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Path planning and optimization of autonomous mobile robots in a logistics warehouse scenario

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Resumo:The use of robots in manufacturing and storage environments is increasing every day. Their benefits within a company’s logistics and their speed in performing tasks make them more productive in terms of carrying out tasks directed towards a common goal. With this, the main objective of this work is to optimize the path planning of autonomous mobile robots (AMRs) for later use in various applications, focusing on logistics warehouse scenarios. The work involves the analysis and improvement of algorithms (and their variants) for control and movement of AMRs, focusing on reducing the execution time of the algorithm itself. Variants of the A* algorithm were implemented and used in path planning problems, aiming to find the shortest path for robots, even in environments with obstacles. The adopted methodology includes a literature review of algorithm models for use in the context of this work, as well as the analysis of algorithm variants found for adaptation and use in the proposed scenario. After analyzing them, the obtained results were checked and compared for two different map models. The performance of the algorithm depends on the functions programmed into it and the steps it must go through to get from the start point to the end point. The greater the number of functions in the algorithm, the longer its execution time will be. The generation of graphs to visualize the algorithm’s result requires most of this execution time, as it uses visual resources to show the user the proper functioning of the tested algorithm. The analyses and tests conducted revealed that, although both algorithms have quick response times suitable for use in scenarios with entry warehouses, exit warehouses, and obstacles along the route, the Sakai A* algorithm variant proved to be more efficient due to its simple structure, fewer steps within the algorithm, and agile response time in generating optimal solutions. This had a shorter execution time compared to the Mehdi A* algorithm variant, which, although good, proved to be slower in obtaining the optimal path.
Autores principais:Cangussu, Gabriel Henrique Silva
Assunto:A* AMRs Path planning
Ano:2025
País:Portugal
Tipo de documento:dissertação de mestrado
Tipo de acesso:acesso aberto
Instituição associada:Instituto Politécnico de Bragança
Idioma:inglês
Origem:Biblioteca Digital do IPB

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