The introduction of electric autonomous Unmanned Arial Vehicles (UAVs) in cities is considered the ultimate disruptive sustainable technological solution due to the promised speed, affordability, and significant greenhouse gas (GHG) emission reductions. The integration of UAVs into the future smart city fabric offers a wide range of applications. In particular, UAVs are ideal for last-mile operation, which is expected to reduce delivery costs, GHG emissions, and delivery time compared to light trucks and other traditional delivery methods. As UAVs operate in the city airspace, and with the current generation of older cities, several technological challenges arise with the anticipated proliferation of heterogeneous UAV fleets in low-altitude airspace of dense urban areas. Being a fairly new disruptive technology with no real-world operation data, the literature only considers a few of the system design parameters and often disregards the impact of other essential parameters such as Kinematics and airspace policies. This leads to significant uncertainty in the estimated UAV energy consumption, ranges, and emissions yielding inaccurate conclusions regarding the full system design predilections. Therefore, an effective UAV system design should strive to understand the broad spectrum of parameters’ impacts to optimize the integration and operation. Towards that end, this research aims at investigating the different UAV system design parameters and their intertwined impacts on operation efficiency to obtain accurate system optimization results. The research utilized several datasets for the delivery demand and digital-twin city model data of Toronto, Ontario, Canada. The research employed a state-of-the-art flexible energy use model for UAVs calibrated to experimental measurements to generate a minimum-energy trajectory along with several proposed novel airspace discretization, trajectory optimization, and charging infrastructure allocation optimization models. In this respect, this dissertation quantified the impact of airspace policies, discretization, and trajectory generation on the energy consumption of UAVs. Furthermore, it unveiled the operation uncertainties and their implications on the cost, emissions, and allocated charging infrastructure demand. Unlike the UAV literature, our research included all system design parameters and their impact on the performance metrics. The dissertation also proposes a novel combined airspace discretization and trajectory generation algorithm for optimal UAV energy consumption, airspace capacity maximization, airspace traffic control, and off-grid solar charging station allocation. For instance, it is found that UAV deployment with carefully tailored airspace policies in delivery could reduce GHG emissions in the freight sector by up to 35% compared to EVs. Furthermore, the research highlighted how building integrated photovoltaic BIPV upgrades with associated buildings can eliminate GHG emissions and significantly reduce the decarbonization price through associated savings and excess generated electricity. Overall, this research presents a unique contribution to the knowledge of UAV research for practitioners, policymakers, and academia. / Thesis / Doctor of Philosophy (PhD)

Multi-robot systems (MRS) offer many performance benefits over single robots for tasks that can be completed by one robot. They offer potential redundancies to the system to improve robustness and allow tasks to be completed in parallel. These benefits, however, can be quickly offset by losses in productivity from diminishing returns caused by interference between robots and communication problems. This dissertation developed and evaluated MRS control architectures to solve the dynamic multi-robot autonomous routing problem. Dynamic multi-robot autonomous routing requires robots to complete a trip from their initial location at the time of task allocation to an assigned destination. The primary concern for the control architectures was how well the communication requirements and overall system performance scaled as the number of robots in the MRS got larger. The primary metrics for evaluation of the controller were the effective robot usage rate and the bandwidth usage. This dissertation evaluated several different approaches to solving dynamic multi-robot autonomous routing. The first three methods were based off of common MRS coordination approaches from previous research. These three control architectures with distributed control without communication (a swarm-like method), distributed control with communication, and centralized control. An additional architecture was developed to solve the problem in a way that scales better as the number of robots increase. This architecture, mixed mode autonomy, combines the strengths of distributed control with communication and centralized control. Like distributed control with communication, mixed mode autonomy's performance degrades gracefully with communication failures and is not dependent on a single controller. Like centralized control, there is oversight from a central controller to ensure repeatable high performance of the system. Each of the controllers other than distributed control without communication is based on building world models to facilitate coordination of the routes. A second variant of mixed mode autonomy was developed to allow robots to share parts of their world models with their peers when their models were incomplete or outdated. The system performance was evaluated for three example applications that represent different cases of dynamic multi-robot autonomous routing. These example applications were the automation of open pit mines, container terminals, and warehouses. The effective robot usage rates for mixed mode autonomy were generally significantly higher than the other controllers with a higher numbers of robots. The bandwidth usage was also much lower. These performance trends were also observed across a wide range of operating conditions for dynamic multi-robot autonomous routing. The original contributions from this work were the development of a new MRS control architecture, development of system model for the dynamic multi-robot autonomous routing problem, and identification of the tradeoffs for MRS design for the dynamic multi-robot autonomous routing problem. / Ph. D.