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Advisor(s)
Abstract(s)
No cenário atual de logística e gestão de armazéns, a eficiência no que leva a gestão de
stocks, reposição de materiais, organização do layout do armazém, previsões de encomendas
e consumos, entre outros aspetos, desempenha um papel fulcral. A transição para armazéns
inteligentes, visa usar tecnologias avançadas para otimizar estas operações, melhorando a
precisão e eficiência neste setor.
À medida que a indústria se movimenta em direção à automação avançada e armazéns inteligentes,
a aplicação de robôs em grandes superfícies retalhistas, representa uma abordagem
inovadora, não só na execução de tarefas precisas, rápidas e adaptativas, mas também na
contribuição para a redução do desgaste físico e da fadiga dos trabalhadores humanos, minimizando
o risco de lesões relacionadas ao trabalho que podem prevalecer o resto das suas
vidas. Para isto, a capacidade desses robôs em identificar e categorizar imagens é essencial
para a manipulação dos alimentos e a sua reposição de modo eficiente.
Nesse contexto, esta dissertação explora a implementação e inovação de algoritmos de categorização
de imagens, visando capacitar um robô despaletizador reorganizar as paletes de
alimentos que chegam a uma grande superfície retalhista.
In the current logistics and warehouse management landscape, efficiency in stock management, material replenishment, warehouse layout organization, order and consumption forecasting, among other aspects, plays a critical role. The transition to smart warehouses aims to leverage advanced technologies to optimize these operations, enhancing accuracy and efficiency in this sector. As the industry progresses towards advanced automation and smart warehouses, the deployment of robots in large retail spaces represents an innovative approach. These robots not only excel in executing precise, fast, and adaptive tasks but also contribute to reducing physical wear and fatigue among human workers, minimizing the risk of work-related injuries that may persist throughout their lives. For this purpose, the ability of these robots to identify and categorize images is crucial for the efficient handling and replenishment of goods, especially in the context of food management. In this context, this dissertation explores the implementation and innovation of image categorization algorithms, aiming to enable a despicking robot to reorganize food pallets arriving at a large retail store.
In the current logistics and warehouse management landscape, efficiency in stock management, material replenishment, warehouse layout organization, order and consumption forecasting, among other aspects, plays a critical role. The transition to smart warehouses aims to leverage advanced technologies to optimize these operations, enhancing accuracy and efficiency in this sector. As the industry progresses towards advanced automation and smart warehouses, the deployment of robots in large retail spaces represents an innovative approach. These robots not only excel in executing precise, fast, and adaptive tasks but also contribute to reducing physical wear and fatigue among human workers, minimizing the risk of work-related injuries that may persist throughout their lives. For this purpose, the ability of these robots to identify and categorize images is crucial for the efficient handling and replenishment of goods, especially in the context of food management. In this context, this dissertation explores the implementation and innovation of image categorization algorithms, aiming to enable a despicking robot to reorganize food pallets arriving at a large retail store.
Description
Keywords
Retail Neural networks Artificial intelligence Food detection Retalho Redes convolucionais Inteligência artificial Deteção de alimentos