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Energy is seen as a significant factor in wireless sensor networks (WSNs). It is a challenge to balance between battery lifetime of the different sensors and network lifetime. The main contribution of the proposed approach is to decrease the energy consumption of each sensor node, overcome unbalanced energy usage among sensor nodes, reduce the data gathering time and enhance the network lifetime. To achieve these goals, we combine the Hierarchical Agglomerative algorithm and an optimal path selection method. First, the suitable cluster heads (CHs) are elected based on the Euclidean distance and the residual energy of each sensor node. Then, the base station is situated at the center of the field, which will be partitioned into equal subareas, one for every mobile data collector (MDC). Second, the Kruskal algorithm is used to create an optimal data gathering path from each subset of elected cluster heads. Finally, each mobile data collector travels the optimal path to collect the data from the set of cluster heads of each subarea and returns periodically to the base station to upload gathered data. Computer simulation proves that the proposed approach outperforms existing ones in terms of data gathering time, residual energy and network lifetime

The most advanced ports as of writing of this report are at least somewhat autonomous. Whether discussing the transporters between crane and stack (temporary storage) or cranes, the ports are shifting into a completely autonomous system. This ultimate goal presents a challenge in regards to unloading and loading cargo ships in the harbour. How do you achieve unloading of a ship without human intervention while still guaranteeing secure trajectories for the containers? ABB Ports in collaboration with the Division of Vehicular Systems at Linköping University have developed a simulation that utilises a simple control model to investigate the behaviour, limitations and capabilities of such an autonomous crane. Specifically, this simulation utilises a model of the dynamics of a Ship-to-Shore crane (STS), which has the task of unloading a ship. In order to set the crane model in context of realistic scenarios, some additions to the simulation are needed. One of these additions is obstacles. Before this thesis work, the model enjoyed an empty simulation environment to freely optimise how quickly the containers could be transported off of the ship. The addition of obstacles in the form of other containers on the cargo ship as well as the physical presence of the crane’s legs presents new challenges for the optimiser used to solve the optimal control problems formulated through the model in the simulation. The implementation of obstacles is one of the objectives for this thesis. This addition was implemented by modeling the obstacle dimensions and ship limitations by looking at the largest container ships in the world. Due to the simulation not containing obstacles previous to this thesis work, the initial guess provided to the solver initialised the solving in an area of convergence that is unfair to the solver, This rendered the simulation useless, as any obstacle presented to the solver would generate an infeasible solution. Another functionality needed for the obstacle i

Based on optimal path features and multiple fields of view (FOVs), a star recognition algorithm is developed to improve the recognition rate of star sensors with low cost and small FOV under all-sky autonomous recognition pattern. First, observed star images in multiple FOVs are fused through identification between images. Second, randomly distributed ant colonies are set at the positions of all the stars in the neighborhood of the main star. Then an optimal traversal path combining all these stars is acquired by iteration. The geometrical features of the path are applied for matching with guide star catalogue. Compared with several existing methods, the proposed algorithm provides high recognition rate and robustness against position and magnitude noise., SCOPUS: ar.j, info:eu-repo/semantics/published

A Master of Science thesis in Mechatronics Engineering by Hussein Ali Mohammed entitled, “Mobile Robot Navigation in Dynamic Environments Using an Improved RRT* Approach”, submitted in November 2018. Thesis advisor is Dr. Lotfi Romdhane and thesis co-advisor is Dr. Mohammad Jaradat. Soft and hard copy available., College of Engineering, Multidisciplinary Programs

