基于时空解耦规划方法的无人机自主降落策略

doi:10.12382/bgxb.2024.0653

摘要/Abstract

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。

关键词: 多旋翼无人机, 自主降落, 时空解耦方法, 轨迹规划

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

中图分类号:

TJ301

徐扬, 魏超, 冯付勇, 胡乐云. 基于时空解耦规划方法的无人机自主降落策略[J]. 兵工学报, 2025, 46(7): 240653-.

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-.

图/表 16

图1 时空解耦规划方法概述图

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

图2 FM算法概述图

Fig.2 Overview of the Fast Marching algorithm

图3 安全走廊生成过程示意图

Fig.3 Schematic diagram of the safe corridor generation process

图4 空间域轨迹生成策略

Fig.4 Spatial trajectory generation strategy

图5 分段轨迹离散化过程

Fig.5 Discretization process of segmented trajectories

表1 UAV试验平台性能参数明细

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

图6 静态降落仿真实验环境

Fig.6 Static landing simulation environment

图7 静态自主降落仿真结果

Fig.7 Static autonomous landing simulation results

表2 静态自主降落对比试验仿真结果

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

图8 动态自主降落仿真场景

Fig.8 Dynamic autonomous landing simulation scenario

图9 动态自主降落仿真结果

Fig.9 Dynamic autonomous landing simulation results

图10 动态自主降落对比试验结果

Fig.10 Comparative experimental results of dynamic autonomous landing

图11 实际测试试验设备

Fig.11 The experimental equipment in real-world applications

图12 静态自主降落测试

Fig.12 Static landing test in real-world applications

图13 动态自主降落测试

Fig.13 Dynamic landing test in real-world applications

图14 三维飞行轨迹

Fig.14 UAV 3D flight trajectory and UGV trajectory

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