\part{GENERAL DISCUSSION AND CONCLUSIONS}

\chapter{Discussion and Concluding Remarks}

\label{c6}

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\section{Discussion}
The general discussion of the thesis is structured into three major sections, each contributing to the overall objective of enhancing humanoid locomotion on slippery floors.

The first section (Chapter 2) is dedicated to the development of the ITShoe, a low-cost, wireless, and adaptable solution designed for measuring the net GRFs in humanoid robots. The ITShoe not only provides valuable insights into sensor positioning, but it also offers a simple architecture that streamlines data gathering and manipulation processes.

The primary objective of this section extends beyond addressing the needs of humanoid NAO robots navigating slippery floors. It also focuses on the seamless integration of the ITShoe with a wide range of humanoid robots. The development of the ITShoe was carefully considered with future adaptability in mind, allowing for easy customization by changing its outer design to match the foot design of each specific robot. This approach ensures that the ITShoe can be effortlessly integrated into different humanoid robots, enhancing their locomotion capabilities on real-world surfaces.

Through our experimental results, we demonstrate the practical application of the ITShoe in measuring ground reaction forces. While the precise magnitudes, particularly for horizontal forces that are challenging to quantify, may not be exact, we highlight the significance of observing variations in these forces. Even without resorting to mathematical approaches, visual examination allows us to perceive the differences across various types of floors. However, a significant challenge lies in the lack of a robust system for consistently placing the ITShoe on the robot's foot, ensuring precise pressure measurements. This issue represents a crucial area for future improvement, as similar-looking slippery floors can have varying effects on robot stability. Thus, detecting even small variations in the total GRFs using the ITShoe becomes essential for enhancing the overall performance and adaptability of humanoid robots on such surfaces.

The second part of the thesis (Chapter 3 and 4) explores and implements learning approaches to interpret the measured ground reaction forces. These approaches harness the power of machine learning techniques to extract meaningful insights and patterns from the force data.

Chapter 3 introduces the initial approach for classifying slippery floors. While we achieved successful classification results using neural networks on test datasets, we encountered challenges when applying the trained models to real robot scenarios with new data. Several factors contributed to this discrepancy. Firstly, the combination of gait parameters led to diverse force curves even within the same floor type. This complexity, compounded by the limited representation of the curves due to insufficient data, made accurate classification difficult. Although this particular research did not yield conclusive results for our specific problem, it paved the way for Chapter 4.

In Chapter 4, we adopted a more suitable network architecture, specifically a LSTM network, which is well-suited for analysing temporal data, such as the evolving force patterns during walking. By augmenting the dataset with a larger volume of data and utilizing a fixed gait, we achieved improved classification results in real-time scenarios. The utilization of the LSTM network, coupled with the increased dataset and controlled gait, proved instrumental in addressing the challenges faced in this section and obtaining more reliable classification outcomes. The real robot was used to conduct experiments on labelled floors with decreasing coefficients of friction, which were measured when the robot ITShoe came into contact with the floor.

The third part (chapter 5) is dedicated to enhancing the adaptability of a humanoid controller by incorporating specific tweaks and adjustments based on the insights gained from the previous chapters. This section presents a solution that optimizes humanoid efficiency on slippery floors. By combining the knowledge acquired from measuring the ground reaction forces and the application of learning approaches, the humanoid controller is fine-tuned to enhance its stability, agility, and overall performance in challenging and slippery environments. Notably, the implementation of an EYC with adaptable parameters based on the classification of different floor types has proven to significantly improve the defined stability criteria outlined in this chapter.

In summary, these three parts collectively contribute to the specific challenge of improving bipedal locomotion on slippery floors. 

\section{Concluding Remarks}

In conclusion, this research has made substantial progress in addressing the challenge of locomotion on various surfaces, with a particular focus on slippery floors. 

Firstly, the development of the ITshoes wearable force system aligns with the vision set forth in Hypothesis 1, enabling accurate measurements of ground reaction forces. These measurements have provided a robust foundation for subsequent analysis and control strategies, supported by extensive experimentation and data collection. The reliability and validity of the collected data have ensured that informed decisions can be made based on the insights derived from the ground reaction forces.

The development of the ITshoes has taken into account crucial factors such as cost-effectiveness, weight, adaptability, and user-friendliness. These considerations have been instrumental in ensuring that the ITshoes have the potential to be widely accessible and seamlessly integrated into various designs of humanoid robots.

