基于着色Petri网的无人机侦察战术规划

doi:10.12305/j.issn.1001-506X.2022.03.22

Abstract

Abstract:

Unmanned aerial vehicle (UAV) tactical planning is the core of UAV operation. This paper takes the resource allocation and target reasoning in UAV tactical planning as the research object. Aiming at the problem that the traditional Petri net can not accurately simulate the modeling of positive and negative effects of resources and tasks in tactical planning, after describing the hierarchical decomposition of the overall task according to the order of plan and target by using the traditional Petri net. The colored Petri net is used to design the inter network structure, and the overall objective management diagram and variable management diagram are used to realize the simulation of positive and negative effects in resource consumption and multi-objective task planning. Finally, the proposed method is applied to UAV reconnaissance mission to verify the effectiveness of the method.

Key words: colored Petri nets, resource allocation, positive and negative effects, unmanned aerial vehicle (UAV), tactical planning

CLC Number:

TP391.9

Yuanyuan ZHANG, Yang GAO, Peng ZHU, Jintao LIU, Shushan GU. UAV reconnaissance tactical planning based on colored Petri nets[J]. Systems Engineering and Electronics, 2022, 44(3): 900-907.

Figures/Tables 16

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References 30

1	朱剑佑. 无人机任务规划研究[J]. 无线电工程, 2007, (12): 56- 58. doi: 10.3969/j.issn.1003-3106.2007.12.019
	ZHU J Y . Research on unmanned aerial vehicle mission planning[J]. Radio Engineering, 2007, (12): 56- 58. doi: 10.3969/j.issn.1003-3106.2007.12.019
2	沈林成, 陈璟, 王楠. 飞行器任务规划技术综述[J]. 航空学报, 2014, 35 (3): 593- 606.
	SHEN L C , CHEN J , WANG N . Overview of air vehicle mission planning techniques[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35 (3): 593- 606.
3	PASHNA M , YUSOF R , ISMAIL Z H , et al. Autonomous multi-robot tracking system for oil spills on sea surface based on hybrid fuzzy distribution and potential field approach[J]. Ocean Engineering, 2020, 207 (6): 107238.
4	BATISTA J , SOUZA D , SILVA J , et al. Trajectory planning using artificial potential fields with metaheuristics[J]. IEEE Latin America Transactions, 2020, 18 (5): 914- 922. doi: 10.1109/TLA.2020.9082920
5	KUMAR P B , RAWAT H , PARHI D R . Path planning of humanoids based on artificial potential field method in unknown environments[J]. Expert Systems, 2019, 36 (1): e12360.
6	SANG H Q , YOU Y S , SUN X J , et al. The hybrid path planning algorithm based on improved A^* and artificial potential field for unmanned surface vehicle formations[J]. Ocean Engineering, 2021, 223 (4): 108709.
7	GIBSON J , SCHULER T , MCGUIRE L , et al. Swarm and multi-agent time-based A^* path planning for LTA3 systems[J]. Unmanned Systems, 2020, 8 (3): 253- 260. doi: 10.1142/S2301385020500181
8	THORESEN M , NIELSEN N H , MATHIASSEN K , et al. Path planning for ugvs based on traversability hybrid A^*[J]. IEEE Robotics and Automation Letters, 2021, 6 (2): 1216- 1223. doi: 10.1109/LRA.2021.3056028
9	XIONG C K , LU D , ZENG Z , et al. Path planning of multiple unmanned marine vehicles for adaptive ocean sampling using elite group-based evolutionary algorithms[J]. Journal of Intelligent & Robotic Systems, 2020, 99 (3): 875- 889.
10	LIN B L , ZHAO Y N , LIN R X , et al. Integrating traffic routing optimization and train formation plan using simulated annealing algorithm[J]. Applied Mathematical Modelling, 2021, 93, 811- 830. doi: 10.1016/j.apm.2020.12.031
11	ASIF R, LOFFEL H, ASSAVASANG V, et al. Aerial path planning for multi-vehicles[C]//Proc. of the IEEE 2nd International Conference on Artificial Intelligence and Knowledge Engineering, 2019: 97-99.
