面向复杂场景的层次式任务规划方法

doi:10.12305/j.issn.1001-506X.2025.04.22

摘要/Abstract

摘要：

针对复杂环境下态势动态变化、信息获取渠道受阻导致难以确保任务完成的问题, 开展面向复杂场景不确定性的任务规划技术研究。在分析任务方案内容的基础上, 提出灵活动态、细粒度分配的任务规划方法, 建立通用的层次式任务规划模型, 重点解决任务方案难以适应场景变化、不确定性因素发生时无法满足任务需求的问题, 提升在复杂场景下任务规划的有效性。通过仿真验证, 层次式任务规划方法能够提升70%的任务规划效率, 同时优化了资源利用率。

关键词: 复杂场景, 任务规划, 任务方案, 任务分配

Abstract:

Due to the dynamic changes of complex environments and obstacles in information acquisition channel, it is difficult to assure completing tasks successfully. The task planning technology research under uncertainty in complex scenarios is focused in this paper. After analyzing the content of task schemes, a flexible and dynamic task planning approach which supports fine-grained task allocation is proposed. A general hierarchical task planning model, which deals with difficulties of task schemes in adapting to changing environments and fulfilling task requirements under uncertainty is established. The proposed task planning model improves the effectiveness of task planning in complex scenarios. The simulation test demonstrate the hierarchical task planning approach is able to improve 70% task planning efficiency and optimize resource utilization rate.

Key words: complex scenario, task planning, task scheme, task allocation

中图分类号:

TP391

王雯, 赵凯南, 杨林, 杨雄军. 面向复杂场景的层次式任务规划方法[J]. 系统工程与电子技术, 2025, 47(4): 1255-1264.

Wen WANG, Kainan ZHAO, Lin YANG, Xiongjun YANG. Hierarchical task planning approach for complex scenarios[J]. Systems Engineering and Electronics, 2025, 47(4): 1255-1264.