Secondly, our research validates Hypothesis 2, as we effectively harnessed machine learning techniques to distinguish between different types of slippery surfaces. Leveraging the normal and tangential ground reaction forces data obtained from the ITshoes, our classification system provides the humanoid robot with real-time knowledge of floor conditions. This achievement empowers the robot to make informed decisions and adapt its locomotion accordingly.

Lastly, our work strongly supports Hypothesis 3, as we have seamlessly integrated the classified floor types into the humanoid controller, thereby significantly enhancing the robot's locomotion performance. The adapted controller, tailored to the specific characteristics of the NAO robot, enables the robot to navigate different slippery floors with remarkable ease.

In essence, our research has not only addressed the hypotheses outlined at the beginning but has also opened avenues for further advancements in the field of humanoid robotics, demonstrating the potential of wearable force systems and machine learning techniques in enhancing robot locomotion and adaptability.

Furthermore, our work introduces a novel approach involving four floors with varying coefficients of friction and the development of a real-time classification system to adapt the humanoid controller accordingly. Nevertheless, the overarching objective lies in extending this concept to detect transitions between different floor types. Such transitions would signify new frictional challenges and, consequently, necessitate the adaptation of controller parameters. This avenue of exploration represents a promising direction for future research, promising to further advance our understanding of bipedal locomotion on diverse surfaces.

\section{Future Directions}

To enhance the accuracy, reliability, and overall performance of ground reaction force measurements with the ITshoes, as well as the subsequent data information extraction and humanoid controller improvements, it is recommended to focus on the following future directions:

\begin{itemize}

\item \textbf{Design a secure attachment mechanism:}

Develop a mechanism that maintains consistent pressure between the robot's feet and the ITshoes. This ensures reliable and precise data collection. Incorporate adjustable straps or fasteners to create a firm connection, minimizing slippage or movement during locomotion. Use materials with suitable frictional properties to enhance grip and stability.

\item \textbf{Expand the dataset:}

Acquire more data to improve the accuracy and robustness of the floor classification system. Increase the diversity and quantity of data to enhance machine learning algorithms' effectiveness and generalization capabilities. Include a range of floors with varying frictional properties within the classified ones to better predict unfamiliar surfaces.

\item \textbf{Explore challenging floor conditions:}

Actively seek out and analyse new and unclassified floor conditions to enhance the system's capabilities. Gather data from diverse and unexplored floors to improve the classification model iteratively. This helps the system adapt to a wider range of floor conditions.

\item \textbf{Implement the developed controller in the real robot:}

Integrate the controller with the ITshoes' ground reaction force measurements, replacing simulated sensors with real-time data. Validate the integration through experimentation and testing in real-world scenarios. Evaluate the system's performance and adaptability to different types of floors and environmental conditions.

\end{itemize}

By implementing these steps, the accuracy, reliability, and adaptability of the ITshoes and the floor classification system can be significantly enhanced, leading to an improved controller for humanoid locomotion on slippery floors. This advancement will contribute to the development of more capable and agile humanoid robots that can navigate challenging slippery environments with increased stability and efficiency.

\section{Contributions}

In this section, the contributions made in this PhD thesis will be outlined, with each corresponding to a specific chapter (which comprises the actual scientific papers):

\begin{itemize}

\item \textbf{Instrumented system for Humanoid Gait Analysis}

The first contribution of this thesis is the development of an innovative instrumented system, as presented and explored in Chapter 2, scientific article \parencite{almeida2018novel}, which serves as the foundation for precise measurement and analysis of Ground Reaction Forces on various surfaces, particularly slippery ones.

\item \textbf{Computational Intelligence Techniques for Floor Classification}

The second contribution is detailed in Chapters 3 and 4, scientific articles \parencite{almeida2020learning,9598809}, where computational intelligence techniques are introduced to interpret force data and classify different types of slippery floors.

\item \textbf{Humanoid Adaptive Controller}

The third contribution encompasses the integration of a humanoid controller and additional adaptations and improvements, as detailed in Chapter 5, scientific article submitted. These enhancements significantly enhance control and responsiveness, especially on challenging surfaces.

\end{itemize}

Additionally, there is a separate contribution made to research focused on automating the NAO Robot's gait. In this context, a controller was presented that adjusts the robot's gait based on the analysis of human walking and its adaptation to slippery surfaces, scientific article \parencite{gui}.

Finally, the last contribution of this thesis centers on the adaptation of humanoid gait on slippery surfaces, marking the culmination of all previous efforts, as elaborated in Chapter 5 (submitted scientific article). This achievement exemplifies the synergistic effect resulting from the integration of the instrumented system, computational intelligence techniques, and the humanoid controller.

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