24 research outputs found
A COLLISION AVOIDANCE SYSTEM FOR AUTONOMOUS UNDERWATER VEHICLES
The work in this thesis is concerned with the development of a novel and practical collision
avoidance system for autonomous underwater vehicles (AUVs). Synergistically,
advanced stochastic motion planning methods, dynamics quantisation approaches,
multivariable tracking controller designs, sonar data processing and workspace representation,
are combined to enhance significantly the survivability of modern AUVs.
The recent proliferation of autonomous AUV deployments for various missions such
as seafloor surveying, scientific data gathering and mine hunting has demanded a substantial
increase in vehicle autonomy. One matching requirement of such missions is
to allow all the AUV to navigate safely in a dynamic and unstructured environment.
Therefore, it is vital that a robust and effective collision avoidance system should be
forthcoming in order to preserve the structural integrity of the vehicle whilst simultaneously
increasing its autonomy.
This thesis not only provides a holistic framework but also an arsenal of computational
techniques in the design of a collision avoidance system for AUVs. The
design of an obstacle avoidance system is first addressed. The core paradigm is the
application of the Rapidly-exploring Random Tree (RRT) algorithm and the newly
developed version for use as a motion planning tool. Later, this technique is merged
with the Manoeuvre Automaton (MA) representation to address the inherent disadvantages
of the RRT. A novel multi-node version which can also address time varying
final state is suggested. Clearly, the reference trajectory generated by the aforementioned
embedded planner must be tracked. Hence, the feasibility of employing the
linear quadratic regulator (LQG) and the nonlinear kinematic based state-dependent
Ricatti equation (SDRE) controller as trajectory trackers are explored.
The obstacle detection module, which comprises of sonar processing and workspace
representation submodules, is developed and tested on actual sonar data acquired
in a sea-trial via a prototype forward looking sonar (AT500). The sonar processing
techniques applied are fundamentally derived from the image processing perspective.
Likewise, a novel occupancy grid using nonlinear function is proposed for the
workspace representation of the AUV. Results are presented that demonstrate the
ability of an AUV to navigate a complex environment.
To the author's knowledge, it is the first time the above newly developed methodologies
have been applied to an A UV collision avoidance system, and, therefore, it is
considered that the work constitutes a contribution of knowledge in this area of work.J&S MARINE LT
Development of systematic technique for energy and property integration in batch processes
The increasing consumption of energy, generation of waste as well as higher cost of fresh resources and waste treatment systems are the important driving forces for developing efficient, environmentally friendly and economic resource conservation techniques in the process industries. Process integration is being recognized as an useful systematic strategy for resource conservation and waste minimization. Up to date, less research works have been investigated on heat and property integration and these works are only focused on continuous processes.Since the application of batch processes is increasingly popular due to the development of technology-intensive industries such as pharmacy, fine chemistry and foods, it is necessary to consider both heat and property integration in batch processes simultaneously. In this thesis, a new mixed integer nonlinear programming (MINLP) mathematical model is introduced to synthesize a property-based heat integrated resource conservation networks (HIRCNs) for batch processes. A source-HEN-sink superstructure is constructed to embed all possible network configurations. Then, an MINLP model that consists of propertybased resource conservation network (RCN) and heat exchanger network (HEN) models is developed.In the proposed model, the property-based RCN model is formulated based on supertargeting approach while HEN model is formulated via automated targeting method (ATM). The optimization objective is to minimize total annualized cost (TAC) for a batch process system. This includes the operating cost of fresh resources, hot and cold utilities as well as the capital cost of storage tanks. To demonstrate the proposed approach, three case studies were solved. Based on the optimized results, the proposed simultaneous targeting approach for property-based HIRCNs is more effective in term of TAC for HIRCNs than the presented sequential targeting approach
Optimizing transportation systems and logistics network configurations : From biased-randomized algorithms to fuzzy simheuristics
