UAV Online Track Planning Based on DMOEA-APTC Algorithm

doi:10.16182/j.issn1004731x.joss.23-0615

Abstract

Abstract:

In order to solve the dynamic multi-objective optimization problem with time correlation, this paper introduces the concept of time correlation feature and establishes the model of UAV time-correlation dynamic multi-objective optimization problem moedl on the basis of UAV online track planning problem, and proposes a dynamic multi-objective double-layer optimization algorithm using adaptive predictive response mechanism and time-correlation optimization mechanism (DMOEA-APTC). The intensity of environmental change was judged according to the correlation of environmental change and different response mechanisms were used to quickly adapt to environmental change. In the optimization process, the least square method was used to learn the historical data to fit the future predicted value of the flight path, and the optimization mode of "only optimize the current" or "optimize the current and future at the same time" was adaptively selected according to the reliability of the prediction. The improved Chebyshev decomposition method was used to make the decision of the preferred flight path. The experimental results show that the proposed algorithm can reduce the flight duration and have higher survival probability in complex flight environments, improve the flight stability of the UAV, and deal with the online flight path planning more reasonably and effectively.

Key words: UAV, online track planning, time correlation characteristics, dynamic multi-objective optimization algorithm, prediction strategy, dynamic threat

CLC Number:

TP273

Li Erchao, Zhang Shenghui. UAV Online Track Planning Based on DMOEA-APTC Algorithm[J]. Journal of System Simulation, 2024, 36(9): 2086-2099.

Figures/Tables 19

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Table 1

Threat feed parameters

威胁	$μ x$	$μ y$	$σ x$	$σ y$
K1	-30	10	3.0	3.0
K2	5	0	3.0	3.0
K3	20	-10	3.0	3.0
K4	25	-5	3.0	3.0
K5	30	0	2.7	2.7
K6	30	-40	3.0	3.0
K7	-10	40	3.0	3.0
K8	-20	-30	2.7	2.7
K9	0	-30	2.1	2.1
K10	-14	-10	2.4	2.4
K11	-10	20	3.0	3.0
K12	10	30	2.1	2.1
K13	-30	-10	3.3	3.3
K14	-30	40	3.9	3.9
K15	20	20	4.5	4.5
K16	10	-20	4.5	4.5
K17	30	40	3.0	3.0

Table 1

Fig. 6

Fig. 7

Fig. 8

Table 2

Plan results for static threat scenarios

偏好设置	规划方法	飞行时间/s	生存概率	航向角平均变化值/(°)
$(ω 1, ω 2) Τ = (0.7,0.3) Τ$	DMOEA-APTC	171	0.931 68	9.667 7
$(ω 1, ω 2) Τ = (0.7,0.3) Τ$	DNSGA-II	175	0.907 86	11.457 4
$(ω 1, ω 2) Τ = (0.3,0.7) Τ$	DMOEA-APTC	165	0.876 83	5.309 0
$(ω 1, ω 2) Τ = (0.3,0.7) Τ$	DNSGA-II	168	0.850 36	8.586 5

Table 2

Fig. 9

Fig. 10

Table 3

Plan outcomes in emergent threat situations

偏好设置	规划方法	飞行时间/ s	生存概率	航向角平均变化值/(°)
$(ω 1, ω 2) Τ = (0.7,0.3) Τ$	DMOEA-APTC	306	0.953 26	12.943 7
$(ω 1, ω 2) Τ = (0.7,0.3) Τ$	DNSGA-II	312	0.957 49	14.849 7
$(ω 1, ω 2) Τ = (0.3,0.7) Τ$	DMOEA-APTC	297	0.812 97	8.558 2
$(ω 1, ω 2) Τ = (0.3,0.7) Τ$	DNSGA-II	306	0.850 36	11.978 3

Table 3

Fig. 11

Fig. 12

Table 4

Plan outcomes in mutation threat situations

偏好设置	规划方法	飞行时间/s	生存概率	航向角平均变化值/(°)
$(ω 1, ω 2) Τ = (0.7,0.3) Τ$	DMOEA-APTC	438	0.916 01	11.887 3
$(ω 1, ω 2) Τ = (0.7,0.3) Τ$	DNSGA-II	447	0.892 31	14.160 6
$(ω 1, ω 2) Τ = (0.3,0.7) Τ$	DMOEA-APTC	429	0.844 18	8.930 3
$(ω 1, ω 2) Τ = (0.3,0.7) Τ$	DNSGA-II	432	0.810 41	11.247 6

Table 4

Fig. 13

Fig. 14

Table 5

Plan outcomes in case of preference changes

偏好设置	规划方法	飞行时间/s	生存概率	航向角平均变化值/(°)
$(0.7,0.3) Τ → (0.3,0.7) Τ$	DMOEA-APTC	435	0.884 95	11.036 7
$(0.7,0.3) Τ → (0.3,0.7) Τ$	DNSGA-II	441	0.875 93	13.085 1

Table 5

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