12	HERTZ A , RIDREMONT T . A tabu search for the design of capacitated rooted survivable planar networks[J]. Journal of Heuristics, 2020, 26 (6): 829- 850. doi: 10.1007/s10732-020-09453-x
13	ZHENG J G , MI X M , LIAO H C . Advanced planning and scheduling based on the constraint theory and improved tabu search algorithm[J]. Recent Patents on Engineering, 2020, 13 (2): 546- 549.
14	CHÂARI I , KOUBÂA A , BENNACEUR H , et al. On the ade- quacy of tabu search for global robot path planning problem in grid environments[J]. Procedia Computer Science, 2014, 32 (1): 604- 613.
15	TRAN N H , NGUYEN A D , NGUYEN T N . A genetic algorithm application in planning path using b-spline model for autonomous underwater vehicle (AUV)[J]. Applied Mechanics and Materials, 2020, 902 (1): 54- 64.
16	LIN L H , WU C Z , MA L . A genetic algorithm for the fuzzy shortest path problem in a fuzzy network[J]. Complex & Intelligent Systems, 2020, 7 (3): 225- 234.
17	RATH A K , PARHI D R , DAS H C , et al. Path optimization for navigation of a humanoid robot using hybridized fuzzy-genetic algorithm[J]. International Journal of Intelligent Unmanned Systems, 2019, 7 (1): 77- 79.
18	SHI L , XU S K . UAV path planning with qos constraint in device-to-device 5G networks using particle swarm optimization[J]. IEEE Access, 2020, 8, 137884- 137896. doi: 10.1109/ACCESS.2020.3010281
19	SHAO S K , YU P , HE C L , et al. Efficient path planning for UAV formation via comprehensively improved particle swarm optimization[J]. ISA Transactions, 2020, 97 (2): 415- 430.
20	KUMAR P B , PARHI D R , SAHU C . An approach to optimize the path of humanoid robots using a hybridized regression-adaptive particle swarm optimization-adaptive ant colony optimization method[J]. Industrial Robot, 2019, 46 (1): 5- 10.
21	CHEN Y Q , GUO J L , YANG H , et al. Research on navigation of bidirectional A^* algorithm based on ant colony algorithm[J]. The Journal of Supercomputing, 2021, 77 (1): 23- 27. doi: 10.1007/s11227-020-03258-2
22	DAI Y , ZHAO M . Manipulator path-planning avoiding obstacle based on screw theory and ant colony algorithm[J]. Journal of Computational and Theoretical Nanoscience, 2016, 13 (1): 922- 927. doi: 10.1166/jctn.2016.4894
23	MOUHCINE E, KHALIFA M, MOHAMED Y. Route optimization for school bus scheduling problem based on a distributed ant colony system algorithm[C]//Proc. of the IEEE International Conference on Intelligent Systems and Computer, 2017.
24	JENSEN K . Coloured petri nets[M]. Berlin: Springer Berlin Heidelberg, 1987: 249- 299.
25	JENSEN K . Coloured Petri nets: basic concepts[M]. Berlin: Springer Berlin Heidelberg, 1992: 23- 50.
26	THANGARAJAH J, WINIKOFF M, LIN P, et al. Avoiding resource conflicts in intelligent agents[C]//Proc. of the 15th European Conference on Artifical Intelligence, 2002: 18-22.
27	THANGARAJAH J, PADGHAM L. An empirical evaluation of reasoning about resource conflicts[C]//Proc. of the 3rd International Joint Conference on Autonomous Agents and Multiagent Systems, 2004: 1298-1299.
28	THANGARAJAH J, PADGHAM L, WINIKOFF M. Detecting & exploiting positive goal interaction in intelligent agents[C]//Proc. of the International Joint Conference on Autonomous Agents & Multiagent Systems, 2003: 401-408.
29	VISSER S , THANGARAJAH J , HARLAND J , et al. Prefe-rence-based reasoning in BDI agent systems[J]. Autonomous Agents and Multi-Agent Systems, 2015, 30 (2): 291- 330.
30	THANGARAJAH J , LIN P . Computationally effective reaso-ning about goal interactions[J]. Journal of Automated Reaso-ning, 2011, 47 (1): 17- 56. doi: 10.1007/s10817-010-9175-0

