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。本论文提出了新颖的ROBUST（距离观测者二分-单分时空）框架，为复杂时空域内观测者网络的建模、分析与优化提供了重要进展。ROBUST采用独特的二分-单分方法，区分观测者与可观测实体，同时纳入空间约束与时间动态。本研究通过引入新颖的基于图的度量（包括近视度、空间紧密度中心性及边长比例）拓展了时空网络理论。这些度量与先进聚类技术（如近端递归）相结合，为网络结构、韧性及观测者布设有效性提供了深入见解。与传统模型相比，ROBUST框架展现出更优的资源分配与战略响应能力。在海洋监测、城市安全网络及多智能体路径规划中的案例研究展示了其实际适用性与适应性。结果表明，该方法在覆盖范围、响应时间及整体网络效率方面均有显著提升。本工作为未来研究融入不完善知识、改进时序路径方法及拓展应用范围奠定了基础。通过连接理论进展与实际解决方案，ROBUST成为该领域的重要贡献，有望启发并推动网络优化与多智能体系统规划领域当前及未来的研究。