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developed to allow robots to share parts of their world models with their peers when their models were incomplete or outdated.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.

Coordinated or synchronized tasks can always be found in various manufacturing processes, e.g., machining along spatial trajectories, coordinated operations of multi-manipulators, and vacuum pumps, etc. The vacuum pump is a typical device with synchronized motion among those examples. The vacuum pump has played an important role in current semiconductor manufacturing processes. Its pumping feature is achieved by synchronized motion of two mating pump rotors. A common approach to accomplish the synchronized motion is by idle gears. Nevertheless, this design cannot meet serious requirements of vacuum systems demanded by growing manufacturing techniques. In order to provide a complete and proper control strategy for synchronized motion, and to overthrow traditional architecture of vacuum pumps by raising a better control scheme for new generation oil-less products, the paper focuses on synchronized motion control for dual motors. The first objective of here is to develop a control method for synchronized motion of two separated motors. Both system uncertainties and unknown disturbances occurring in actual implementation need to be carefully considered. An experimental setup will also be established for examinations and verifications. And then synchronized motion control of dual motors including two mating screw rotors then will be investigated. During this period, the emphasis will be on solution finding for unexpected contact collision between two rotors. An effective and efficient control strategy will be developed for synchronized motion control of dual motors. Longer operation time and better synchronization performance for two motors can therefore be anticipated.

Abstract Coordinated or synchronized tasks can always be found in various manufacturing processes, e.g., machining along spatial trajectories, coordinated operations of multi-manipulators, and vacuum pumps, etc. The vacuum pump is a typical device with synchronized motion among those examples. The vacuum pump has played an important role in current semiconductor manufacturing processes. Its pumping feature is achieved by synchronized motion of two mating pump rotors. A common approach to accomplish the synchronized motion is by idle gears. Nevertheless, this design cannot meet serious requirements of vacuum systems demanded by growing manufacturing techniques. In order to provide a complete and proper control strategy for synchronized motion, and to overthrow traditional architecture of vacuum pumps by raising a better control scheme for new generation oil-less products, the paper focuses on synchronized motion control for dual motors. The first objective of here is to develop a control method for synchronized motion of two separated motors. Both system uncertainties and unknown disturbances occurring in actual implementation need to be carefully considered. An experimental setup will also be established for examinations and verifications. And then synchronized motion control of dual motors including two mating screw rotors then will be investigated. During this period, the emphasis will be on solution finding for unexpected contact collision between two rotors. An effective and efficient control strategy will be developed for synchronized motion control of dual motors. Longer operation time and better synchronization performance for two motors can therefore be anticipated.

Dynamic security limits the power transfer capacity between regions and therefore has an economic impact. The power modulation control of high-voltage direct current (HVDC) links can improve the dynamic security of the power system. Having several HVDC links in a system creates the opportunity to coordinate such control, and coordination also ensures that negative interactions do not occur among the controllable devices. This thesis aims to increase dynamic security by coordinating HVDC links, as an alternative to decreasing the transfer capacity. This thesis contributes four control approaches for increasing the dynamic stability, based on feedforward control, adaptive control, optimal control, and exact-feedback linearization control. Depending on the available measurements, dynamic system model, and system topology, one of the developed methods can be applied. The wide-area measurement system provides the central controller with real-time data and sends control signals to the HVDC links. The feedforward controller applies rapid power dispatch, and the strategy used here is to link the N-1 criterion between two systems. The adaptive controller uses the modal analysis approach; based on forecasted load paths, the controller gains are adaptively adjusted to maximize the damping in the system. The optimal controller is designed based on an estimated reduced-order model; system identification develops the model based on the system response. The exact-feedback linearization approach uses a pre-feedback loop to cancel the nonlinearities; a stabilizing controller is designed for the remaining linear system. The conclusion is that coordinating the HVDC links improves the dynamic stability, which makes it possible to increase the transfer capacity. This conclusion is also supported by simulations of each control approach. / QC 20110302