The research in Wireless Sensor Networks (WSNs) have increasingly been done since many year for its application-specific utilization. Depending on the environment, WSNs have been deployed for the sake data sensing, gathering and transmitting it to the nearest sink. Usually, the WSNs are small in size with limited computational capabilities. Therefore, an efficient routing scheme determine the usage of battery having minimal power. In this study, we develop a scenario with the deployment of sensor nodes and sinks in the WSNs field. The sensor nodes are connected to the nearest possible sink. Then, we apply Genetic Algorithm (GA) with some basic modifications for the evaluation and optimization of the distance and energy consumption. If any node sensed the data and the path is optimized then the distance is reduced by multi-hop scheme, the lifetime of the sensor network is considerably maximized. Multi-hop routing scheme has an edge over the single hop or direct routing scheme where minimal energy is consumed in transmitting the data towards the nearest sink. For the sake of simulation, a variable number of sensor nodes with one or more sinks are deployed and the results are done in MATLAB. Further, these results are compared with TEEN (threshold sensitive energy efficient sensor network) protocol.

‘Form-finding of branching structures supporting freeform architectural surfaces’ is the graduation thesis of the master track Building Technology at the University of Technology in Delft. This thesis is made by Alex kouwenhoven in the time-span of three-quarter of a year and tutored by Andrew Borgart and Ate Snijder. With the increasing complexability of the built environment, the challenges for both the architect and the engineer are becoming bigger. Architects tend to make new shapes or free-forms and the engineers are challenged to make it buildable. A shape often used as a support of this free-form architecture is the tree-like column or branching column. A structure branching out and redirecting forces from a big roof surface to one single point. However, the design of these complex columns is often done by the computer and optimizing software. There is a missing link in the field of structural mechanics and these types of columns, the knowledge of forces flowing through these complex three-dimensional structures. In this thesis, a method of designing these complex structures is proposed. By an analytical approach, an efficient structural column can be made and multiple optimization strategies are proposed. Following these strategies, a design can be made. In the final chapters of the thesis, an example of a design is made using this strategy., Architecture, Urbanism and Building Sciences | Building Technology

[ES] El objetivo de este Trabajo Fin de Máster es diseñar e implementar una metodología y sistema para dotar a un robot terrestre móvil con la capacidad de alcanzar eficientemente una posición objetivo dentro de un entorno limitado y que puede verse condicionado por obstáculos desconocidos. Para cumplir este objetivo, el sistema robótico identifica en primer lugar los obstáculos presentes en el entorno y, a continuación, planifica la mejor trayectoria que permita al robot evitar de forma eficiente cualquier posible colisión, lo cuál es un requisito necesario para su seguridad y completa autonomía. La técnica propuesta aborda la planificación y el despliegue de tales maniobras complejas del robot en un entorno real y son aplicables a cualquier entorno industrial. Esto se logra combinando modelos simples, implementaciones eficientes y simulaciones interactivas que aprovechan la agilidad y la maniobrabilidad del robot. En primer lugar, se obtiene una solucion óptima al problema de la evasión de obstáculos mediante el desarrollo de un controlador en Simulink que se basa en una implementación en Matlab del algoritmo A*, un algoritmo clásico de Inteligencia Artificial que permite calcular trayectorias de coste mínimo desde un punto inicial hasta un objetivo dado en un área delimitada. La estrategia de navegación propuesta se prueba primero con SFunctions para validar interactivamente el comportamiento del controlador, y luego en cosimulación de Simulink y el framework Simscape Multibody, para obtener una visión más real al considerar las propiedades físicas del robot y la fricción. A partir de una matriz de entradas que representa una imagen del entorno obtenida utilizando una cámara monocular, la imagen se segmenta y se procesa utilizando la herramienta software Matlab, y se calcula una trayectoria optima para ir desde un punto inicial a un punto final evadiendo los obstáculos del entorno. La trayectoria calculada se evalúa entonces mediante co-simulación, lo que permite, [EN] The goal of this Master Thesis is to design and implement a methodology and system to endow a mobile terrestrial robot with the capability to efficiently reach its goals in a bounded environment that can be constrained with unknown obstacles. To meet this goal, the robotic system first identifies the obstacles in the environment and then plans the best robot trajectory that efficiently avoids any possible collision, which is a necessary requirement for the robot to stay safe and completely autonomous. The technique proposed in this thesis supports planning and deploying of such complex robot maneuvers in a real environment and can be applied to any industrial environment. This is done by combining simple models, efficient implementations, and interactive simulations that leverage the agility and maneuverability of the robot. First, an optimal solution to the problem of obstacle avoidance is achieved by developing in Simulink a controller that relies on a Matlab implementation of the A* algorithm, a classical Artificial Intelligence algorithm that is able to compute minimal cost paths from a start point to a target point in a bounded area. The proposed navigation strategy is first tested with S-Functions to interactively validate the controller¿s behavior, and then in co-simulation of Simulink and the Simscape Multibody framework for a more real view that considers the robot¿s physical properties and friction. Starting from a matrix of input values that correspond to an image of the environment obtained by using a monocular camera, the image is segmented and processed in the Matlab software environment, and an optimal trajectory is computed which leads from the starting point to the target point without collision. The path trajectory is evaluated by using co-simulation to visually analyze the estimated path trajectory followed by the robot. Then, the technique is implemented in a real robotic system, the LEGO MINDOSTORMS EV3, by following a methodology that also

