<p dir="ltr">In this dissertation, we explore the design and control of complex advance systems at various scales with a focus on UAVs in urban environment. In Chapter 2, we introduce a standard for defining routing restrictions based on potential field and propose an air traffic management infrastructure that takes advantage of it. The infrastructure would facilitate the deployment of a collaborative platform, enabling independent parties involved in aerial travel to operate within the same airspace. Chapter 3 introduces a path planning framework tailored for restrictive routing within potential fields. At its core, it decomposes a potential field into multi-scale cells with an estimated risk for restriction violation and utilizes graph-based path planning algorithms to generate routes that are demonstratively safe. Chapter 4 formalizes a new class of neural controllers and an accompanying architecture that use Pontryagin's maximum/minimum principle to generate future state prediction of a system, and the corresponding optimal control needed to drive it to variable online reference state. The proposed model allows for adjustment of the transient characteristics of the system and control. The work sets the stage for the development of advanced flight algorithms for UAVs.</p>

<p dir="ltr">The traditional architectural divide between Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) systems has historically led organizations to rely heavily on Extract-Transform-Load (ETL) pipelines to bridge transactional databases with analytical data warehouses. While effective for batch analytics, this decoupled architecture introduces latency, reduces data freshness, and increases operational complexity, especially for modern applications that demand real-time insights.</p><p dir="ltr">Hybrid Transactional/Analytical Processing (HTAP) database systems aim to unify OLTP and OLAP capabilities within a single platform, potentially eliminating the need for dedicated ETL pipelines. This study presents a comprehensive comparative analysis of OLTP (MySQL with row-based storage), OLAP (Amazon Redshift with column-based storage), and HTAP (TiDB with hybrid storage) systems. Industry-standard benchmarks, including TPC-H for mixed workloads and the Star Schema Benchmark (SSB) for analytical workloads, are used to evaluate performance across various schema designs (star vs. flat), scale factors (SF=5, SF=10, SF=20), and cluster sizes. Key evaluation metrics include query execution time, mean latency, throughput, data freshness rate, and scalability.</p><p dir="ltr">The findings reveal that HTAP systems such as TiDB not only match or exceed the analytical performance of dedicated OLAP systems but also provide real-time data availability by eliminating traditional ETL delays. Moreover, TiDB's hybrid storage model and distributed architecture enable efficient handling of complex workloads, offering dynamic query optimization and strong consistency guarantees.</p><p dir="ltr">Based on these insights, this study argues that HTAP systems represent a viable, unified alternative to ETL-dependent architectures. It offers recommendations for their adoption in enterprise environments seeking to simplify infrastructure, reduce latency, and improve responsiveness to real-time data.</p>

<p dir="ltr">Autonomous robots can perform dangerous and tedious tasks, eliminating the need for human involvement. To deploy an autonomous robot in the field, a typical planning and control hierarchy is used, consisting of a high-level planner, a mid-level motion planner, and a low-level tracking controller. In applications such as simultaneous localization and mapping, package delivery, logistics, and surveillance, a group of autonomous robots can be more efficient and resilient than a single robot. However, deploying a multi-robot team by directly aggregating each robot's planning hierarchy into a larger, centralized hierarchy faces challenges related to scalability, resilience, and real-time computation. Distributed algorithms offer a promising solution for introducing effective coordination within a network of robots, addressing these issues. This thesis explores the application of distributed algorithms in multi-robot systems, focusing on several essential components required to enable distributed multi-robot coordination, both in general terms and for specific applications.</p>