元素	含义	元素	含义
p₁	资源量	f₁	q
t₁	检查余量	f₂	q
t₂	消耗资源	f₃	q-x

库所	含义
p₁	子目标1
p₂	子目标1资源信息总览
p₃	计划1资源信息总览
p₄	计划1
p₅	计划2资源信息总览
p₆	计划2
p₇	计划3资源信息总览
p₈	计划3

元素	含义
p₁	目标序列加入新目标g₁
p₂	g₁完成
p₃	新目标g₁资源信息总览
t₁	更新资源信息总览
t₂	开始执行新目标g₁
t₃	更新资源信息总览
f₁	R_new
f₂	R_old
f₃	R_old
f₄	R_new

元素	含义	元素	含义
p₁	目标变量v₁	p₈	已完成目标列表
p₂	子目标g₁	p₉	总目标完成
p₃	计划	t₁	检查目标变量
p₄	子目标1完成	t₂	设置目标变量
p₅	开始推理	t₃	检查目标变量
p₆	目标未实现	t₄	开始执行计划
p₇	未完成目标列表	t₅	判断已完成目标值
p₇	未完成目标列表	-	-

元素	含义	元素	含义
p₇	子目标g₁	t₄	开始执行计划、检查并设置保护托肯
p₈	计划	t₅	开始执行计划
p₉	子目标2完成	t₆	设置目标变量、设置保护托肯

UAV reconnaissance tactical planning based on colored Petri nets

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元素	含义
p₁	侦察任务目标开始
p₂	侦察计划开始
p₃, p₃₄	传输A域信息子目标
p₄, p₃₅	传输A域信息资源总览
p₅, p₃₆	正效应推理
p₆, p₇, p₃₇, p₃₈	目标未实现
p₈	传输A域信息计划1资源总览
p₉	传输A域信息计划1开始
p₁₀	传输A域信息计划1实现
p₁₁	传输A域信息计划2资源总览
p₁₂	传输A域信息计划2开始
p₁₃	传输A域信息计划2实现
p₁₄	传输A域信息子目标完成
p₁₅(p₂₀)	飞至A(B)地子目标
p₁₆(p₂₂)	飞至A(B)地计划资源总览
p₁₇(p₂₃)	飞至A(B)地计划开始
p₁₈(p₂₄)	飞至A(B)地计划实现
p₁₉(p₂₅)	飞至A(B)地子目标完成
p₂₆(p₃₀)	侦察A(B)地子目标
p₂₁(p₄₇)	侦察A(B)地计划资源总览
p₂₇(p₃₁)	侦察A(B)地计划起始
p₂₈(p₃₂)	侦察A(B)地计划完成
p₂₉(p₃₃)	侦察A(B)地目标完成
p₄₅	侦察计划完成
p₄₆	侦察任务目标完成
p₄₇	待完成子目标库所
p₄₈	执行序列加入新目标
p₄₉	某子目标完成
p₅₀	已完成子目标
p₅₁	全部子目标完成
p₅₂	燃油总量
p₅₃	位置
t₂	侦察计划的子目标分解
t₃, t₂₄	检查目标变量
t₅, t₆, t₂₆, t₂₇, t₁₁, t₁₅, t₁₈, t₂₁	相关计划行动序列
t₃₂	与t₄, t₉, t₁₀, t₁₄, t₁₇, t₂₀, t₂₃, t₂₅关联, 同时更新资源信息总览, 并对待保护变量库所加入保护托肯
t₃₃	更新资源信息总览
t₃₅	判断子目标是否全部完成
t₃₇	与t₁₁, t₁₅, t₂₇, t₁₈, t₂₁, t₆关联