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Multi-Agent Planning Under Complex Constraints for Deep-Space Probes Group

机译：复杂约束下的深空探测器组多主体规划

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With the development of launch technology and the miniaturization of probes, multi-probe systems can be used in space missions, and the demand for on-board planning is rising. Multi-probe planning needs tasks to be allocated properly to increase the total task profit and satisfy the complex time and resource constraints of probes. We propose to solve this problem using a multi-agent system, taking advantage of its negotiation strategy in task allocation to increase the global task profit. And we add complex constraint handling ability to agents, to deal with the more realistic and detailed constraints models in practical engineering. Before planning starts, a dynamic distributed multi-probe group formation pattern is designed to cope with the unstable communication condition between probes. In the multi-agent system, two types of agents are designed: execution agents represent task accomplish abilities of probes and target agents represent target requirements in the task. A novel three-phase multi-agent negotiation planning method is proposed, target agents ask for high-profit actions and execution agents respond depend on constraints and optimal strategy. The last phase of planning satisfies resource constraints through an action-bind strategy, which can handle constraints between overlapped resource consumption and resource generation actions. The experimental results show that the proposed multi-agent planning method can obtain rational plans satisfying complex constraints and increases the task profit compared to the traditional greedy search method.
机译：随着发射技术的发展和探头的小型化，多探头系统可用于太空任务，并且对机载计划的需求正在增加。多探针计划需要正确分配任务，以增加总任务收益并满足探针的复杂时间和资源约束。我们建议使用多代理系统解决此问题，在任务分配中利用其协商策略来增加全局任务利润。并且我们为代理添加了复杂的约束处理能力，以处理实际工程中更现实，更详细的约束模型。在开始计划之前，设计了动态分布式多探针组形成模式来应对探针之间的不稳定通信条件。在多代理系统中，设计了两种类型的代理：执行代理代表探针的任务完成能力，目标代理代表任务中的目标需求。提出了一种新颖的三阶段多主体协商计划方法，目标主体提出高利润的行动，执行主体根据约束和最优策略做出响应。计划的最后阶段通过操作绑定策略满足资源约束，该策略可以处理重叠的资源消耗和资源生成操作之间的约束。实验结果表明，与传统的贪婪搜索方法相比，该方法能获得满足复杂约束条件的合理计划，增加了任务收益。

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