242 páginasTransportation and logistics (T&L) are currently highly relevant functions in any competitive industry. Locating facilities or distributing goods to hundreds or thousands of customers are activities with a high degree of complexity, regardless of whether facilities and customers are placed all over the globe or in the same city. A countless number of alternative strategic, tactical, and operational decisions can be made in T&L systems; hence, reaching an optimal solution –e.g., a solution with the minimum cost or the maximum profit– is a really difficult challenge, even by the most powerful existing computers. Approximate methods, such as heuristics, metaheuristics, and simheuristics, are then proposed to solve T&L problems. They do not guarantee optimal results, but they yield good solutions in short computational times. These characteristics become even more important when considering uncertainty conditions, since they increase T&L problems’ complexity. Modeling uncertainty implies to introduce complex mathematical formulas and procedures, however, the model realism increases and, therefore, also its reliability to represent real world situations. Stochastic approaches, which require the use of probability distributions, are one of the most employed approaches to model uncertain parameters. Alternatively, if the real world does not provide enough information to reliably estimate a probability distribution, then fuzzy logic approaches become an alternative to model uncertainty. Hence, the main objective of this thesis is to design hybrid algorithms that combine fuzzy and stochastic simulation with approximate and exact methods to solve T&L problems considering operational, tactical, and strategic decision levels. This thesis is organized following a layered structure, in which each introduced layer enriches the previous one.El transporte y la logística (T&L) son actualmente funciones de gran relevancia en cual quier industria competitiva. La localización de instalaciones o la distribución de mercancías
a cientos o miles de clientes son actividades con un alto grado de complejidad, indepen dientemente de si las instalaciones y los clientes se encuentran en todo el mundo o en la
misma ciudad. En los sistemas de T&L se pueden tomar un sinnúmero de decisiones al ternativas estratégicas, tácticas y operativas; por lo tanto, llegar a una solución óptima –por
ejemplo, una solución con el mínimo costo o la máxima utilidad– es un desafío realmente di fícil, incluso para las computadoras más potentes que existen hoy en día. Así pues, métodos
aproximados, tales como heurísticas, metaheurísticas y simheurísticas, son propuestos para
resolver problemas de T&L. Estos métodos no garantizan resultados óptimos, pero ofrecen
buenas soluciones en tiempos computacionales cortos. Estas características se vuelven aún
más importantes cuando se consideran condiciones de incertidumbre, ya que estas aumen tan la complejidad de los problemas de T&L. Modelar la incertidumbre implica introducir
fórmulas y procedimientos matemáticos complejos, sin embargo, el realismo del modelo
aumenta y, por lo tanto, también su confiabilidad para representar situaciones del mundo
real. Los enfoques estocásticos, que requieren el uso de distribuciones de probabilidad, son
uno de los enfoques más empleados para modelar parámetros inciertos. Alternativamente,
si el mundo real no proporciona suficiente información para estimar de manera confiable
una distribución de probabilidad, los enfoques que hacen uso de lógica difusa se convier ten en una alternativa para modelar la incertidumbre. Así pues, el objetivo principal de
esta tesis es diseñar algoritmos híbridos que combinen simulación difusa y estocástica con
métodos aproximados y exactos para resolver problemas de T&L considerando niveles de
decisión operativos, tácticos y estratégicos. Esta tesis se organiza siguiendo una estructura
por capas, en la que cada capa introducida enriquece a la anterior. Por lo tanto, en primer
lugar se exponen heurísticas y metaheurísticas sesgadas-aleatorizadas para resolver proble mas de T&L que solo incluyen parámetros determinísticos. Posteriormente, la simulación
Monte Carlo se agrega a estos enfoques para modelar parámetros estocásticos. Por último,
se emplean simheurísticas difusas para abordar simultáneamente la incertidumbre difusa
y estocástica. Una serie de experimentos numéricos es diseñada para probar los algoritmos
propuestos, utilizando instancias de referencia, instancias nuevas e instancias del mundo
real. Los resultados obtenidos demuestran la eficiencia de los algoritmos diseñados, tanto
en costo como en tiempo, así como su confiabilidad para resolver problemas realistas que
incluyen incertidumbre y múltiples restricciones y condiciones que enriquecen todos los
problemas abordados.Doctorado en Logística y Gestión de Cadenas de SuministrosDoctor en Logística y Gestión de Cadenas de Suministro