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参考文献 30

1	孙鑫, 陈晓东, 曹晓文, 等. 军用任务规划技术综述与展望[J]. 指挥与控制学报, 2017, 3 (4): 289- 298.
	SUN X , CHEN X D , CAO X W , et al. Review and prospect of military mission planning technology[J]. Journal of Command and Control, 2017, 3 (4): 289- 298.
2	赵国宏, 罗雪山. 作战任务规划系统研究[J]. 指挥与控制学报, 2015, 1 (4): 391- 394.
	ZHAO G H , LUO X S . On operation mission planning system[J]. Journal of Command and Control, 2015, 1 (4): 391- 394.
3	HEBAH E , KAMAL Y T . Dynamic task allocation in an uncertain environment with heterogeneous multi-agents[J]. Autonomous Robots, 2019, 43 (7): 1639- 1664. doi: 10.1007/s10514-018-09820-5
4	张永夫, 刘洋, 袁禾. 不确定性下任务规划方法研究综述[J]. 军事运筹与系统工程, 2021, 35 (4): 68- 72.
	ZHANG Y F , LIU Y , YUAN H . Summary of research on mission planning methods under uncertainty[J]. Military Operations Research and Systems Engineering, 2021, 35 (4): 68- 72.
5	胡晓峰, 荣明. 关于联合作战规划系统的几个问题[J]. 指挥与控制学报, 2017, 3 (4): 273- 280.
	HU X F , RONG M . Joint operation planning systems: issues and prospects[J]. Journal of Command and Control, 2017, 3 (4): 273- 280.
6	SKALTSIS G M, SHIN H S, TSOURDOS A. A survey of task allocation techniques in MAS[C]//Proc. of the International Conference on Unmanned Aircraft Systems, 2021: 488-497.
7	马悦, 吴琳, 许霄, 等. 智能化作战任务规划需求分析[J]. 指挥控制与仿真, 2021, 43 (4): 61- 67.
	MA Y , WU L , XU X , et al. Requirement analysis of intelligent operation task planning[J]. Command Control & Simulation, 2021, 43 (4): 61- 67.
8	董倩. 基于行为树的空中作战行动分层智能规划方法研究[D]. 长沙: 国防科技大学, 2018.
	DONG Q. Study on intelligent hierarchical operations planning methodology for air combat based on behavior tree model[D]. Changsha: National University of Defense Technology, 2018.
9	SAFRONOV E, COLLEDANCHISE M, NATALE L. Task planning with belief behavior trees[C]//Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020.
10	吴歇尔. 面向多无人机的协同任务预分配及重分配研究[D]. 南昌: 南昌航空大学, 2018.
	WU X E. Coordination tasks pre-allocation and redistribution studies in UAVs[D]. Nanchang: Nanchang Hangkong University, 2018.
11	刘东红. 基于可计算任务树的人机混合智能任务规划新范式[J]. 指挥与控制学报, 2023, 9 (1): 85- 92.
	LIU D H . A new paradigm of human-machine hybrid intelligent mission planning based on computational mission tree[J]. Journal of Command and Control, 2023, 9 (1): 85- 92.
12	ZHANG Y X , CHEN C , SHI J M . Resource allocation heuristics for project scheduling[J]. Applied Mechanics and Materials, 2013, 409, 1539- 1542.
13	梁星星, 马扬, 冯旸赫, 等. 基于预测编码的样本自适应行动策略规划[J]. 软件学报, 2022, 33 (4): 1477- 1500.
	LIANG X X , MA Y , FENG Y H , et al. Sample adaptive policy planning based on predictive coding[J]. Journal of Software, 2022, 33 (4): 1477- 1500.
14	ZHANG Y, SREEDHARAN S, KULKARNI A, et al. Plan explicability and predictability for robot task planning[C]//Proc. of the IEEE International Conference on Robotics and Automation, 2017.
15	曹雷, 孙彧, 陈希亮, 等. 联合作战任务智能规划关键技术及其应用思考[J]. 国防科技, 2020, 41 (3): 49- 56.
	CAO L , SUN Y , CHEN X L , et al. The key technology in intelligent planning for joint operations and its application[J]. National Defense Science and Technology, 2020, 41 (3): 49- 56.
16	易侃, 张杰勇, 焦志强, 等. 基于层次任务网络的作战任务系统功能映射方法[J]. 系统工程与电子技术, 2023, 45 (10): 3183- 3191.
	YI K , ZHANG J Y , JIAO Z Q , et al. Combat task-system function mapping method based on hierarchical task network[J]. Systems Engineering and Electronics, 2023, 45 (10): 3183- 3191.
17	CHENG Y J , SUN L T , TOMIZUKA M . Human-aware robot task planning based on a hierarchical task model[J]. IEEE Robotics and Automation Letters, 2021, 6 (2): 1136- 1143.
18	OH Y , PATEL R , NGUYEN T , et al. Hierarchical planning with state abstractions for temporal task specifications[J]. Autonomous Robots, 2022, 46 (6): 667- 683.
19	张宏军, 邵天浩, 程恺, 等. 基于HTN的混合式作战计划监视与重规划方法研究[J]. 军事运筹与系统工程, 2021, 35 (2): 18- 25.
	ZHANG H J , SHAO T H , CHENG K , et al. Research on hybrid mission planning monitoring and replanning based on HTN[J]. Military Operations Research and Systems Engineering, 2021, 35 (2): 18- 25.
20	邵天浩, 张宏军, 程恺, 等. 层次任务网络中的重新规划研究综述[J]. 系统工程与电子技术, 2020, 42 (12): 2833- 2846. doi: 10.3969/j.issn.1001-506X.2020.12.21
	SHAO T H , ZHANG H J , CHENG K , et al. Review of replanning in hierarchical task network[J]. Systems Engineering and Electronics, 2020, 42 (12): 2833- 2846. doi: 10.3969/j.issn.1001-506X.2020.12.21
21	BAI G Q , CHEN Y L , HU X Y , et al. Correction redistribution mechanism based on forward-reverse solutions and real-time path dynamic adaptive re-planning for multi-AUVs collaborative search[J]. IEEE Trans.on Intelligent Transportation Systems, 2024, 25 (7): 7583- 7601.
22	王喆, 王红卫, 唐攀, 等. 考虑资源分配的HTN规划方法及其应用[J]. 管理科学学报, 2013, 16 (3): 53- 60.
	WANG Z , WANG H W , TANG P , et al. HTN planning method with resource allocation and its application[J]. Journal of Management Sciences in China, 2013, 16 (3): 53- 60.
23	LI M L, YANG W J, CAI Z X, et al. Integrating decision sharing with prediction in decentralized planning for multi-agent coordination under uncertainty[C]//Proc. of the 28th International Joint Conference on Artificial Intelligence Main Track, 2019.
24	马悦, 吴琳, 郭圣明. 作战任务分配建模及求解方法研究[J]. 系统仿真学报, 2023, 35 (4): 887- 898.
	MA Y , WU L , GUO S M . Research on modeling and solution method of operational tasks assignment[J]. Journal of System Simulation, 2023, 35 (4): 887- 898.
25	刘兆鹏, 司光亚, 唐宇波, 等. 联合作战资源调度模型研究[J]. 科技导报, 2019, 37 (13): 23- 31.
	LIU Z P , SI G Y , TANG Y B , et al. A joint operation resource scheduling model by way of genetic fuzzy network[J]. Science & Technology Review, 2019, 37 (13): 23- 31.
26	马硕, 马亚平. 基于分层目标任务网络的作战任务规划方法[J]. 火力与指挥控制, 2020, 45 (2): 110- 114.
	MA S , MA Y P . A mission planning method based on hierarchical goal-task network[J]. Fire Control & Command Control, 2020, 45 (2): 110- 114.
27	LI Y F , HUANG H J . Efficient task planning for heterogeneous AGVs in warehouses[J]. IEEE Trans.on Intelligent Transportation Systems, 2024, 25 (8): 100- 1019.
28	ZHANG K, LUCET E, SANDRETTO J A, et al. Task and motion planning methods: applications and limitations[C]//Proc. of the 19th International Conference on Informatics in Control, Automation and Robotics, 2022.
29	LIU J M , CHEN Y G , WANG R , et al. Complex task planning method of space-aeronautics cooperative observation based on multi-layer interaction[J]. Journal of Systems Engineering and Electronics, 2023, 34 (6): 1550- 1564.
30	曾斌, 樊旭, 李厚朴. 支持重规划的战时保障动态调度研究[J]. 自动化学报, 2023, 49 (7): 1519- 1529.
	ZENG B , FAN X , LI H P . Research of dynamic scheduling with re-planning for wartime logistics support[J]. Acta Automatica Sinica, 2023, 49 (7): 1519- 1529.

