考虑任务分配的无人机信息交互拓扑生成

薛莹; 何锋; 谷晓燕

doi:10.13700/j.bh.1001-5965.2021.0486

考虑任务分配的无人机信息交互拓扑生成

doi: 10.13700/j.bh.1001-5965.2021.0486

薛莹¹,
何锋^1, ,,
谷晓燕²

1.
北京航空航天大学电子信息工程学院，北京 100191
2.
北京信息科技大学信息管理学院，北京 100096

基金项目: 国家自然科学基金(62071023)

详细信息

通讯作者:
E-mail：robinleo@buaa.edu.cn

中图分类号: V243.1；N945.15
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- 文章访问数: 217
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- 被引次数: 0
出版历程
- 收稿日期: 2021-08-25
- 录用日期: 2021-11-26
- 网络出版日期: 2022-01-25
- 整期出版日期: 2023-07-31

UAV information interaction topology generation considering task allocation

XUE Ying¹,
HE Feng^{1
, ,},
GU Xiaoyan²

1.
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
2.
School of Information Management, Beijing Information Science and Technology University, Beijing 100096, China

Funds: National Natural Science Foundation of China (62071023)

More Information

Corresponding author: E-mail：robinleo@buaa.edu.cn

摘要

摘要:
无人机（UAV）持久编队信息交互拓扑的优化是保证UAV编队结构稳定性和任务执行时效性的重要基础。现有的编队生成算法针对距离因素进行权重赋值和拓扑生成，由于未考虑任务分配因素，可能会引起整体任务执行时间过长甚至任务失败的问题，对UAV的能量也造成了不必要的损耗。以任务消息传输时间和能量损耗为关键优化目标，在保证UAV编队结构稳定的前提下，提出考虑任务分配因素的信息交互拓扑生成算法，优先连接承载实时性要求较高通信任务的关键汇聚链路，对剩余链路通过引入惩罚项，在权重上进一步将任务消息传输量因素考虑在内，生成最终的信息交互拓扑。使用OMNet++进行仿真验证，相比于只考虑距离因素的信息交互拓扑生成算法，所提算法在20架UAVs编队场景下，消息传输时间方面最高降低57.3%，最低降低28.1%，关键任务消息的到达时延降低了45.2%~51.6%，而任务执行过程单UAV的能量损耗总体减少了17.5%，平均每个节点减少损耗16.1%。
- 无人机编队 /
- 任务分配 /
- 信息交互拓扑 /
- 刚性矩阵 /
- 最优持久图
Abstract:
The optimization of information interaction topology for persistent unmanned aerial vehicle (UAV) formation is an important basis for the stability of UAV formation structure and the timeliness of task execution. The existing formation generation algorithms assign weights and generate topologies based on the factor of distance rather than task allocation; therefore, the overall task execution may be too long or even fail, causing unnecessary loss of UAV energy. This study proposes an information interaction topology optimization algorithm considering the factor of task allocation, with the task message transmission time and energy loss being the key optimization objectives, and with the premise of ensuring the stability of UAV formation structure. The key aggregation links performing high real-time communication tasks are preferentially connected, and the penalty term is introduced for the remaining links. Furthermore, the weight factor of task message transmission is taken into account to generate the final information interaction topology. OMNet++ is used for simulation verification. Compared with the information interaction topology generation algorithm considering only the distance factor, the algorithm considering the task factor can reduce the message transmission time by 57.3% and 28.1% at the highest and lowest levels in the formation scenario of 20 UAVs, respectively. The arrival delay of mission-critical messages is reduced by 45.2% to 51.6%. During the mission execution, the energy loss of a single UAV is reduced by 17.5% overall, and the loss per node is reduced by 16.1% on average.
- UAV formation /
- task assignment /
- information interaction topology /
- rigid matrix /
- optimal persistence graph

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图 1 通信链路集合关系示意图

Figure 1. Schematic diagram of relationship between communication link sets

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图 2 同一组无人机的不同的最小刚性图

Figure 2. Different minimum rigidity diagrams of same group of UAVs

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图 3 任务分配与消息传递关系示意图

Figure 3. Relationship diagram between task allocation and message passing

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图 4 本文算法流程

Figure 4. Flow chart of proposed algorithm

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图 5 小规模案例使用2种算法生成的信息交互拓扑

Figure 5. Information interaction topology generated by two algorithms in small-scale cases

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图 6 小规模实验时2种算法生成的拓扑下部分无人机任务执行结果对比

Figure 6. Comparison of task execution results under two algorithms’ topologies in small-scale experiment

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图 7 2种算法下执行任务的能量消耗情况

Figure 7. Energy consumption of tasks under two algorithms

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图 8 复杂案例使用2种算法生成的信息交互拓扑图

Figure 8. Information interaction topology generated by two algorithms in complex cases

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图 9 2种算法生成的拓扑下部分无人机任务执行结果对比

Figure 9. Comparison of mission execution results of some UAVs under two algorithms’ topologies

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图 10 复杂场景下2种算法下执行任务的能量消耗情况

Figure 10. Energy consumption of tasks under two algorithms in complex experiment

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表 1 任务列表

Table 1. Task list

任务组	任务	时间/s	耗费资源	后继任务
目标侦测	TD1	20	3	TD3
	TD2	16	4	TD3
	TD3	20	8	TP1
搜索锁定	TT1	13	7	TT2
搜索锁定	TT2	20	10	TP1
综合导航	TN1	10	4	TN5
	TN2	17	4	TN5
	TN3	14	4	TN5
	TN4	13	5	TN5
	TN5	20	9
信息融合	TP1	20	3	TP3，TF1，TF2
	TP2	16	1	TP3，TF1，TF2
	TP3	20	6	TN5
火力攻击	TF1	14	10	TF3
	TF2	13	10	TF3
	TF3	18	10

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表 2 任务分配方案

Table 2. Task assignment scheme

编号	任务	对应无人机	后继任务
1	TD1	UAV1	TD3
2	TD2	UAV2	TD3
3	TD3	UAV1	TP1
4	TT1	UAV3	TT2
5	TT2	UAV4	TP1
6	TN1	UAV2	TN5
7	TN2	UAV3	TN5
8	TN3	UAV6	TN5
9	TN4	UAV5	TN5
10	TN5	UAV1
11	TP1	UAV8	TP3，TF1，TF2
12	TP2	UAV7	TP3，TF1，TF2
13	TP3	UAV8	TN5
14	TF1	UAV9	TF3
15	TF2	UAV10	TF3
16	TF3	UAV7

下载: 导出CSV

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