Dynamic collaborative planning method of earth observation resources based on contract network
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摘要:
卫星、无人机等对地观测资源已经成为执行灾害救援、灾损评估等多样化监测任务的主要观测手段，而大规模任务的随机调整和动态执行环境是快速制定对地观测方案的核心难点。针对此问题，提出一种面向不确定环境的对地观测资源动态协同规划方法，以动态高效地制定异构观测资源的协同观测方案。首先，结合合同网协议提出一种自下而上的分布式动态协同框架，以整合空天地异构观测资源构建分布式、动态、松耦合的协同观测网络。然后，根据该协同框架提出多轮组合分配方法及优化算法以快速动态地分配大规模监测任务。最后，通过仿真实验证明，在任务持续并发的动态不确定环境中，基于合同网的动态协同规划方法在提升了约25%任务完成率的同时，降低了约20%的运行时间，实现了任务完成率与方法运行时间的平衡。
Abstract:Satellite, UAV and other earth observation resources have become the main observation means to carry out various monitoring tasks, such as disaster rescue, disaster damage assessment, etc. Random adjustment and dynamic execution environment of large-scale tasks are the core problems to quickly develop earth observation programs. In view of the above problems, a dynamic collaborative planning method for earth observation resources in uncertain conditions is proposed to dynamically and efficiently develop the cooperative observation scheme of heterogeneous resources. First, a bottom-up distributed dynamic cooperative framework is proposed based on the contract network protocol to integrate the heterogeneous observation resources of air-space-ground and to build a distributed, dynamic and loosely coupled cooperative observation network. Then, based on the above collaborative framework, a multi-round combination allocation method and an optimization algorithm are proposed to allocate large-scale monitoring tasks rapidly and dynamically. Finally, the results of simulation experiment show that the dynamic collaborative planning method based on contract network improves the task completion rate by about 25% and reduces the collaborative planning time by about 20% in the dynamic uncertain conditions with continuous task concurrency, which achieves the balance between task completion efficiency and method running time.
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图 1 分布式动态协同框架示意图
Figure 1. Schematic diagram of distributed dynamic collaborative framework
图 3 自下而上的对地观测资源动态协同规划流程示意图
Figure 3. Down-top flowchart for dynamic collaborative planning of earth observation resources
图 4 协同规划方法对比实验结果
Figure 4. Comparative experimental results of collaborative planning method
表 1 重规划仿真实验任务完成率
Table 1. Re-planning simulation experimental task completion rate
重规划
次数任务
添加量任务
总量任务完成率/% DDCP MCP BCP AUS SSA 1 40 80 97.5 100 85.0 61.3 77.5 2 46 126 94.4 98.4 84.9 61.1 74.6 3 33 159 88.7 93.1 74.8 63.5 64.8 4 37 196 85.2 89.8 69.9 54.6 59.7 5 40 236 83.9 89.4 69.9 48.3 54.2 6 42 278 80.2 85.3 68.0 43.2 50.0 
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表 2 重规划仿真实验运行时间
Table 2. Re-planning simulation experimental task running time
重规划次数 运动时间/s DDCP MCP BCP AUS SSA 1 0.764 2.200 1.226 0.766 0.781 2 0.803 3.297 2.707 0.875 0.828 3 0.892 5.641 2.979 1.031 1.203 4 1.088 7.219 4.394 1.466 1.437 5 1.331 9.578 5.908 1.969 2.344 6 1.549 14.98 8.768 2.375 2.984
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表 3 重规划仿真实验方案改变率
Table 3. Re-planning simulation experimental scheme change rate
重规划
次数任务
增长率方案改变率/% DDCP MCP BCP AUS SSA 1 1 29.5 41.2 41.3 35.5 46.9 2 0.575 21.8 36.3 37.3 27.7 40.3 3 0.261 19.1 32.4 28.9 28.2 25.7 4 0.232 15.6 25.6 18.4 20.0 20.6 5 0.204 10.6 17.5 16.9 13.6 15.8 6 0.177 8.5 13.5 14.4 10.9 10.8
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