This thesis addresses the improvement of cooperative task allocation to vehicles in multiple-vehicle, multiple-target scenarios through the use of more effective preplanned tours. Effective allocation of vehicles to targets requires knowledge of both the team objectives and the contributions that individual vehicles can make toward accomplishing team goals. This is primarily an issue of individual vehicle path planning --- determining the path the vehicles will follow to accomplish individual and team goals. Conventional methods plan optimal point-to-point path segments that often result in lengthy and suboptimal tours because the trajectory neither considers future tasks nor the overall path. However, cooperation between agents is improved when the team selects vehicle assignments based on the ability to complete immediate and subsequent tasks. This research demonstrates that planning more efficient tour paths through multiple targets results in better use of individual vehicle resources, faster completion of team objectives, and improved overall cooperation between agents. This research presents a method of assigning unmanned aerial vehicles to targets to improve cooperation. A tour path planning method was developed to overcome shortcomings of traditional point-to-point path planners, and is extended to the specific tour-planning needs of this problem. The planner utilizes an on-line learning heuristic search to find paths that accomplish team goals in the shortest flight time. The learning search planner uses the entire sensor footprint, more efficiently plans tours through closely packed targets, and learns the best order for completion of the multiple tasks. The improved planner results in assignment completion times that range on average between 1.67 and 2.41 times faster, depending on target spread. Assignments created from preplanned tours make better use of vehicle resources and improve team cooperation. Path planning and assignment selection are accomplished in near real-time through the use of path heuristics and assignment cost estimates to reduce the problem size to tractable levels. Assignments are ordered according to estimated or predicted value. A reduced number of ordered assignments is considered and evaluated to control problem growth. The estimates adequately represent the actual assignment value, effectively reduce problem size, and produce near-optimal paths and assignments for near-real-time applications.

Both the solar photovoltaic (PV) installation and electric vehicles (EVs) deployment are increasing significantly in Sweden. With the large-scale integration of PV and EVs, problems such as the voltage deviations and overloading of components can arise, since the existing distribution grids are not designed to host the large shares of new EV loads and the intermittent PV power feed-in. This thesis investigates a C2V2C (i.e., Community to EV to Community) energy flow concept and evaluates how it can improve the power balance performances in communities with both PV and EV integrated in Sweden. Community refers to a group of buildings (i.e., two or more) connected within the same microgrid. It aims to develop a C2V2C model, which utilizes smart charging of electric vehicles to deliver electricity between different communities, for improving the performances at multiple-community-level. A coordinated control of EV smart charging is developed using the genetic algorithm, and its performance is compared with an existing individual control. Two control strategies are considered: (i) minimizing the peak energy exchanges with the grid and (ii) minimizing the electricity costs. Case studies are conducted considering a residential community and workplace community, as well as one EV commuting between them. The study results show that the advanced control achieves a cost reduction of up to 280 % in a summer week compared to the individual control. In a winter week, a performance improvement of up to 13 % can be achieved using advanced control. The advanced control can also reduce the energy exchange peaks with the power grid of the multiple communities. This study has proven the effectiveness of the C2V2C model in enhancing the local power balance at multiple-community-level. It will enhance the resilience and grid-friendliness of building communities, thus paving way for the large PV and EV penetration in the future.

A smart grid cost/benefit analysis answers a series of economic questions that address the incremental benefits of each stage or decision point. Each stage of the economic analysis provides information about the incremental benefits of that stage with respect to the previous stage. With this approach stages that provide little or no economic benefits can be identified. In this study there are series of applications,-including quasi-steady state power flows over time-varying loads and costs of service, Monte Carlo simulations, reconfiguration for restoration, and coordinated control - that are used to evaluate the cost-benefits of a series of smart grid investments. In the electric power system planning process, engineers seek to identify the most cost-effective means of serving the load within reliability and power quality criteria. In order to accurately assess the cost of a given project, the feeder losses must be calculated. In the past, the feeder losses were estimated based upon the peak load and a calculated load factor for the year. The cost of these losses would then be calculated based upon an expected, fixed per-kWh generation cost. This dissertation presents a more accurate means of calculating the cost of losses, using hourly feeder load information and time-varying electric energy cost data. The work here attempts to quantify the improvement in high accuracy and presents an example where the economic evaluation of a planning project requires the more accurate loss calculation. Smart grid investments can also affect response to equipment failures where there are two types of responses to consider -blue-sky day and storm. Storm response and power restoration can be very expensive for electric utilities. The deployment of automated switches can benefit the utility by decreasing storm restoration hours. The automated switches also improve system reliably by decreasing customer interruption duration. In this dissertation a Monte Carlo simulation is used to mimic storm equipment failure events, followed by reconfiguration for restoration and power flow evaluations. The Monte Carlo simulation is driven by actual storm statistics taken from 89 different storms, where equipment failure rates are time varying. The customer outage status and durations are examined. Changes in reliability for the system with and without automated switching devices are investigated. Time varying coordinated control of Conservation Voltage Reduction (CVR) is implemented. The coordinated control runs in the control center and makes use of measurements from throughout the system to determine control settings that move the system toward optimum performance as the load varies. The coordinated control provides set points to local controllers. A major difference between the coordinated control and local control is the set points provided by the coordinated control are time varying. Reduction of energy and losses of coordinated control are compared with local control. Also eliminating low voltage problems with coordinated control are addressed. An overall economic study is implemented in the final stage of the work. A series of five evaluations of the economic benefits of smart grid automation investments are investigated. Here benefits that can be quantified in terms of dollar savings are considered here referred to as "hard dollar" benefits. Smart Grid investment evaluations to be considered include investments in improved efficiency, more cost effective use of existing system capacity with automated switches, and coordinated control of capacitor banks and voltage regulators. These Smart Grid evaluations are sequentially ordered, resulting in a series of incremental hard dollar benefits. Hard dollar benefits come from improved efficiency, delaying large capital equipment investments, shortened storm restoration times, and reduced customer energy use. The evaluation shows that when time varying loads are considered in the design, investments in automation can improve performance and significantly lower costs resulting in "hard dollar" savings. / Ph. D.