符号表示	含义
T	任务
ra_i	资源分配方案
za_i^k	任务执行方案
ra_i\|za_i^k	任务方案
s(T, ra_i\|za_i^k)	任务方案ra_i\|za_i^k对任务T的满足性
p	单个任务单元失效概率
d	任务方案切换平均时延
transfer(ra_i\|za_i^k, T)	任务T调整当前任务方案ra_i\|za_i^k

符号表示	含义
z_iⁿ	任务资源个体
Z_i={z_i¹, z_i², …, z_iⁿi}	同种资源类型个体构成资源集合Z_i
C_i^j={z_iⁿ}	Z_i按照个体差异划分为多个资源簇C_i^j
T={t₁, t₂, …, t_{h_t}}	任务T包括多个子任务
ra={(t_h, {C_i^j, n(t_h, C_i^j)})}	资源分配方案ra中子任务t_h需要资源簇C_i^j中n(t_h, C_i^j)个资源
C_i^j∈ra	资源分配方案ra中的资源簇C_i^j中存在个体参与执行任务
limit(t_h, Z_i)	子任务t_h对资源Z_i的能力要求
require(t_h, Z_i)	子任务t_h对资源Z_i的需求量
$\mathrm{za} \subset \mathrm{ra}$	可根据资源分配方案ra 生成执行单元分配方案za
za={(t_h, {z_i^t})}	执行单元分配方案中子任务t_h 由资源个体集合{z_i^t}执行
z_i^t∈za	执行单元分配方案za中的z_i^t用于执行任务
cap(C_i^j)	资源簇C_i^j的平均能力
cap(z_i^t)	资源个体z_i^t的平均能力
comb(ra_i)	组合资源分配方案ra_i下的任务执行方案
comb(T)	针对任务T, 组合资源分配方案及其对应任务执行方案
avail(z_i^t)	资源个体z_i^t的可用状态