Linear Temporal Logic (LTL), as one of the temporal logic, can generate a fully automated correct-by-design controller synthesis approach for single or multiple autonomous vehicles, under much more complex missions than the traditional point-to-point navigation.In this master thesis, a framework which combines model- checking-based robot motion planning with action planning is proposed based on LTL for-mulas. The specifications implicitly require both sequential regions for multi-agent to visit and the desired actions to perform at these regions while avoid-ing collision with each other and fixed obstacles. The high level motion and task planning and low level navigation function based collision avoidance controller are verified by nontrivial simulation and implementation on real quadcopter in Smart Mobility Lab.

This report summarizes the research conducted under the MURI entitled Optimal Vessel Maneuvering in Evolving Nonlinear Wave Fields. A radar system was developed to measure the wave field around the ship. A nonlinear pseudo-spectral wave model that accounts for wave-wave interactions, wave-current interactions, wave breaking and wind-wave effects has been developed to forecast in time and space the evolving waves around the ship. The radar wave field predictions that initialize the wave field model are improved using assimilation techniques. Two techniques were developed to compute the responses of the ship to the predicted wave field: A body-exact strip theory and a FFT accelerated boundary element method. The optimal path through the wave field that minimizes ship motions and travel time is found using a new dynamic programing model. Various control algorithms were investigated to allow the ship to follow the optimum path.

We consider the problem of switching in stochastic systems with delayed feedback. A general variational formation is derived for the switch rate in a stochastic differential delay equation where the noise sources is of general form. The resulting equations of motion and boundary conditions describe the optimal escape path which maximizes the probability of escape. Analyzing the dynamics along the optimal path yields exponents of the distribution in terms of delay time, dissipation, and noise intensity. Theoretical predictions compare very well with numerical simulations in both additive and multiplicative noise cases, even outside of regions where the delay is assumed to be small., The original document contains color images.

This paper examines the value of real-time traffic information gathered through Geographic Information Systems for achieving an optimal vehicle routing within a dynamically stochastic transportation network. We present a systematic approach in determining the dynamically varying parameters and implementation attributes that were used for the development of a Web-based transportation routing application integrated with real-time GIS services. We propose and implement an optimal routing algorithm by modifying Dijkstra’s algorithm in order to incorporate stochastically changing traffic flows. We describe the significant features of our Web application in making use of the real-time dynamic traffic flow information from GIS services towards achieving total costs savings and vehicle usage reduction. These features help users and vehicle drivers in improving their service levels and productivity as the Web application enables them to interactively find the optimal path and in identifying destinations effectively.

