异构对地观测资源动态任务规划的自底向上机制和改进的合同网协议
Bottom-up mechanism and improved contract net protocol for the dynamic task planning of heterogeneous Earth observation resources
摘要：
地球观测资源在救灾、损害评估和相关领域中变得越来越不可或缺。观测任务需求的变化、恶劣天气的发生、资源故障等不可预测因素都可能导致预定观测方案的不可行。因此，至关重要的是，能够迅速，也许经常开发高质量的重新规划的观察计划，最大限度地减少对预定任务的影响。提出了一种自下而上的分布式协调框架和改进的合同网，以促进异构地球观测资源的动态任务再规划。该层次框架包括三个层次，即相邻资源协调，单规划中心协调，多规划中心协调。受不可预测因素影响的观察任务被分配和处理，从资源到规划中心的自下而上的路线。这种自底向上的分布式协调框架将部分计算负载转移到观测系统的各个节点，以更有效和鲁棒地分配任务。为支持动态环境下大规模任务对地观测资源的快速分配，提出了一种多轮组合分配方法。此外，提出了一种新的基于浮点区间的局部搜索算法，以更快地获得有希望的规划方案。实验结果表明，MCA方法能够在满足时间效率的情况下，获得更好的大规模任务完成率。仿真结果表明，该方法能够在动态环境下有效地获得基于原方案的重规划方案。
英文摘要：
Earth observation resources are becoming increasingly indispensable in disaster relief, damage assessment and related domains. Many unpredicted factors, such as the change of observation task requirements, to the occurring of bad weather and resource failures, may cause the scheduled observation scheme to become infeasible. Therefore, it is crucial to be able to promptly and maybe frequently develop high-quality replanned observation schemes that minimize the effects on the scheduled tasks. A bottom-up distributed coordinated framework together with an improved contract net are proposed to facilitate the dynamic task replanning for heterogeneous Earth observation resources. This hierarchical framework consists of three levels, namely, neighboring resource coordination, single planning center coordination, and multiple planning center coordination. Observation tasks affected by unpredicted factors are assigned and treated along with a bottom-up route from resources to planning centers. This bottom-up distributed coordinated framework transfers part of the computing load to various nodes of the observation systems to allocate tasks more efficiently and robustly. To support the prompt assignment of large-scale tasks to proper Earth observation resources in dynamic environments, we propose a multiround combinatorial allocation (MCA) method. Moreover, a new float interval-based local search algorithm is proposed to obtain the promising planning scheme more quickly. The experiments demonstrate that the MCA method can achieve a better task completion rate for large-scale tasks with satisfactory time efficiency. It also demonstrates that this method can help to efficiently obtain replanning schemes based on original scheme in dynamic environments.
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