Existing network analysis methods struggle to optimize observer placements in dynamic environments with limited visibility. This dissertation introduces the novel ROBUST (Ranged Observer Bipartite-Unipartite SpatioTemporal) framework, offering a significant advancement in modeling, analyzing, and optimizing observer networks within complex spatiotemporal domains. ROBUST leverages a unique bipartite-unipartite approach, distinguishing between observer and observable entities while incorporating spatial constraints and temporal dynamics. This research extends spatiotemporal network theory by introducing novel graph-based measures, including myopic degree, spatial closeness centrality, and edge length proportion. These measures, coupled with advanced clustering techniques like Proximal Recurrence, provide insights into network structure, resilience, and the effectiveness of observer placements. The ROBUST framework demonstrates superior resource allocation and strategic responsiveness compared to conventional models. Case studies in oceanographic monitoring, urban safety networks, and multi-agent path planning showcases its practical applicability and adaptability. Results demonstrate significant improvements in coverage, response times, and overall network efficiency. This work paves the way for future research in incorporating imperfect knowledge, refining temporal pathing methodologies, and expanding the scope of applications. By bridging theoretical advancements with practical solutions, ROBUST stands as a significant contribution to the field, promising to inform and inspire ongoing and future endeavors in network optimization and multi-agent system planning.

翻译：现有网络分析方法难以在能见度受限的动态环境中优化观测者布设。本论文提出新颖的ROBUST（基于距离观测者的二分-单分时空）框架，为复杂时空域内观测网络的建模、分析与优化提供了重要进展。ROBUST采用独特的二分-单分建模方法，在纳入空间约束与时间动态的同时区分观测者与被观测实体。本研究通过引入包括近视度、空间紧密中心性与边长比例在内的新型图论度量，拓展了时空网络理论。这些度量与近端递归等先进聚类技术相结合，能够深入揭示网络结构、鲁棒性及观测者布设的有效性。与传统模型相比，ROBUST框架展现出更优越的资源分配能力与战略响应性。在海洋监测、城市安全网络及多智能体路径规划等案例研究中，该框架体现了实际适用性与适应性。实验结果表明其在覆盖率、响应时间及整体网络效率方面均有显著提升。本工作为未来研究指明了方向，包括纳入非完备知识、优化时序路径规划方法以及拓展应用范畴。通过连接理论进展与实践解决方案，ROBUST作为该领域的重要贡献，有望为网络优化与多智能体系统规划的当前及未来研究提供启示与借鉴。