Nežinomo ploto dengimas yra aktuali ir paplitusi problema. NPD sprendimas realiuose robotuose susiduria su daviklių ir mechanizmų netikslumu. Atliktame darbe yra pateiktas „Bouncing“ NPD algoritmo sprendimas robotui, turinčiam mažo tikslumo daviklius ir neprecizinius valdiklius. Taip pat atliktas darbas parodė sudėtingus roboto kūrimo aspektus ir galimus sprendimus. Sukurtas robotas dėl pigumo ir nesudėtingos realizacijos gali būti naudojamas kaip platforma kitokių algoritmų tyrimui. / The problem of unknown area coverage with mobile robots has received considerable attention over the past years. This problem is a common challenge in many applications, including automatic lawn mowing and vacuum cleaning. However, most of the approaches find difficult to implement in real life because of problems of environment data reading. In this paper we consider the problem of robust area covering algorithm implementation in mobile robot. The chosen approach is based on simple and robust algorithm for uncertain environment and simple robot platform. The results showed robustness, reliability of chosen method of control. The constructed robot has shown simplicity, cheapness of creation and possibility for different algorithm testing. The significance of this paper lies in the practical solution for robust mobile robot area coverage, suitable for noisy environment and low precisions robot sensors.

An area coverage control law in cooperation with reinforcement learning techniques is proposed for deploying multiple autonomous agents in a two-dimensional planar area. A scalar field characterizes the risk density in the area to be covered yielding nonuniform distribution of agents while providing optimal coverage. This problem has traditionally been addressed in the literature to date using locational optimization and gradient descent techniques, as well as proportional and proportional-derivative controllers. In most cases, agents' actuator energy required to drive them in optimal configurations in the workspace is not considered. Here the maximum coverage is achieved with minimum actuator energy required by each agent. Similar to existing coverage control techniques, the proposed algorithm takes into consideration time-varying risk density. These density functions represent the probability of an event occurring (e.g., the presence of an intruding target) at a certain location or point in the workspace indicating where the agents should be located. To this end, a coverage control algorithm using reinforcement learning that moves the team of mobile agents so as to provide optimal coverage given the density functions as they evolve over time is being proposed. Area coverage is modeled using Centroidal Voronoi Tessellation (CVT) governed by agents. Based on [1,2] and [3], the application of Centroidal Voronoi tessellation is extended to a dynamic changing harbour-like environment. The proposed multi-agent area coverage control law in conjunction with reinforcement learning techniques is implemented in a distributed manner whereby the multi-agent team only need to access information from adjacent agents while simultaneously providing dynamic target surveillance for single and multiple targets and feedback control of the environment. This distributed approach describes how automatic flocking behaviour of a team of mobile agents can be achieved by leveraging the geometrical properties of centroidal Voronoi tessellation in area coverage control while enabling multiple targets tracking without the need of consensus between individual agents. Agent deployment using a time-varying density model is being introduced which is a function of the position of some unknown targets in the environment. A nonlinear derivative of the error coverage function is formulated based on the single-integrator agent dynamics. The agent, aware of its local coverage control condition, learns a value function online while leveraging the same from its neighbours. Moreover, a novel computational adaptive optimal control methodology based on work by [4] is proposed that employs the approximate dynamic programming technique online to iteratively solve the algebraic Riccati equation with completely unknown system dynamics as a solution to linear quadratic regulator problem. Furthermore, an online tuning adaptive optimal control algorithm is implemented using an actor-critic neural network recursive least-squares solution framework. The work in this thesis illustrates that reinforcement learning-based techniques can be successfully applied to non-uniform coverage control. Research combining non-uniform coverage control with reinforcement learning techniques is still at an embryonic stage and several limitations exist. Theoretical results are benchmarked and validated with related works in area coverage control through a set of computer simulations where multiple agents are able to deploy themselves, thus paving the way for efficient distributed Voronoi coverage control problems.