Aerobiological sampling using unmanned aerial vehicles (UAVs) is an exciting research field blending various scientific and engineering disciplines. The biological data collected using UAVs helps to better understand the atmospheric transport of microorganisms. Autopilot-equipped UAVs can accurately sample along pre-defined flight plans and precisely regulated altitudes. They can provide even greater utility when they are networked together in coordinated sampling missions: such measurements can yield further information about the aerial transport process. In this work flight vehicle path planning, control and coordination strategies are considered for unmanned autonomous aerial vehicles. A time-optimal path planning algorithm, that is simple enough to be solved in real time, is derived based on geometric concepts. The method yields closed-form solution for an important subset of candidate extremal paths; the rest of the paths are found using a simple numerical root-finding algorithm. A multi-UAV coordination framework is applied to a specific control-volume sampling problem that supports aerobiological data-collection efforts conducted in the lower atmosphere. The work is part of a larger effort that focuses on the validation of atmospheric dispersion models developed to predict the spread of plant diseases in the lower atmosphere. The developed concepts and methods are demonstrated by field experiments focusing on the spread of the plant pathogen <i>Phytophthora infestans</i>. / Ph. D.

Electric vehicles, which have both energy storage capability and mobility capability, can provide a new solution for electricity sharing between different building communities (i.e., a group of buildings connected with a microgrid). This comes to the community-to-community (C2C) energy sharing network. The C2C energy sharing networks have the potential to not only minimize the effects of electric vehicle integration into the energy grid, but also improve the electricity grid efficiency as a whole. In this thesis, a coordinated smart charging method of electric vehicles (EVs) is proposed for the C2C model. The proposed method considers the power regulation needs in both the present parking community and the next destination community. Then, based on the needs of both communities, the control method will decide the optimal amount of electricity that can be delivered by EV, so that the energy performances in both communities can be the best. The developed coordinated control has been compared with a base case (without any smart charging) and an uncoordinated control case under two control strategies: minimizing the peak energy exchanges with the grid and maximizing the renewable self-utilization. The genetic algorithm tools in MATLAB software are used for the optimization of the model. Meanwhile, to test the robustness of this C2C model, different combinations of building communities have been studied, namely residential-workplace, residential-university, and residential-workshop communities. The case study reveals that the C2C model is effective in improving energy performance under both control strategies. Peak reduction control strategies work most effectively for smaller systems with lower electricity demand and production. With C2C energy sharing, the annual mean peak reduction ranged from 39 % at the smallest community and 20 % at the largest community. Self-consumption maximization strategies work best for systems with a larger surplus of electricity production. With C2C energy sharing, the annual self-consumption increase ranged from 50 % at the community with the largest production surplus, to 7 % at the community with the smallest production surplus. The residential-workshop community studied in this thesis benefited the most from C2C charging control due to its production surplus and the relatively low electricity demands of the communities.

This dissertation discusses vision-based static obstacle avoidance for a fleet of nonholonomic robots tasked to arrive at a final destination simultaneously. Path generation for each vehicle is computed using a single polynomial function that incorporates the vehicle constraints on velocity and acceleration and satisfies boundary conditions by construction. Furthermore, the arrival criterion and a preliminary obstacle avoidance scheme is incorporated into the path generation. Each robot is equipped with an inertial measurement unit that provides measurements of the vehicle's position and velocity, and a monocular camera that detects obstacles. The obstacle avoidance algorithm deforms the vehicle's original path around at most one obstacle per vehicle in a direction that minimizes an obstacle avoidance potential function. Deconfliction of the vehicles during obstacle avoidance is achieved by imposing a separation condition at the path generation level. Two estimation schemes are applied to estimate the unknown obstacle parameters. The first is an existing method known in the literature as Identifier-Based Observer and the second is a recently-developed fast estimator. It is shown that the performance of the fast estimator and its effect on the obstacle avoidance algorithm can be arbitrarily improved by the appropriate choice of parameters as compared to the Identifier-Based Observer method. Coordination in time of all vehicles is completed in an outer loop which adjusts the desired velocity profile of each vehicle in order to meet the simultaneous arrival constraints. Simulation results illustrate the theoretical findings. / Ph. D.