% Title, Portuguese and English titles, and thesis year.
\newcommand{\authorname}{Luís Miguel\newline Martins Almeida}
\newcommand{\englishtitle}{Improved humanoid gait using learning-based analysis of a new wearable 3D force system}
\newcommand{\portuguesetitle}{Melhoramento da marcha humanóide utilizando uma análise baseada em aprendizagem de um novo sistema de força 3D}
\newcommand{\thesisyear}{2023}

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  \TEXT{}{Tese apresentada à Universidade de Aveiro para cumprimento dos requisitos necessários à obtenção do grau de Doutor em Eng. Mecânica, realizada sob a orientação científica do Doutor Vítor Manuel Ferreira dos Santos, Professor Associado com Agregação do Departamento de Engenharia Mecânica da Universidade de Aveiro e do Doutor João Paulo Morais Ferreira, Professor Coordenador do Instituto Politécnico de Coimbra.}
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  \TEXT{}{This work was supported by FCT through the doctoral grant
SFRH/BD/136680/2018 and Project PTDC/EEI-AUT/5141/2014. }
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 \TEXT{}{Dedicated to my parents and sister.}
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  \TEXT{\textbf{o júri~/~the jury\newline}}
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       {\textbf{Doutor José Fernando Ferreira Mendes}\newline {\small
        Professor Catedrático da Universidade de Aveiro}}
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       {\textbf{Doutor Manuel Fernando dos Santos Silva}\newline {\small
        Professor Coordenador, Instituto Superior de Engenharia do Porto}}
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  \TEXT{}
       {\textbf{Doutor Vítor Manuel Ferreira dos Santos}\newline {\small
        Professor Associado com Agregação, Universidade de Aveiro (orientador)}}
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  \TEXT{}
       {\textbf{Doutor Jorge Manuel Mateus Martins}\newline {\small
        Professor Associado, Universidade de Lisboa}}
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       {\textbf{Doutora Cristina Manuela Peixoto dos Santos}\newline {\small
        Professora Auxiliar com Agregação, Universidade do Minho}}
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  \TEXT{}
       {\textbf{Doutor Filipe Miguel Teixeira Pereira da Silva}\newline {\small
        Professor Auxiliar, Universidade de Aveiro}}
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  \TEXT{\textbf{acknowledgements}}
       {I am deeply grateful for the invaluable contributions of my supervisor, Professor Doctor Vítor Santos. His insightful discussions, unwavering guidance, and constant encouragement were crucial in shaping the research presented in this thesis.
Furthermore, I extend my appreciation to my co-supervisor, Professor Doctor João Ferreira, whose expertise and readiness to assist have greatly enriched the depth and breadth of this study. Their combined mentorship has been instrumental in the successful completion of this research work.
  My sincere gratitude to the Fundação para a Ciência e a Tecnologia (FCT) for funding the work presented in this thesis throught the PhD grant SFRH/BD/136680/2018. This research was also partially supported by FCT project A2HR with reference PTDC/EEIAUT/5141/2014. 
  I am grateful to the Institute of Electronics and Informatics Engineering of Aveiro (IEETA), the Department of Mechanical Engineering at Aveiro University, and the Institute of Systems and Robotics (ISR) at Coimbra University for their generous accommodation and provision of essential space and materials during my time of research.
  I would like to thank the Serbian Institute Mihajlo Pupin and Professor Doctor Aleksandar Rodić for graciously welcoming me into their laboratories at the inception of my research journey. I would also like to extend my appreciation to UC3M and Professor Doctor Santiago Martínez for granting me access to their humanoid robot and facilitating the commencement of my research into humanoid control.  