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Swarm Intelligence (SI) is a young field of study from which solutions to complex problems have been proposed based on how some natural organisms (e.g. ants, bees and others) achieve many of their daily tasks through simple sets of interactions. This thesis proposes two models for area exploration and coverage based on SI principles. These two models present a novel approach based on the combination of: ants’ pheromones, in order to keep track of visited places; and bird flocks or fish schooling, so as to move and collaborate. An implementation of both models was done in order to simulate and evaluate both the emergent behavior of the agents as well as their area exploration and coverage performance. Based on the outcome of the simulations it is concluded that both models are able to perform the exploration and coverage task and that one model is better than the other.

Wireless Sensor Networks (WSNs) are widely used for various civilian and military applications, and thus have attracted significant interest in recent years. This work investigates the important problem of optimal deployment of WSNs in terms of coverage and energy consumption. Five deployment algorithms are developed for maximal sensing range and minimal energy consumption in order to provide optimal sensing coverage and maximum lifetime. Also, all developed algorithms include self-healing capabilities in order to restore the operation of WSNs after a number of nodes have become inoperative. Two centralized optimization algorithms are developed, one based on Genetic Algorithms (GAs) and one based on Particle Swarm Optimization (PSO). Both optimization algorithms use powerful central nodes to calculate and obtain the global optimum outcomes. The GA is used to determine the optimal tradeoff between network coverage and overall distance travelled by fixed range sensors. The PSO algorithm is used to ensure 100% network coverage and minimize the energy consumed by mobile and range-adjustable sensors. Up to 30% - 90% energy savings can be provided in different scenarios by using the developed optimization algorithms thereby extending the lifetime of the sensor by 1.4 to 10 times. Three distributed optimization algorithms are also developed to relocate the sensors and optimize the coverage of networks with more stringent design and cost constraints. Each algorithm is cooperatively executed by all sensors to achieve better coverage. Two of our algorithms use the relative positions between sensors to optimize the coverage and energy savings. They provide 20% to 25% more energy savings than existing solutions. Our third algorithm is developed for networks without self-localization capabilities and supports the optimal deployment of such networks without requiring the use of expensive geolocation hardware or energy consuming localization algorithms. This is important for indoor monitoring applications since current localization algorithms cannot provide good accuracy for sensor relocation algorithms in such indoor environments. Also, no sensor redeployment algorithms, which can operate without self-localization systems, developed before our work.

This dissertation focuses on the path planning of unmanned aerial vehicle (UAV) swarms under distributed and hybrid control scenarios. It presents two such models and analyzes them both from theory and practice. In the first method, a distributed formation control strategy for UAV swarm based on consensus law is presented. This model makes use of the fundamental concepts of leader-follower structure, social potential functions, and algebraic graph theory to jointly address flocking and de-confliction in the formation control problem. The impact of network topology on formation control is analyzed. It is shown that the degree distribution of the network representing the multi-agent system defines the rate at which formation is attained. Conditions for convergence and stability are derived. In the second method, a hybrid framework for path planning and coverage area by UAV swarms is presented. This strategy significantly improves the current labor-intensive and resource-constraint operations in aquaculture farms. To monitor the farms periodically, an optimized back-and-forth flight path based on the Shamos algorithm is utilized. A trajectory tracking strategy for UAV swarms under uncertain wind conditions is presented.