The objective of this paper is to re-examine the risk-and effort attitude in the context of strategic dynamic interactions stated as a discrete-time finite-horizon Nash game. The analysis is based on the assumption that players are endogenously risk-and effort-averse. Each player is characterized by distinct risk-and effort-aversion types that are unknown to his opponent. The goal of the game is the optimal risk-and effort-sharing between the players. It generally depends on the individual strategies adopted and, implicitly, on the the players' types or characteristics.

The objective of this paper is to clarify the interactive nature of the leader-follower relationship when both players are endogenously risk-averse. The analysis is placed in the context of a dynamic closed-loop Stackelberg game with private information. The case of a risk-neutral leader, very often discussed in the literature, is only a borderline possibility in the present study. Each player in the game is characterized by a risk-averse type which is unknown to his opponent. The goal of the leader is to implement an optimal incentive compatible risk-sharing contract. The proposed approach provides a qualitative analysis of adaptive risk behavior profiles for asymmetrically informed players in the context of dynamic strategic interactions modelled as incentive Stackelberg games. Download Info

The objective of this paper is to correct and improve the results obtained by Van der Ploeg (1984a, 1984b) and utilized in the theoretical literature related to feedback stochastic optimal control sensitive to constant exogenous risk-aversion (see, Jacobson, 1973, Karp, 1987 and Whittle, 1981, 1989, 1990, among others) or to the classic context of risk-neutral decision-makers (see, Chow, 1973, 1976a, 1976b, 1977, 1978, 1981, 1993). More realistic and attractive, this new approach is placed in the context of a time-varying endogenous risk-aversion which is under the control of the decision-maker. It has strong qualitative implications on the agent's optimal policy during the entire planning horizon.

A new method for obtaining the optimal path to robot navigation in 2-D environments is presented in this paper. To obtain the optimal path we use mathematical morphology in binary worlds and the geodesic distance. The navigation algorithm is based on the search for a path of minimum cost by using the wave-front of the geodesic distance of the mathematical morphology. The optimal path will be the one that minimize the direction changes of the robot. The algorithm of optimal path will be applied in several and complex 2-D environments.

This dissertation addresses two classes of network flow problems in networks with multiple, stochastic and time-varying attributes. The first problem class is concerned with providing routing instructions with the ability to make updated decisions as information about travel conditions is revealed for individual travelers in a transportation network. Three exact algorithms are presented for identifying all or a subset of the adaptive Pareto-optimal solutions with respect to the expected value of each criterion from each node to a desired destination for each departure time in the period of interest. The second problem class is concerned with problems of determining the optimal set of a priori path flows for evacuation in capacitated networks are addressed, where the time-dependent and stochastic nature of arc attributes and capacities inherent in these problems is explicitly considered. The concept of Safest Escape is formulated for developing egress instructions. An exact algorithm is proposed to determine the pattern of flow that maximizes the minimum path probability of successful arrival of supply at the sink. While the Safest Escape problem considers stochastic, time-varying capacities, arc travel times, while time-varying, are deterministic quantities. Explicit consideration of stochastic and time-varying travel times makes the SEscape problem and other related problems significantly more difficult. A meta-heuristic based on the principles of genetic algorithms is developed for determining optimal path flows with respect to several problems in dynamic networks, where arc traversal times and capacities are random variables with probability mass functions that vary with time. The proposed genetic algorithm is extended for use in more difficult, stochastic, time-varying and multicriteria, capacitated networks, for which no exact, efficient algorithms exist. Several objectives may be simultaneously considered in determining the optimal flow pattern: minimize total

Presenting a discrete time version of the Romer (1986) model, this paper analyzes optimal paths in a one-sector growth model when the technology is not convex. We prove that for a given quality of knowledge technology, the countries could take-off if their initial stock of capital are above a critical level; otherwise they could face a poverty-trap. We show that for an economy which wants to take-off by means of knowledge technology requires three factors large amount of initial knowledge, small fixed costs and a good quality of knowledge technology.