集群	任务单元	聚合分类
b₁	b₁¹(0.9, 10), b₁²(0.9, 9), b₁³(0.8, 9), b₁⁴(0.7, 6), b₁⁵(0.6, 7), b₁⁶(0.6, 6), b₁⁷(0.6, 7), b₁⁸(0.4, 6), b₁⁹(0.5, 5), b₁¹⁰(0.6, 5)	{b₁¹, b₁², b₁³}(0.87, 9, 3), {b₁⁴, b₁⁵, b₁⁶, b₁⁷}(0.63, 6.5, 2), {b₁⁸, b₁⁹, b₁¹⁰}(0.5, 5.3, 1)
b₂	b₂¹(0.8, 6), b₂²(0.7, 7), b₂³(0.8, 7), b₂⁴(0.6, 7), b₂⁵(0.6, 5), b₂⁶(0.6, 6), b₂⁷(0.9, 8), b₂⁸(0.8, 9), b₂⁹(0.9, 9)	{b₂¹, b₂², b₂³}(0.77, 6.67, 4), {b₂⁴, b₂⁵, b₂⁶}(0.6, 6, 1), {b₂⁷, b₂⁸, b₂⁹}(0.87.8.67, 3)
b₃	b₃¹(0.6, 5), b₃²(0.6, 6), b₃³(0.6, 6), b₃⁴(0.5, 4), b₃⁵(0.5, 4), b₃⁶(0.5, 4), b₃⁷(0.4, 3), b₃⁸(0.4, 3)	{b₃¹, b₃², b₃³}(0.6, 5.67, 3), {b₃⁴, b₃⁵, b₃⁶}(0.5, 4, 4), {b₃⁷, b₃⁸}(0.4, 3, 1)
b₄	b₄¹(0.8, 7), b₄²(0.7, 8), b₄³(0.5, 6), b₄⁴(0.6, 5), b₄⁵(0.7, 6), b₄⁶(0.6, 7), b₄⁷(0.4, 4), b₄⁸(0.4, 5), b₄⁹(0.5, 4)	{b₄¹, b₄²}(0.75, 7.5, 2), {b₄³, b₄⁴}(0.55, 6.5, 2), {b₄⁵, b₄⁶}(0.65, 6.5, 4), {b₄⁷, b₄⁸, b₄⁹}(0.43, 4.33, 1)
b₅	b₅¹(0.6, 6), b₅²(0.7, 6), b₅³(0.6, 5), b₅⁴(0.5, 5), b₅⁵(0.6, 5), b₅⁶(0.8, 8), b₅⁷(0.8, 7)	{b₅¹, b₅²}(0.65, 6, 3), {b₅³, b₅⁴, b₅⁵}(0.57, 5, 1), {b₅⁶, b₅⁷}(0.8, 7.5, 4)

任务	t₁	t₂	t₃	t₄	t₅	t₆	t₇	t₈	t₉	t₁₀
资源需求量	1	2	5	1	4	3	3	1	3	2
单元资源能力要求	≥0.6	≥0.3	≥0.4	≥0.3	≥0.6	≥0.7	≥0.5	≥0.6	≥0.5	≥0.5

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[12]	马维宁, 胡起伟, 曹文斌, 贾希胜. 考虑维修任务分配的装备选择性维修决策优化[J]. 系统工程与电子技术, 2023, 45(6): 1902-1910.
[13]	文启翟, 康志宇, 卫国宁, 贺彦博, 吴斌, 周睿逸. 子母型航天器抵近观测任务流程规划方法研究[J]. 系统工程与电子技术, 2023, 45(12): 3941-3948.
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