网络化约束下的大规模无人机编队柔性拓扑重构方法研究

Formation flying of multiple unmanned aerial vehicles (UAVs) has been identified as the key technology for the future war. However until now formations result from many of the current formation control strategies suffer from less flexible, lags in response and less robustness. Other factors such as communication latency in situations of sharp countermeasures cause the time-space inconformity to the formation as well. All these confines the usage of multiple UAVs organized in flexible formations. This project meant to solve the large-scale constrained optimization problems of flexible reconstruction of large-scale UAVs formation in network environments. Challenges involved in the optimization are dynamic evolutions along with the solving process, localized control, network status asynchronous and combinatorial explosion in state space. Therefore works will be done in the following three aspects: time-space conformity model of topology formation in network constrains; dynamic clustering and control strategies of flexible topology formation; flexible reconstruction methods for topological structures based on distributed self-organizing theme. Efforts will be taken to make breakthroughs to the following challenges: the time-space conformity coupling mechanism with space geometry and communication topology; flexible dynamic clustering and coordinate topology control principle; algorithms of topological optimization under distributed constrains in network. In the end, the project will figure out a flexible control strategy of large-scale UAVs formation in complex communication environments and a distributed self-organizing reconstruction algorithm for topology structures, this will make sense for improving the formation cooperative abilities of multiple UAVs in counter environments and offer theory support for proper usage of large formation of UAVs as well.

大规模无人机编队是未来网络战中无人机必然作战方式，然而目前刚性编队结构存在脆性强、应变能力低、鲁棒性差的问题，同时高对抗环境下通讯时延、中断等网络约束条件直接影响编队信息时空一致性，因此解决网络约束下的柔性拓扑控制已经成为无人机发展的迫切需求。本项目面向大规模无人机编队柔性拓扑重构分布式约束优化问题，针对求解时动态演化、控制局部化、网络状态异步、状态空间组合爆炸的难点，开展网络化约束下编队拓扑的时空一致性模型、大规模编队动态分群和分解拓扑控制机理、分布式自组织编队柔性拓扑重构实现方法三方面的研究，着力突破空间几何拓扑与通讯拓扑对于时空一致性的耦合机理模型、柔性拓扑动态分群和拓扑控制协调机制、网络约束下分布式约束拓扑优化算法三个关键科学问题，给出复杂通信环境下大规模无人机编队柔性拓扑控制及分布式自组织拓扑重构方法，提升高对抗环境下无人机自主编队协同能力，为大规模编队应用提供基础理论支持。

大规模无人机编队已经成为未来无人机应用的必然趋势，但还存在以下的问题：①密集刚性队形拓扑结构脆性强、应变能力低、鲁棒性差，明显不能应用于动态战场环境，而柔性拓扑重构问题缺乏系统性深入的研究；②高对抗环境下存在通讯时延等不稳定网络因素，直接影响到大规模编队信息时空一致性。研究解决网络约束条件下的大规模无人机柔性拓扑重构的新方法和相关理论对推动高自主能力无人机的广泛应用具有十分重要意义。.本项目面向大规模无人机编队柔性拓扑重构分布式约束优化问题，针对求解时动态演化、控制局部化、状态空间组合爆炸的难点，开展编队拓扑的时空一致性模型、大规模编队动态分群和分解拓扑控制机理、分布式自组织编队柔性拓扑重构实现方法三方面的研究，着力突破空间几何拓扑与通讯拓扑对于时空一致性的耦合机理模型、柔性拓扑动态分群和拓扑控制协调机制、网络约束下分布式约束拓扑优化算法三个关键科学问题，给出复杂通信环境下大规模无人机编队柔性拓扑控制及分布式自组织拓扑重构方法。本项目为提升高对抗环境下无人机自主编队协同能力，为大规模编队应用提供基础理论支持。.在本项目支持下，目前已在国内外高水平期刊和会议上发表录用论文10篇，专利1个，培养硕士研究生3名。