Connected Vehicle research has emerged as one of the highest priorities in the transportation systems because connected vehicle technology has the potential to improve safety, mobility, and environment for the current transportation systems. Various connected vehicle based applications have been identified and evaluated through various measurements to assess the performance of connected vehicle applications. However, most of these previous studies have used hypothetical study areas with simple networks for connected vehicle environment. This study represents connected vehicle environment in TRANSIMS to assess the performance of V2V communication applications in the realistic urban network. The communication duration rate and spatial-temporal dispersion of equipped vehicles are investigated to evaluate the capability of V2V communication based on the market penetration rate of equipped vehicles and wireless communication coverage in the whole study area. The area coverage level is used to assess the spatial-temporal dispersion of equipped vehicles for two study areas. The distance of incident information propagation and speed estimation error are used to measure the performance of event-driven and periodic applications based on different market penetration rates of equipped vehicles and wireless communication coverage in both morning peak and non-peak times. The wireless communication coverage is the major factor for event-driven application and the market penetration rate of equipped vehicles has more impact on the performance of periodic application. The required minimum levels of deployment for each application are determined for each scenario. These study findings will be useful for making decisions about investments on deployment of connected vehicle applications to improve the current transportation systems. Notably, event-driven applications can be reliably deployed in the initial stage of deployment despite the low level of market penetration of equipped vehicles. / Ph. D.

As unmanned vehicle capabilities have matured, the design and development of autonomous collaborative Systems-of-Systems (SoS) has gained increased attention. This has been motivated by the indication that significant improvements in overall effectiveness may be possible by employing many systems in cooperation with one another. However, as the potential combinations of vehicles, subsystems, and operational concepts becomes increasingly large, a systematic approach is needed for designing and analyzing alternatives. Furthermore, the discrete nature of the problem can cause variations in effectiveness that are counter-intuitive, such as a point of diminishing returns as the number of systems grows. Systems-of-systems are hierarchical in nature, consisting of top-level mission requirements that are decomposed into system- and subsystem-level performance measures. The overarching research objectives of this dissertation are to show that the analysis of alternatives should be performed at varying levels of the SoS hierarchy and to provide novel means for performing those analyses. In particular, it has been postulated that a formulation built on an energy-based approach to multi-level analysis of SoS components will enable more accurate and transparent subsystem and system trade-offs. Various steps of the design process are established and argued for or against, and significant focus is placed on the analysis of alternatives. The foundation of the new method is laid on structured SoS engineering principles. The full substance comes together by incorporating unique aspects developed within this dissertation. A new virtual experimentation approach is presented for creating sensor performance representations that are functions of vehicle operations. The sonar equation is used as a baseline sensor model for comparison against the new virtual experimentation method. Dozens of forward-looking and side-scan sonar experiments are designed, and data is provided to show the extent to which typical sensor modeling over-predicts performance without vehicle operations considered. In addition, comparisons are made between possible representations of vehicle performance. An underwater vehicle sizing and synthesis process is developed to enable comparisons between system-level component modeling approaches. The experiments attest to significant gaps in accuracy when performing sensor and operational trade-offs without energy-based modeling of the collaborative vehicles. Finally, a heuristic path-planning algorithm is formulated, and mixed-integer linear programming is used to choose between alternative SoS designs. The developed method is demonstrated through a representative example problem: a group of unmanned underwater vehicles (UUVs) operating in a collaborative fashion to search for underwater objects. The example scenario provides an application for illustrating the phenomenon discussed in regards to the analysis of alternatives of collaborative SoS. The significance of providing more or less analytic detail is traced and the effect on mission requirements is quantified. Counter-intuitive results are highlighted, such as the observation that the increased energy required for systems to effectively collaborate can often out-weigh the benefits gained in overall mission effectiveness.

The subject of this dissertation is Prague Fire Brigade. The purpose of this thesis is a basic element of integrated rescue system presentation in a context of legislative requirements and their practical impletion. A complex insight on fire brigade functioning, which provides fire protection in the metropolis, should eventuate.

Fire Rescue Service of Capital of Prague is the subject of this thesis. Generally, Fire Rescue Service is the fundamental element of Integrated Rescue System. FRS of Capital of Prague's primary objective is to protect life, health and property in the case of fire and other emergency events. Area of Capital of Prague is markedly specific in comparison to other regions of the Czech Republic. It puts increased requirements on the labour of Prague's fire fighters. The main aim of this thesis is to obtain integral look in the function of Fire Rescue Service of Capital of Prague and to propose steps that could eventually help to improve conditions for the provided service.