Diseño de un sistema de control para AGVs a utilizar en el marco de una emulación en un sistema de almacenamiento y recuperación automática (AS/RS)

En las últimas décadas, los sistemas tecnológicos han sido influyentes en la competitividad de las empresas. Estos sistemas redujeron el tiempo de ejecución. La logística automatizada, específicamente en los sistemas de almacenamiento y recolección, ha brindado una ventaja de respuesta pronta al cliente, teniendo un mejor funcionamiento dentro de las tareas de una compañía. En la industria manufacturera moderna, el tiempo empleado en el transporte de material representa casi el 80-90 % del tiempo total de verificación, incurriendo en más del 30 % del costo total del proceso. Por esta razón, este trabajo propone el diseño de un sistema de control para AGVs a utilizar en el marco de una emulación en un sistema de almacenamiento y recuperación automática (AS/RS), que realiza la recolección de los paquetes de manera que optimiza la recolección de paquetes y de mismo modo minimice el tiempo de respuesta en casos de perturbación. Para la modelación, se empleará una arquitectura de control basada en el paradigma multi-agentes, y será parametrizado con un modelo de decisión predictivo-reactivo. Específicamente, se utilizarán una herramienta de optimización para la programación de recolección, así como una modelación de control reactiva para responder a posibles perturbaciones. Se considerarán diferentes escenarios para validar la respuesta del modelo ante cada situación, haciendo una emulación del proceso en un ambiente virtual.In recent decades, technological systems have played an influential role in companies' competitiveness. These systems have reduced execution time. Automated logistics, specifically in storage and collection systems, has provided customers with prompt responses, while having a better performance within the tasks of a company. In the modern manufacturing industry, the time spent on material transport represents almost 80-90% of the total time of verification, incurring more than 30% of the total cost of the process. For this reason, this paper proposes the design of a control system for AGVs to be used in the framework of an emulation in an automatic storage and retrieval system (AS/RS), which carries out the collection of packages in a way that optimizes their collection, concurring with minimization in the response time in cases of disturbance. For the emulation, a control architecture based on the multi-agent paradigm will be used, and it will be parameterized with a predictive-reactive decision model. Specifically, an optimization tool will be used for collection scheduling, as well as reactive control modeling to respond to possible disturbances. Different scenarios will be considered to validate the response of the model to each situation, emulating the process in a virtual environment.Ingeniero (a) IndustrialPregrad

Task Assignment and Path Planning for Autonomous Mobile Robots in Stochastic Warehouse Systems

The material handling industry is in the middle of a transformation from manual operations to automation due to the rapid growth in e-commerce. Autonomous mobile robots (AMRs) are being widely implemented to replace manually operated forklifts in warehouse systems to fulfil large shipping demand, extend warehouse operating hours, and mitigate safety concerns. Two open questions in AMR management are task assignment and path planning. This dissertation addresses the task assignment and path planning (TAPP) problem for autonomous mobile robots (AMR) in a warehouse environment. The goals are to maximize system productivity by avoiding AMR traffic and reducing travel time. The first topic in this dissertation is the development of a discrete event simulation modeling framework that can be used to evaluate alternative traffic control rules, task assignment methods, and path planning algorithms. The second topic, Risk Interval Path Planning (RIPP), is an algorithm designed to avoid conflicts among AMRs considering uncertainties in robot motion. The third topic is a deep reinforcement learning (DRL) model that is developed to solve task assignment and path planning problems, simultaneously. Experimental results demonstrate the effectiveness of these methods in stochastic warehouse systems