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In this dissertation, methods for optimal multi-robot task allocation (MRTA) for industrial plant inspection are investigated. MRTA involves distributing and scheduling a set of tasks for a group of robots to minimize the total cost taking into account operational constraints. With technical progress and declining cost of robotic mobility, interest in industrial mobile robotics has grown significantly in recent years. Many efforts have been devoted to mobility-related problems such as self-localization and mapping, though only few studies deal with the optimal task allocation in multi-robot systems. Since a good task allocation provides more efficient scheduling (e.g. less cost, shorter time), the objective of this research is to develop search/optimization methods for inspection problems that involve both single- and two-robot tasks.
Performing experiments for system identification of continuously operated plants might be restricted as it can impact negatively normal production. In such cases, using historical logged data can become an attractive alternative for system identification. However, operating points are rarely changed and parameter estimation methods can suffer numerical problems. Three main drawbacks of current approaches in this research area can be discussed. Firstly, detection tests are not adapted for dynamical systems. Secondly, methods to define upper interval bounds are not robust to colored noise that is more likely to be found in real applications. Thirdly, model estimation with the retrieved data is not supported and the performance of the method cannot be assessed. In the method proposed in this work, called data selection for system identification (DS4SID), previous drawbacks are addressed and robust tests are designed and implemented. The performance of DS4SID is evaluated in a simulated and laboratory multivariate processes. A process unit of the lab-scale factory “μPlant” is used as industryoriented case study. Models estimated with selected data are shown to have similar performance than estimates with the entire data set.
Infrared thermography enables the non-contact measurement of an object’s surface temperature and presents the results in form of thermal images. The analysis of these images provides valuable information about an object’s thermal state. However, the fidelity of the thermal images strongly depends on the pose of the thermographic camera with respect to the surface. 3D thermography offers the possibility to overcome this and other limitations that affect conventional 2D thermography but most 3D thermographic systems developed so far generate 3D thermograms from a single perspective or from few noncontiguous points of view and do not operate in real time. As a result, the 3D thermograms they generate do not offer much advantage over conventional thermal images. However, recent technological advances have unlocked the possibility of implementing affordable handheld 3D thermal imaging systems that can be easily maneuvered around an object and that can generate high-fidelity 3D thermograms in real time. This thesis explores various aspects involved in the real-time generation of high-fidelity 3D thermograms at close range using a handheld 3D thermal imaging system, presents the results of scanning an operating industrial furnace and discusses the problems associated with the generation of 3D thermograms of large objects with complex geometries.
The two-volume set LNAI 12319 and 12320 constitutes the proceedings of the 9th Brazilian Conference on Intelligent Systems, BRACIS 2020, held in Rio Grande, Brazil, in October 2020. The total of 90 papers presented in these two volumes was carefully reviewed and selected from 228 submissions. The contributions are organized in the following topical section: Part I: Evolutionary computation, metaheuristics, constrains and search, combinatorial and numerical optimization; neural networks, deep learning and computer vision; and text mining and natural language processing. Part II: Agent and multi-agent systems, planning and reinforcement learning; knowledge representation, logic and fuzzy systems; machine learning and data mining; and multidisciplinary artificial and computational intelligence and applications. Due to the Corona pandemic BRACIS 2020 was held as a virtual event.
This book compiles some of the latest research in cooperation between robots and sensor networks. Structured in twelve chapters, this book addresses fundamental, theoretical, implementation and experimentation issues. The chapters are organized into four parts namely multi-robots systems, data fusion and localization, security and dependability, and mobility.
This Proceedings Volume documents recent cutting-edge developments in multi-robot systems research and is the result of the Second International Workshop on Multi-Robot Systems that was held in March 2003 at the Naval Research Laboratory in Washington, D.C. This Workshop brought together top researchers working in areas relevant to designing teams of autonomous vehicles, including robots and unmanned ground, air, surface, and undersea vehicles. The workshop focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. A broad range of applications of this technology are presented in this volume, including UCAVS (Unmanned Combat Air Vehicles), micro-air vehicles, UUVs (Unmanned Underwater Vehicles), UGVs (Unmanned Ground Vehicles), planetary exploration, assembly in space, clean-up, and urban search and rescue. This Proceedings Volume represents the contributions of the top researchers in this field and serves as a valuable tool for professionals in this interdisciplinary field.
Mobile robots and Wireless Sensor Networks (WSNs) have enabled great potentials and a large space for ubiquitous and pervasive applications. Robotics and WSNs have mostly been considered as separate research fields and little work has investigated the marriage between these two technologies. However, these two technologies share several features, enable common cyber-physical applications and provide complementary support to each other. The primary objective of book is to provide a reference for cutting-edge studies and research trends pertaining to robotics and sensor networks, and in particular for the coupling between them. The book consists of five chapters. The first chapter presents a cooperation strategy for teams of multiple autonomous vehicles to solve the rendezvous problem. The second chapter is motivated by the need to improve existing solutions that deal with connectivity prediction, and proposed a genetic machine learning approach for link-quality prediction. The third chapter presents an architecture for indoor navigation using an Android smartphone for guiding a variety of users, from sighted to the visually impaired, to their intended destination. In chapter four, the authors deal with accurate prediction modeling of ocean currents for underwater glider navigation. In chapter five, the authors discuss the challenges and limitations of RSS-based localization mechanisms and propose, EasyLoc, an autonomous and practical RSS-based localization technique that satisfies ease of deployment and implementation.
A textbook for an undergraduate course in mathematical programming for students with a knowledge of elementary real analysis, linear algebra, and classical linear programming (simple techniques). Focuses on the computation and characterization of global optima of nonlinear functions, rather than the locally optimal solutions addressed by most books on optimization. Incorporates the theoretical, algorithmic, and computational advances of the past three decades that help solve globally multi-extreme problems in the mathematical modeling of real world systems. Annotation copyright by Book News, Inc., Portland, OR
The first complete overview of evolutionary computing, the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. The text is aimed directly at lecturers and graduate and undergraduate students. It is also meant for those who wish to apply evolutionary computing to a particular problem or within a given application area. The book contains quick-reference information on the current state-of-the-art in a wide range of related topics, so it is of interest not just to evolutionary computing specialists but to researchers working in other fields.