typical multi-target strike mission scenarios and UAV models are established. A search map with no-fly zones as nodes is constructed based on Delaunay triangle theory. A<sup>*</sup> algorithm is used to achieve the least threatening single-aircraft path search. In terms of UAV dynamics constraints and minimum energy loss, the time adjustment factor is introduced and the distributed UAV time-space synchronization trajectory optimization method based on Bezier curve is adopted to obtain the optimized trajectory for simultaneous attack on multiple targets. A trajectory tracking controller was designed for simulation track of pre-planned trajectory. The simulated results show that the proposed multi-target attack task allocation and trajectory optimization method can achieve multi-angle, time-space synchronization, and distributed coordinated attack on multiple targets, and it has strong robustness against interference such as noise and wind gusts.

EFSTRATIOS K , CHARALAMPOS S , MARIA T , et al. Computer vision for autonomous UAV flight safety an overview and a vision-based safe landing pipeline example [J ] . ACM Computing Surveys , 2021 , 54 ( 9 ): 1 - 37 .

MD S A , JARED O . A survey of safe landing zone detection techniques for autonomous unmanned aerial vehicles (UAVs) [J ] . Expert Systems with Applications , 2021 ,179:115091.

ADRIAN B , DANIEL C . A different approach to solving the PBVS control problem [C ] //Proceedings of the 2020 IEEE 29th International Symposium on Industrial Electronics . Delft,the Netherlands : IEEE , 2020 .

EDUARDO R , RAUL M , MARCO T , et al. Second-order position-based visual servoing of a robot manipulator [J ] . IEEE Robotics and Automation Letters , 2024 , 9 ( 1 ): 207 - 214 .

ARMANDO M M , HERMAN C , GORDILLO J L , et al. IBVS based on adaptive sliding mode control for a quadrotor target tracking under perturbations [J ] . Mechatronics , 2022 ,88:102909.

AHMED A , KHALED K , MOHAMMED A , et al. An advanced IBVS-flatness approach for real-time quadrotor navigation:a full control scheme in the image plane [J ] . Machines , 2024 , 12 ( 5 ): 350 .

XU Y B , LIU Z H , WANG X K . Monocular vision based autonomous landing of quadrotor through deep reinforcement learning [C ] //Proceedings of the 2018 37th Chinese Control Conference.Wuhan, China:IEEE , 2018 .

JIANG Z L , SONG G H . A Deep Reinforcement learning strategy for UAV autonomous landing on a platform [C ] //Proceedings of the 2022 International Conference on Computing,Robotics and System Sciences.Macau, China:IEEE , 2022 .

RICHTER C , BRY A , ROY N . Polynomial trajectory planning for aggressive quadrotor flight in dense indoor environments [C ] //Proceedings of Robotics Research:The 16th International Symposium ISRR.Singapore:Springer , 2016 .

ALEXANDER B , BANI A , JAKUB K , et al. Minimum energy route optimisation of a quad-copter UAV with landing incentivisation [C ] //Proceedings of the 2022 IEEE 25th International Conference on Intelligent Transportation Systems.Macau, China:IEEE , 2022 .

MARCOS F , SANTOS R , ALEXANDRE S B , MARIO S . Landing a UAV on static or moving platforms using a formation controller [J ] . IEEE Systems Journal , 2021 , 15 ( 1 ): 37 - 45 .

ZHANG G X , KUANG H L , LIU X F . Fast trajectory optimization for quadrotor landing on a moving platform [C ] // Proceedings of the International Conference on Unmanned Aircraft Systems.Athens, Greece:IEEE , 2020 .

GAO F , WU W , LIN Y , et al. Online safe trajectory generation for quadrotors using fast marching method and bernstein basis polynomial [C ] //Proceedings of the 2018 IEEE International Conference on Robotics and Automation.Brisbane,QLD, Australia:IEEE , 2018 .

JESUS T , BRETT T , MICHAEL E , et al. FASTER:fast and safe trajectory planner for navigation in unknown environments [J ] . IEEE Transactions on Robotics , 2022 , 38 ( 2 ): 922 - 938 .

DANIEL M , VIJAY K . Minimum snap trajectory generation and control for quadrotors [C ] //Proceedings of the 2011 IEEE International Conference on Robotics and Automation.Shanghai, China:IEEE , 2011 .

RAFAEL M C , FRANCISCO S , ANDRY M . A multimodal perception system for precise landing of UAVs in offshore environments [J ] . Journal of Field Robotics , 2024 , 22 ( 1 ): 1 - 22 .

CHANG C W , LO L Y , CHEUNG H G , et al. Proactive guidance for accurate UAV landing on a dynamic platform:a visual-inertial approach [J ] . Sensors 2022 , 22 ( 1 ): 404 .

ZENG Q X , JIN Y , YU H , et al. A UAV localization system based on double UWB tags and IMU for landing platform [J ] . IEEE Sensors Journal , 2023 , 23 ( 9 ): 10100 - 10108 .

GAO F , WU W , PAN J , et al. Optimal time allocation for quadrotor trajectory generation [C ] //Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems.Madrid, Spain:IEEE , 2018 .

WANG Z P , ZHOU X , XU C , et al. Geometrically constrained trajectory optimization for multicopters [J ] . IEEE Transactions on Robotics , 2022 , 38 ( 5 ): 3259 - 3278 .

YU X , ZHOU X B , GUO K X , et al. Safety flight control for a quadrotor UAV using differential flatness and dual-loop observers [J ] . IEEE Transactions on Industrial Electronics , 2022 , 69 ( 12 ): 13326 - 13336 .

Views

251

下载量

CNKI被引量

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on Trajectory Planning Control Method of Intelligent Vehicle Based on Velocity Obstacle Model

Real-time Trajectory Planning for Fixed-wing UAVs Based on Exact Convex Relaxation

A Trajectory Planning Method Based on DQN Variable Dynamic Intelligent Decision

A DDPG-based Trajectory Planning Method for Collision Avoidance of Morphing Spacecraft

Dynamic Reconfigurable Adaptive UGV Formation System

Related Author

Gang LI

Yang HE

Yaxuan LI

Xinfu LIU

Jun LIU

Tianyun DING

Xingling SHAO

Jialin ZHU

Related Institution

College of Automobile and Traffic Engineering, Liaoning University of Technology

School of Aerospace Engineering, Beijing Institute of Technology

School of Instrument and Electronics, North University of China

Research and Development Center, China Academy of Launch Vehicle Technology

Key Laboratory of Instrumentation Science & Dynamic Measurement of Ministry of Education, North University of China

Postal code：100089
Tel：010-68963060/68962718 Fax：010-68963025 Email：bgxb@cos.org.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备05059581号-4 京公网安备11010802024360号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
Total Visits：44524 Visits Today：504

⁰