Yanhong Peng 교수

The fusion of large language models and robotic systems has introduced a transformative paradigm in human–robot interaction, offering unparalleled capabilities in natural language understanding and task execution. This review paper offers a comprehensive analysis of this nascent but rapidly evolving domain, spotlighting the recent advances of Large Language Models (LLMs) in enhancing their structures and performances, particularly in terms of multimodal input handling, high-level reasoning, and

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

Given the increasing demand for efficient fluid dynamics analysis, this study introduces a novel machine learning-based approach for predicting fluidic flow rate and reverse flow rate. Leveraging Multilayer Perceptron and Catboost models, the study aims to advance the analysis and prediction of flexible rectifier behavior. A dataset comprising multiple variables, including pressure, valve dimensions, and flow rate, was utilized to train and test the models. The study successfully demonstrated su

In recent years, functional fluidic and gas electrohydrodynamic (EHD) pumps have received considerable attention due to their remarkable features, such as simple structure, quiet operation, and energy-efficient utilization. EHD pumps can be applied in various industrial applications, including flow transfer, thermal management, and actuator drive. In this paper, the authors reviewed the literature surrounding functional fluidic and gas EHD pumps regarding the following aspects: the initial obser

In recent years, there has been extensive utilization of actuators driven by artificial muscles in wearable devices. However, the muscle distribution configurations of most wearable devices have been specifically designed and are difficult to generalize. Consequently, wearable devices that allow direct installation of actuators onto existing clothing are better suited for a wider range of application scenarios. This letter presents the development and evaluation of Funabot-Suit, a human muscle c

In recent years, deep learning techniques for processing 3D point cloud data have seen significant advancements, given their unique ability to extract relevant features and handle unstructured data. These techniques find wide-ranging applications in fields like robotics, autonomous vehicles, and various other computer-vision applications. This paper reviews the recent literature on key tasks, including 3D object classification, tracking, pose estimation, segmentation, and point cloud completion.

This study presents an innovative approach in soft robotics, focusing on an inchworm-inspired robot designed for enhanced transport capabilities. We explore the impact of various parameters on the robot’s performance, including the number of activated sections, object size and material, supplied air pressure, and command execution rate. Through a series of controlled experiments, we demonstrate that the robot can achieve a maximum transportation speed of 8.54 mm/s and handle loads exceeding 100

Flexible actuators are popular in the consumer and medical fields because of their flexibility and compliance. However, they are typically difficult to model because of their viscoelasticity and nonlinearity. This letter proposes a method for correcting the deformation of the simulated flexible robots to make it similar to the deformation of real robots using point clouds by deep learning. Long short-term memory (LSTM) can simulate the next frame of actuator deformation from the previous frames

Haptic feedback systems play a critical role in enriching the user experience in human-robot interaction. However, existing devices designed for evoking haptic sensations often face limitations owing to their low degree of freedom of deformation. In this study, we introduce the Funabot-Sleeve, a haptic device based on McKibben artificial muscles, and investigate its potential to evoke a range of haptic sensations using both steady-state and transient air pressure patterns. Our investigation exam

With the rapid development of blockchain technology, consensus algorithms have become a significant research focus. Practical Byzantine Fault Tolerance (PBFT), as a widely used consensus mechanism in consortium blockchains, has undergone numerous enhancements in recent years. However, existing review studies primarily emphasize broad comparisons of different consensus algorithms and lack an in-depth exploration of PBFT optimization strategies. The lack of such a review makes it challenging for r

In recent years, functional fluidic electrohydrodynamic (EHD) pumps have attracted considerable attention due to their remarkable features, such as simple structure, quiet operation, and energy-efficient utilization. EHD liquid pumps can be used in various industrial applications, like flow transfer, thermal management, and actuator drive. In this paper, the author reviewed EHD liquid pump research in these specific aspects: the first observation of the EHD effect, its mathematical modeling, and

In recent years, rapid progress in autonomous driving has been achieved through advances in sensing, control, and earning. However, as the complexity of traffic scenarios increases, ensuring safe interaction among vehicles remains a formidable challenge. Recent works combining artificial potential fields (APFs) with game-theoretic methods have shown promise in modeling vehicle interactions and avoiding collisions. However, these approaches often suffer from overly conservative decisions or fail

In recent years, metaheuristic algorithms have garnered significant attention for their efficiency in solving complex optimization problems. However, their performance critically depends on maintaining a balance between global exploration and local exploitation; a deficiency in either can result in premature convergence to local optima or low convergence efficiency. To address this challenge, this paper proposes an enhanced ivy algorithm guided by a particle swarm optimization (PSO) mechanism, r

(1) Achievement motivation can significantly and positively predict pre-service teachers' educational practice ability; (2) Achievement motivation can indirectly affect pre-service teachers' educational practice ability through the mediating effects of professional identity and learning engagement; (3) Professional identity and learning engagement play a chain mediated role in the impact of achievement motivation on pre-service teachers' educational practice ability.