复杂环境下多无人机目标跟踪与避障联合航迹规划

doi:10.12382/bgxb.2022.0525

摘要/Abstract

摘要：

针对多无人机在密集障碍环境中协同执行地面目标跟踪任务时存在避障能力不足的问题,提出一种基于零空间方法的多无人机目标跟踪与避障联合航迹规划算法。利用Lyapunov导引向量场得到协同对地面目标进行Standoff跟踪时的无人机目标跟踪速度指令,构建障碍物模型和避障人工势场函数,利用人工势场方法得到无人机避障速度指令;基于零空间方法,将避障任务设定为高优先级任务,通过将目标跟踪速度指令向避障任务零空间投影后再与避障速度指令相加的联合航迹规划方法,获取综合后的无人机速度指令。通过仿真分析验证联合航迹规划方法的有效性。仿真结果表明,联合航迹规划方法能够在密集障碍物存在的复杂环境中实时规划有效航迹,保证多无人机避开密集障碍物,持续跟踪目标,并且多无人机具有良好的协同性。

关键词: 无人机, 目标跟踪, 避障, 航迹规划, 零空间方法

Abstract:

In scenarios where multiple UAVs need to collaborate in ground target tracking tasks within obstacle-dense environments, the obstacle avoidance ability may be insufficient. To address this challenge, we propose a joint trajectory planning algorithm for multiple UAVs, enabling them to simultaneously track targets and avoid obstacles using the null-space method. First, Lyapunov guidance vector field is used to obtain the target tracking velocity command for UAVs when they perform standoff tracking to the ground target coordinately. Obstacle model and artificial potential field function for obstacle avoidance are established, and the obstacle avoidance velocity command for UAVs is obtained by using artificial potential field method. Second, based on the null-space method, the obstacle avoidance task is set as a high-priority task, and the integrated UAV velocity command is obtained through the joint trajectory planning method, which projects the target tracking velocity command into the null-space of the obstacle avoidance task and then adds it to the obstacle avoidance velocity command. Through simulation analysis, the effectiveness of the proposed method is verified. Simulated results show that the proposed joint trajectory planning method can plan effective trajectories for multiple UAVs in real time in complex environments with dense obstacles, and ensure that UAVs avoid dense obstacles and maintain target tracking with good coordination between UAVs.

Key words: unmanned aerial vehicle, target tracking, obstacle avoidance, trajectory planning, null-space method

中图分类号:

V279

赵军民, 何浩哲, 王少奇, 聂聪, 焦迎杰. 复杂环境下多无人机目标跟踪与避障联合航迹规划[J]. 兵工学报, 2023, 44(9): 2685-2696.

ZHAO Junmin, HE Haozhe, WANG Shaoqi, NIE Cong, JIAO Yingjie. Joint Trajectory Planning for Multiple UAVs Target Tracking and Obstacle Avoidance in a Complicated Environment[J]. Acta Armamentarii, 2023, 44(9): 2685-2696.

图/表 20

图1 多UAV Standoff跟踪示意图

Fig.1 Schematic diagram of standoff tracking of multiple UAVs

图2 障碍物及人工势场示意图

Fig.2 Schematic diagram of an obstacle and its artificial potential

图3 UAV在Standoff圆上的相位关系

Fig.3 Phase relationship between UAVs on a standoff circle

图4 基于零空间方法的联合航迹规划示意图

Fig.4 Schematic diagram of null-space-based joint trajectory planning

图5 UAV航迹规划流程示意图

Fig.5 Schematic diagram of UAV trajectory planning process

表1 UAV初始位置

Table 1 Initial positions of UAVs

UAV编号	UAV1	UAV2	UAV3
初始位置/m	(750,800)	(700,700)	(600,600)

表2 LGVF方法各参数取值

Table 2 LGVF method parameters

参数	R/m	v₀/(m·s^-1)	k₁	k₃
数值	300	20	1/75π	1/75π

表3 障碍物位置

Table 3 Positions of obstacles

障碍物编号	障碍物1	障碍物2	障碍物3
位置/m	(330,550)	(250,270)	(50,400)

图6 协同跟踪非机动运动目标的仿真结果

Fig.6 Simulation results of cooperative tracking of a non-maneuvering target

表4 机动目标运动过程

Table 4 Motion process of a maneuvering target

时间/s	运动形式	速度/ (m·s^-1)	角速度/ ((°) s^-1)
0~260	沿x轴正向做匀速直线运动	10	0
260~280	沿逆时针方向做匀速转弯运动	10	4.5
280~330	沿y轴正向做匀速直线运动	10	0
330~350	沿顺时针方向做匀速转弯运动	10	-4.5
350~800	沿x轴正向做匀速直线运动	10	0

表5 障碍物位置和参数

Table 5 Positions and parameters of other obstacles

障碍物编号	位置/m	r_min/m	r_max/m
障碍物4	(1180,-200)	50	150
障碍物5	(1400,-150)	50	150
障碍物6	(1630,-90)	80	160
障碍物7	(3050, 0)	80	160
障碍物8	(3050, 290)	80	160

图7 协同跟踪机动运动目标的仿真结果

Fig.7 Simulation results of cooperative tracking of a maneuvering target

表6 不同rmin下各UAV与障碍物7中心的最小距离

Table 6 Minimum distance between each UAV and the center of obstacle 7 with different radius of obstacle 7 m

UAV编号	r_min/m
UAV编号	80	100	150
UAV1	123.5743	126.6565	153.7342
UAV2	146.1178	146.6368	156.5215
UAV3	93.7131	107.9902	152.0367

表7 不同rmin下各UAV与障碍物8中心的最小距离

Table 7 Minimum distance between each UAV and the center of obstacle 8 with different radius of obstacle 8 m

UAV编号	r_min/m
UAV编号	80	100	150
UAV1	162.0321	163.5365	170.7342
UAV2	118.7405	122.8926	153.7433
UAV3	129.6581	133.1933	153.5743

表8 不同rmax下各UAV与障碍物7中心的最小距离

Table 8 Minimum distance between each UAV and the center of obstacle 7 with different APF maximum radius m

UAV编号	r_max/m
UAV编号	160	220	300
UAV1	125.5743	173.6564	248.1498
UAV2	146.1178	184.1363	249.3040
UAV3	93.7131	128.3524	203.1966

表9 不同rmax下各UAV与障碍物8中心的最小距离

Table 9 Minimum distance between each UAV and the center of obstacle 8 with different APF maximum radius m

UAV编号	r_max/m
UAV编号	160	220	300
UAV1	162.0321	224.1401	303.2632
UAV2	118.7405	146.4538	230.6955
UAV3	129.6581	153.9718	226.0562

图8 不同rmax下UAV在Standoff圆上相位差

Fig.8 Phase difference between UAVs on a standoff circle under different APF maximum radius

图9 直接相加方法仿真结果

Fig.9 Simulation results using a adding-up method

图10 扰动流场方法仿真结果

Fig.10 Simulation results using a interfered fluid method

表10 3种方法下各UAV与障碍物中心的最小距离

Table 10 Minimum distance between each UAV and obstacles using three different methods

UAV	零空间方法下与障碍物最小距离/m	直接相加方法下与障碍物最小距离/m	扰动流场方法下与障碍物最小距离/m
UAV1	73.919	73.624	126.462
UAV2	108.069	54.895	86.388
UAV3	63.409	61.594	69.446

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