M. Ing. / This work describes the establishment of an intelligent control system on board an AGV for automatic adaptation to the motions and actions of industrial robots. The movements can represent an assembly sequence or material loading sequence. A relationship is established between the workspaces of the industrial robot and the AGV. The coordinate systems are integrated for the AGV to respond to the movements of the industrial robot in the correct way. The integration of these two different coordinate systems leads to the creation of a common workspace. Within this common workspace, the AGV interacts with the robot using the same reference points. The mathematical analysis and practical implementation of this transient workspace is described. The adaptive control presented allows for an intelligent decision making process to be performed on line with the use of an expert system.

<p>Automated Guided Vehicles (AGV) are becoming an increasingly popular system for performing materials handling functions in industry today. The design and implementation of an Automated Guided Vehicle System (AGVS) is a complex issue and involves many aspects. It is highly advisable to design and plan the system prior to its implementation. This project considers some of the key issues and parameters in the initial design phase of an AGVS for a company manufacturing anti lock braking systems for automobiles. The design of the AGVS is carried out using simulation-animation techniques. PROMODEL pc, a popular manufacturing simulator is used for developing the simulation models. Different AGVS alternatives are considered. A multi-attribute framework is used for developing and analyzing the AGVS alternatives. The recommended AGVS design alternative is chosen based on economic justification and satisfaction of the company's throughput requirements. / Master of Engineering

see document / Master of Science

In this work, a user-friendly MATLAB tool is introduced to solve nonlinear optimal control problems by applying collocation techniques using Coupled Radial Basis Functions (CRBFs). CRBFs are a new class of Radial Basis Functions combined with a conical spline r^5, which provides the advantage of insensitivity to the shape parameter while maintaining accuracy and robustness. To solve optimal control problems, software tools are often employed to implement numerical methods and apply advanced techniques to solving differential equations. Although several commercial software tools exist for solving optimal control problems, such as ICLOCS2, GPOPS, and DIDO, there are no options available that utilize adaptive collocation with CRBFs. A unified MATLAB tool named Radial Optimal Control Software (ROCS) is introduced and not only implements the CRBF method, but also enables any user, from professionals to students, to solve nonlinear optimal control problems through a user-friendly interface. The tool accepts user input for boundary conditions, necessary conditions, and the governing equations of motion. The two-point boundary value problem (TPBVP) is approximated through collocation using CRBFs, and the resulting nonlinear algebraic equations (NAEs) are solved with a MATLAB solver. The tool's usefulness and application are demonstrated by solving classical nonlinear optimal control problems and comparing the results with the solutions found in the literature. Compared to classical numerical method techniques, the present tool is shown to solve optimal control problems more efficiently for the same level of accuracy. By introducing this unified MATLAB tool to solving nonlinear optimal control problems, the intent is to enable professionals and students to solve nonlinear optimal control problems, e.g., in astrodynamics and space-flight mechanics, without the need for extensive manipulation of code in existing software tools and without extensive knowledge of applying numerical solvers.

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The purpose of this research was to determine the feasibility of a laser-based positioning system as a primary navigation method. The system developed for this research consisted of an automated guided vehicle which navigated solely with the use of the laser-based positioning system in real-time. To date, there are no systems which can navigate a pre-defined path using such a positioning system. Some lessons were learned by the researcher, however, concerning the viability of this system in an industrial environment. The system should have had the following advantages over previous systems: 1) Greater range, 2) no prior structuring of environment, 3) real-time navigation, and 4) no reliance on dead-reckoning for navigation. The results showed that goals two through four had been met and are advantages of this system over current systems. The range of this system is limited, however, but it is believed that the next generation system should have greater range than the system used in this research. / Master of Science

<p dir="ltr">E-commerce, fueled by the digital revolution, has become a cornerstone of modern retail, driving demand for efficient last-mile logistics services. As online sales soar past $4 trillion, the need for streamlined, cost-effective delivery solutions is urgent, particularly in markets like China, where complex traffic conditions and high customer expectations complicate last-mile delivery. Autonomous driving technology offers a promising approach to meeting these challenges, enabling lower costs and improved delivery efficiency. However, cities such as Suzhou present unique obstacles for autonomous delivery vehicles (ADVs), with unpredictable traffic and diverse obstacles like pedestrians and bicycles. To tackle these issues, this research developed a high-capacity simulation platform capable of executing 300,000 scenarios weekly. It incorporates advanced routing algorithms, such as the Shortest Path Faster Algorithm (SPFA), and high-definition mapping (HDMap) for precise localization, supporting rigorous testing across varied urban logistics scenarios. The platform’s modular microservices architecture ensures scalability, enabling thorough validation of both software and hardware components in unmanned logistics vehicles. Findings demonstrate that the platform’s architecture, particularly its modular microservices and Protocol Buffers for data handling, optimizes the reliability and safety of autonomous systems in dense urban environments. Realistic scenario generation through SPFA routing and HDMap integration provides a robust environment for decision-making tests, contributing to enhanced operational stability and efficiency.</p><p dir="ltr">Practical Implications extend beyond autonomous driving, suggesting relevance to intelligent transportation systems, delivery drones, and smart cities. The platform’s high-throughput capacity underscores the importance of large-scale testing, enabling rapid development cycles with minimal dependence on real-world testing. This research provides a foundation for future improvements in simulation efficiency, scenario diversity, and applications across various sectors, paving the way for further advancements in autonomous technology.</p>

<p>Multiple target tracking (MTT) is a major area that occurs in a variety of real world systems. The problem involves the detection and estimation of an unknown number of targets within a scenario space given a sequence of noisy, incomplete measurements. The classic approach to MTT performs data association between individual measurements, however, this step is a computationally complex problem. Recently, a series of algorithms based on Random Finite Set (RFS) theory, that do not require data association, have been introduced. This thesis addresses some of the main deficiencies involved with RFS methods and derives key extensions to improve them for use in real world systems.\\</p> <p>The first contribution is the Weight Partitioned PHD filter. It separates the Probability Hypothesis Density (PHD) surface into partitions that represent the individual state estimates both spatially and proportionally. The partitions are labeled and propagated over several time steps to form continuous track estimates. Multiple variants of the filter are presented. Next, the Multitarget Multi-Bernoulli (MeMBer) filter is extended to allow the tracking of manoeuvring targets. A model state variable is incorporated into the filter framework to estimate the probability of each motion model. The standard implementations are derived. Finally, a new linear variant of the Intensity filter (iFilter) is presented. A Gaussian Mixture approximation provides more computationally efficient implementation of the iFilter.</p> <p>Each of the new algorithms are validated on simulated data using standard multitarget tracking metrics. In each case, the methods improve on several aspects of multitarget tracking in the real world.</p> / Doctor of Engineering (DEng)

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Thesis (M.S.)--Rochester Institute of Technology, 2008. / Typescript. Includes bibliographical references (leaves 62-63).