Cílem této diplomové práce je implementace plánování optimální trajektorie letadla letícího v nižších výškách, které se musí vyhýbat překážkám. Pro metodu plánování se předpokládá statické a předem známé prostředí. V této práci jsou popsány principy, optimálnost a složitost vybraných metod plánování. Na základě vlastností metod je vybrána metoda nejvhodnější k implementaci., The aim of this master's thesis is the implementation of optimal trajectory planning for an airplane flying in lower altitudes, which has to avoid collision with obstacles. For the planning we assume static and fully known environment. There are described principals, optimality and complexity for some chosen methods of planning in this thesis. And based on the methods' characteristics it's chosen the best method for implementation.

Cílem této diplomové práce je implementace plánování optimální trajektorie letadla letícího v nižších výškách, které se musí vyhýbat překážkám. Pro metodu plánování se předpokládá statické a předem známé prostředí. V této práci jsou popsány principy, optimálnost a složitost vybraných metod plánování. Na základě vlastností metod je vybrána metoda nejvhodnější k implementaci., The aim of this master's thesis is the implementation of optimal trajectory planning for an airplane flying in lower altitudes, which has to avoid collision with obstacles. For the planning we assume static and fully known environment. There are described principals, optimality and complexity for some chosen methods of planning in this thesis. And based on the methods' characteristics it's chosen the best method for implementation.

Cílem této diplomové práce je implementace plánování optimální trajektorie letadla letícího v nižších výškách, které se musí vyhýbat překážkám. Pro metodu plánování se předpokládá statické a předem známé prostředí. V této práci jsou popsány principy, optimálnost a složitost vybraných metod plánování. Na základě vlastností metod je vybrána metoda nejvhodnější k implementaci., The aim of this master's thesis is the implementation of optimal trajectory planning for an airplane flying in lower altitudes, which has to avoid collision with obstacles. For the planning we assume static and fully known environment. There are described principals, optimality and complexity for some chosen methods of planning in this thesis. And based on the methods' characteristics it's chosen the best method for implementation.

Cílem této diplomové práce je implementace plánování optimální trajektorie letadla letícího v nižších výškách, které se musí vyhýbat překážkám. Pro metodu plánování se předpokládá statické a předem známé prostředí. V této práci jsou popsány principy, optimálnost a složitost vybraných metod plánování. Na základě vlastností metod je vybrána metoda nejvhodnější k implementaci., The aim of this master's thesis is the implementation of optimal trajectory planning for an airplane flying in lower altitudes, which has to avoid collision with obstacles. For the planning we assume static and fully known environment. There are described principals, optimality and complexity for some chosen methods of planning in this thesis. And based on the methods' characteristics it's chosen the best method for implementation.

Cílem této diplomové práce je implementace plánování optimální trajektorie letadla letícího v nižších výškách, které se musí vyhýbat překážkám. Pro metodu plánování se předpokládá statické a předem známé prostředí. V této práci jsou popsány principy, optimálnost a složitost vybraných metod plánování. Na základě vlastností metod je vybrána metoda nejvhodnější k implementaci., The aim of this master's thesis is the implementation of optimal trajectory planning for an airplane flying in lower altitudes, which has to avoid collision with obstacles. For the planning we assume static and fully known environment. There are described principals, optimality and complexity for some chosen methods of planning in this thesis. And based on the methods' characteristics it's chosen the best method for implementation.

Cílem této diplomové práce je implementace plánování optimální trajektorie letadla letícího v nižších výškách, které se musí vyhýbat překážkám. Pro metodu plánování se předpokládá statické a předem známé prostředí. V této práci jsou popsány principy, optimálnost a složitost vybraných metod plánování. Na základě vlastností metod je vybrána metoda nejvhodnější k implementaci., The aim of this master's thesis is the implementation of optimal trajectory planning for an airplane flying in lower altitudes, which has to avoid collision with obstacles. For the planning we assume static and fully known environment. There are described principals, optimality and complexity for some chosen methods of planning in this thesis. And based on the methods' characteristics it's chosen the best method for implementation.