No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

Multi-agent planning and control is an active and increasingly studied topic of research, with many practical applications, such as rescue missions, security, surveillance, and transportation. More specifically, cases that involve complex manipulator-endowed systems  deserve extra attention due to potential complex cooperative manipulation tasks and their interaction with the environment. This thesis addresses the problem of cooperative motion- and task-planning of multi-agent and multi-agent-object systems under complex specifications expressed as temporal logic formulas. We consider manipulator-endowed robotic agents that can coordinate in order to perform, among other tasks, cooperative object manipulation/transportation. Our approach is based on the integration of tools from the following areas: multi-agent systems, cooperative object manipulation, discrete abstraction design of multi-agent-object systems, and formal verification. More specifically, we divide the main problem into three different parts.The first part is devoted to the control design for the formation control of a team of rigid-bodies, motivated by its application to cooperative manipulation schemes. We propose decentralized control protocols such that desired position and orientation-based formation between neighboring agents is achieved. Moreover, inter-agent collisions and connectivity breaks are guaranteed to be avoided. In the second part, we design continuous control laws explicitly for the cooperative manipulation/transportation of an object by a team of robotic agents. Firstly, we propose robust decentralized controllers for the trajectory tracking of the object's center of mass.  Secondly, we design model predictive control-based controllers for the transportation of the object with collision and singularity constraints. In the third part, we design discrete representations of multi-agent continuous systems and synthesize hybrid controllers for the satisfaction of complex tasks expressed as temporal logic formulas. We achieve this by combining the results of the previous parts and by proposing appropriate trajectory tracking- and potential field-based continuous control laws for the transitions of the agents among the discrete states. We consider teams of unmanned aerial vehicles and mobile manipulators as well as multi-agent-object systems where the specifications of the objects are also taken into account.Numerical simulations and experimental results verify the claimed results. / <p>QC 20180219</p>

Dynamical systems that provably satisfy given specifications have become increasingly important in many engineering areas. For instance, safety-critical systems such as human-robot networks or autonomous driving systems are required to be safe and to also satisfy some complex specifications that may include timing constraints, i.e., when or in which order some tasks should be accomplished. Temporal logics have recently proven to be a valuable tool for these control systems by providing a rich specification language. Existing temporal logic-based control approaches discretize the underlying dynamical system in space and/or time, which is commonly referred to as the abstraction process. In other words, the continuous dynamical system is abstracted into a finite system representation, e.g., into a finite state automaton. Such approaches may lead to high computational burdens due to the curse of dimensionality, which makes it hard to use them in practice. Especially with respect to multi-agent systems, these methods do not scale computationally when the number of agents increases. We will address this open research question by deriving abstraction-free control methods for single- and multi-agent systems under signal temporal logic tasks. Another aim of this research is to consider robustness, which is partly taken care of by the robust semantics admitted by signal temporal logic as well as by the robustness properties of the derived control methods. In this work, we propose computationally-efficient frameworks that deal with the aforementioned problems for single- and multi-agent systems by using feedback control strategies such as optimization-based techniques, prescribed performance control, and control barrier functions in combination with hybrid systems theory that allows us to model some higher level decision-making. In each of these approaches, the temporal properties of the employed control methods are used to impose a temporal behavior on the closed-loop system dynamics, which eventually results in the satisfaction of the signal temporal logic task. With respect to the multi-agent case, we consider a bottom-up approach where each agent is subject to a local (individual) task. These tasks may depend on the behavior of other agents. Hence, the multi-agent system is subject to couplings induced on the task level as well as on the dynamical level. The main challenge then is to deal with these couplings and derive control methods that can still satisfy the given tasks or alternatively result in least violating solutions. The efficacy of the theoretical findings is demonstrated in simulations of single- and multi-agent systems under complex specifications. / <p>QC 20180502</p>

This dissertation consists of two chapters. Chapter 1 focuses on the barriers that diversifying companies could face and explore how barriers to entry differ across different types of entry. Chapter 2 turns the attention to the market competition among insurance companies that are already in a market and examines how product bundling impact insurers' market power. Chapter 1 proposes and estimates a multi-agent model of entry. The prior literature often treats the number of companies in a market as an exogenous measure of market structure. However, the number of companies is endogenously decided by the market structure and other participants. Thus, I propose a structural model of entry to address the endogenous entry decision. In addition, the estimations are conducted at each market-year level, therefore, it provides an opportunity to delineate the relative importance of barriers to entry across three dimensions: geographic, product, and time. I find that barriers to entry exist in the financial services industry, and can be quite substantial to the \textit{de novo} entrants. Overall, I find \textit{de novo} entrants are the ones most subject to barriers to entry across all markets. Expanding within a state is as costly as expanding within a product line. Upon further examination, I discover that product-specific knowledge, such as underwriting expertise, pricing schemes, and coverage designs, plays a critical role in a successful expansion. This information is also relatively more important than state-specific connections, such as how well the company knows its customers and connections with distribution channels. Among all product lines, I find that expertise in mortgage guaranty insurance creates the most barriers, and these barriers are most subject to impacts of the financial crisis. In Chapter 2, I turn the focus to the market competition \emph{within} a market and explore the impact of product bundling on market power. Product bundling is a popular way for companies to retain their customers and keep up with fast-changing market demand. In this chapter, I will specifically examine the impact of bundling on price elasticity for personal lines of insurance. Insurance demand estimation is well-explored in the literature because it is difficult to obtain individual-level data. I overcome this hurdle by using a random coefficients logit model, which incorporates flexible consumer preferences over companies' characteristics. The second difficulty in insurance demand estimation is that it is hard to find a good instrument for the endogenous price. Therefore, I propose a novel instrument, which exploits an idiosyncrasy in insurance tax laws for identification. I find that bundling, on average, can reduce consumers' price sensitivity. Thus, companies that can offer bundle-able products experience a less elastic demand and achieve market power. However, product bundling has differential impacts on the auto insurance and homeowners' insurance markets. Auto insurers that offer bundled packages experience less elastic demand in response to price increases. However, we do not observe similar patterns in the homeowners' insurance market, where doing so intensifies price elasticity. With a closer examination, we discover that the different valuation in homeowners is not driven by the financial ratings of insurers. This indicates that homeowners tend to value other characteristics, such as claims management and the quality of service, more than just price of the contract. / Business Administration/Risk Management and Insurance