Autonomous Landing of UAVs based on Spatio-temporal Decomposition Planning

doi:10.12382/bgxb.2024.0653

Abstract

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.

Key words: multirotor unmanned aerial vehicle, autonomous landing, spatio-temporal decomposition, trajectory planning

CLC Number:

TJ301

XU Yang, WEI Chao, FENG Fuyong, HU Leyun. Autonomous Landing of UAVs based on Spatio-temporal Decomposition Planning[J]. Acta Armamentarii, 2025, 46(7): 240653-.

Figures/Tables 16

Fig.1 Overview diagram of spatio-temporal decomposition planning method

Fig.2 Overview of the Fast Marching algorithm

Fig.3 Schematic diagram of the safe corridor generation process

Fig.4 Spatial trajectory generation strategy

Fig.5 Discretization process of segmented trajectories

Table 1 Detailed parameters of UAV test platform

飞行器参数	指标明细
无人机类型	四旋翼无人机
最大起飞重量/kg	4
机架类型	X型
轴距/mm	600
挂载传感器	双目深度相机、光电吊舱等
最大平飞速度/(m·s^-1)	15

Fig.6 Static landing simulation environment

Fig.7 Static autonomous landing simulation results

Table 2 Comparative experimental simulation results of static autonomous landing

测试算法	STCP	STDP
任务耗时/s	19.2	7.9
平均速度/(m·s^-1)	0.84	1.82
平均加速度/(m·s^-2)	0.46	1.25
最大速度/(m·s^-1)	1.18	2.77
最大加速度/(m·s^-2)	0.43	2.36

Fig.8 Dynamic autonomous landing simulation scenario

Fig.9 Dynamic autonomous landing simulation results

Fig.10 Comparative experimental results of dynamic autonomous landing

Fig.11 The experimental equipment in real-world applications

Fig.12 Static landing test in real-world applications

Fig.13 Dynamic landing test in real-world applications

Fig.14 UAV 3D flight trajectory and UGV trajectory

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