Power, Control, and Data Acquisition Systems for Rectal Simulator Integrated with Soft Pouch Actuators

Fecal incontinence (FI) is a significant health issue with various underlying causes. Research in this field is limited by social stigma and the lack of effective replication models. To address these challenges, we developed a sophisticated rectal simulator that integrates power, control, and data acquisition systems with soft pouch actuators. The system comprises four key subsystems: mechanical, electrical, pneumatic, and control and data acquisition. The mechanical subsystem utilizes common materials such as aluminum frames, wooden boards, and compact structural components to facilitate the installation and adjustment of electrical and control components. The electrical subsystem supplies power to regulators and sensors. The pneumatic system provides compressed air to actuators, enabling the simulation of FI. The control and data acquisition subsystem collects pressure data and regulates actuator movement. This comprehensive approach allows the robot to accurately replicate human defecation, managing various feces types including liquid, solid, and extremely solid. This innovation enhances our understanding of defecation and holds potential for advancing quality-of-life devices related to this condition.

Bio-inspired circular soft actuators for simulating defecation process of human rectum

Soft robots have found extensive applications in the medical field, particularly in rehabilitation exercises, assisted grasping, and artificial organs. Despite significant advancements in simulating various components of the digestive system, the rectum has been largely neglected due to societal stigma. This study seeks to address this gap by developing soft circular muscle actuators (CMAs) and rectum models to replicate the defecation process. Using soft materials, both the rectum and the actuators were fabricated to enable seamless integration and attachment. We designed, fabricated, and tested three types of CMAs and compared them to the simulated results. A pneumatic system was employed to control the actuators, and simulated stool was synthesized using sodium alginate and calcium chloride. Experimental results indicated that the third type of actuator exhibited superior performance in terms of area contraction and pressure generation. The successful simulation of the defecation process highlights the potential of these soft actuators in biomedical applications, providing a foundation for further research and development in the field of soft robotics.

Design, modeling, and characteristics of ringshaped robot actuated by functional fluid

The controlled actuation of hydraulic and pneumatic actuators has unveiled fresh and thrilling opportunities for designing mobile robots with adaptable structures. Previously reported rolling robots, which were powered by fluidic systems, often relied on complex principles, cumbersome pump and valve systems, and intricate control strategies, limiting their applicability in other fields. In this investigation, we employed a distinct category of functional fluid identified as Electrohydrodynamic (EHD) fluid, serving as the pivotal element within the ring-shaped actuator. A short stream of functional fluid is placed within a fluidic channel and is then actuated by applying a direct current voltage aiming at shifting the center of mass of the robot and finally pushed the actuator to roll. We designed a ring-shaped fluidic robot, manufactured it using digital machining methods, and evaluated the robot's characteristics. Furthermore, we developed static and dynamic models to analyze the oscillation and rolling motion of the ring-shaped robots using the Lagrange method. This study is anticipated to contribute to the expansion of current research on EHD flexible actuators, enabling the realization of complex robotic systems.

Large Language Model-empowered multimodal strain sensory system for shape recognition, monitoring, and human interaction of tensegrity

A tensegrity-based system is a promising approach for dynamic exploration of uneven and unpredictable environments, particularly, space exploration. However, implementing such systems presents challenges in terms of intelligent aspects: state recognition, wireless monitoring, human interaction, and smart analyzing and advising function. Here, we introduce a 6-strut tensegrity integrate with 24 multimodal strain sensors by leveraging both deep learning model and large language models to realize smart tensegrity. Using conductive flexible tendons assisted by long short-term memory model, the tensegrity achieves the self-shape reconstruction without extern sensors. Through integrating the flask server and gpt-3.5-turbo model, the tensegrity autonomously enables to send data to iPhone for wireless monitoring and provides data analysis, explanation, prediction, and suggestions to human for decision making. Finally, human interaction system of the tensegrity helps human obtain necessary information of tensegrity from the aspect of human language. Overall, this intelligent tensegrity-based system with self-sensing tendons showcases potential for future exploration, making it a versatile tool for real-world applications.

Predictive Modeling of Flexible EHD Pumps using Kolmogorov-Arnold Networks

We present a novel approach to predicting the pressure and flow rate of flexible electrohydrodynamic pumps using the Kolmogorov-Arnold Network. Inspired by the Kolmogorov-Arnold representation theorem, KAN replaces fixed activation functions with learnable spline-based activation functions, enabling it to approximate complex nonlinear functions more effectively than traditional models like Multi-Layer Perceptron and Random Forest. We evaluated KAN on a dataset of flexible EHD pump parameters and compared its performance against RF, and MLP models. KAN achieved superior predictive accuracy, with Mean Squared Errors of 12.186 and 0.001 for pressure and flow rate predictions, respectively. The symbolic formulas extracted from KAN provided insights into the nonlinear relationships between input parameters and pump performance. These findings demonstrate that KAN offers exceptional accuracy and interpretability, making it a promising alternative for predictive modeling in electrohydrodynamic pumping.

Machine-Learning-Enhanced Soft Robotic System Inspired by Rectal Functions for Investigating Fecal incontinence

Fecal incontinence, arising from a myriad of pathogenic mechanisms, has attracted considerable global attention. Despite its significance, the replication of the defecatory system for studying fecal incontinence mechanisms remains limited largely due to social stigma and taboos. Inspired by the rectum's functionalities, we have developed a soft robotic system, encompassing a power supply, pressure sensing, data acquisition systems, a flushing mechanism, a stage, and a rectal module. The innovative soft rectal module includes actuators inspired by sphincter muscles, both soft and rigid covers, and soft rectum mold. The rectal mold, fabricated from materials that closely mimic human rectal tissue, is produced using the mold replication fabrication method. Both the soft and rigid components of the mold are realized through the application of 3D-printing technology. The sphincter muscles-inspired actuators featuring double-layer pouch structures are modeled and optimized based on multilayer perceptron methods aiming to obtain high contractions ratios (100%), high generated pressure (9.8 kPa), and small recovery time (3 s). Upon assembly, this defecation robot is capable of smoothly expelling liquid faeces, performing controlled solid fecal cutting, and defecating extremely solid long faeces, thus closely replicating the human rectum and anal canal's functions. This defecation robot has the potential to assist humans in understanding the complex defecation system and contribute to the development of well-being devices related to defecation.

Integration of Large Language Models in Control of EHD Pumps for Precise Color Synthesis

This paper presents an innovative approach to integrating Large Language Models (LLMs) with Arduino-controlled Electrohydrodynamic (EHD) pumps for precise color synthesis in automation systems. We propose a novel framework that employs fine-tuned LLMs to interpret natural language commands and convert them into specific operational instructions for EHD pump control. This approach aims to enhance user interaction with complex hardware systems, making it more intuitive and efficient. The methodology involves four key steps: fine-tuning the language model with a dataset of color specifications and corresponding Arduino code, developing a natural language processing interface, translating user inputs into executable Arduino code, and controlling EHD pumps for accurate color mixing. Conceptual experiment results, based on theoretical assumptions, indicate a high potential for accurate color synthesis, efficient language model interpretation, and reliable EHD pump operation. This research extends the application of LLMs beyond text-based tasks, demonstrating their potential in industrial automation and control systems. While highlighting the limitations and the need for real-world testing, this study opens new avenues for AI applications in physical system control and sets a foundation for future advancements in AI-driven automation technologies.