  Finally, but by no means the least, I want to wholeheartedly express my gratitude to my parents and sister for their devoted support. }
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  \TEXT{\textbf{Palavras-chave}}{Robô Humanóide, Locomoção, Sensores de Força, Inteligência Artificial, Controlo, Melhoramento}
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  \TEXT{\textbf{Resumo}}{A locomoção de robôs humanoides em superfícies escorregadias apresenta desafios significativos, exigindo soluções inovadoras para estabilidade e adaptabilidade. Este trabalho de doutoramento é composto por três partes integrantes que abordam coletivamente esses desafios, com cada parte construindo sobre a anterior. \newline Parte I explora a medição crítica das forças de reação do solo em robôs humanoides, fundamentais para a análise biomecânica e aplicações potenciais na reabilitação da marcha humana. Um sistema inovador de sapato instrumentado (ITshoe), económico e leve, é introduzido para a medição em tempo real das forças de reação, melhorando a nossa compreensão da locomoção do robô em diversas superfícies escorregadias. \newline Parte II aprofunda a tarefa vital do reconhecimento de superfícies para robôs humanoides que navegam em ambientes do mundo real. Um conjunto abrangente de dados, obtido através de sensores táteis de força em várias superfícies, serve como base. Técnicas de inteligência computacional, incluindo \textit{Artificial Neural Networks} (ANNs), \textit{Extreme Learning Machines} (ELMs) e \textit{Long Short-Term Memory} (LSTM) \textit{recurrent neural network}, são aplicadas para classificar diferentes superfícies. Experiências em tempo real são apresentadas para demonstrar a adequação das abordagens propostas para o problema de classificação múltipla abordado. \newline Parte III apresenta uma estratégia abrangente para melhorar a locomoção de robôs humanoides em superfícies escorregadias. Esta estratégia incorpora uma arquitetura de controlo baseada no \textit{Divergent-Component-of-Motion} (DCM) e um \textit{Embbed Yaw Controller} (EYC) baseado num algoritmo PID. Esta estratégia não aborda apenas o comportamento de deslize em superfícies de baixa fricção, mas também enfrenta padrões de locomoção não-lineares, mesmo em pisos não escorregadios. Experiências extensivas de locomoção demonstram melhorias significativas na estabilidade, redução do consumo de energia e duração da tarefa. \newline As descobertas apresentadas nesta tese fornecem evidências convincentes de uma locomoção adaptável de humanoides em superfícies escorregadias. Estas experiências destacam o potencial de um sistema \textit{wearable} para aprimorar a interpretação das forças de reação do solo em conjunto com técnicas de inteligência computacional. Além disso, demonstram a capacidade do sistema de adaptar o controlador do robô, em conjunto com um controlador PID, minimizando eficazmente o escorregamento em superfícies classificadas.}
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  \TEXT{\textbf{Keywords}}{Humanoid Robot, Locomotion, Force Sensors, Artificial Intelligence, Control, Improvement}
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  \TEXT{\textbf{Abstract}}{Humanoid robot locomotion on slippery surfaces poses significant challenges, demanding innovative solutions for stability and adaptability. This doctoral research comprises three integral parts that collectively address these challenges, with each part building upon the last.\newline Part I explores the critical measurement of Ground Reaction Forces (GRFs) in humanoid robots, crucial for biomechanical analysis and potential applications in human walking rehabilitation. A novel, cost-effective, and lightweight instrumented shoe (ITshoe) system is introduced for real-time GRF measurement, enhancing our understanding of robot locomotion across diverse slippery surfaces.\newline Part II delves into the vital task of surface recognition for humanoid robots navigating real-world environments. A comprehensive dataset, acquired through force tactile sensors on varius floors, serves as the foundation. Computational intelligence techniques, including artificial neural networks (ANNs), extreme learning machines (ELMs) and Long short-term memory (LSTM) recurrent neural network are applied to classify different surfaces. Real-time experiments are presented to demonstrate the suitability of the proposed approaches for the multi-classification problem addressed. \newline Part III presents a comprehensive strategy to enhance humanoid robot locomotion on slippery surfaces. It incorporates a Divergent-Component-of-Motion (DCM) based control architecture and an Embedded Yaw Controller (EYC) based on a PID algorithm. This strategy not only addresses slip behavior on low-friction surfaces but also tackles non-straight walking patterns, even on non-slippery floors. Extensive locomotion experiments demonstrate significant improvements in stability, reduced energy consumption, and task duration. \newline The findings presented in this thesis offer compelling evidence of adaptable humanoid locomotion on slippery surfaces. They underscore the potential of a wearable system to enhance the interpretation of ground reaction forces (GRFs) in conjunction with computational intelligence techniques. Moreover, they demonstrate the system's ability to adapt robot controller, coupled with a well-established PID controller, effectively minimizing slipperiness on classified surfaces.
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