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2023 International Conference on Electrical Engineering and Informatics, ICEEI 2023

作者： Pranowo, Samuel Raymond Sukra, Zener Lie Asy'ari, Muhammad Zacky Paryasto, Marisa Binus Aso School of Engineering Bina Nusantara University Automotive and Robotics Program Computer Engineering Department Jakarta Indonesia Telkom University Computer Engineering Program Electrical Engineering Department Bandung Indonesia

ISBN: (纸本)9798350381290

Agriculture is a vital industry for the people of Indonesia, but there are several obstacles, including limited space in urban areas and inefficient conventional sorting and harvesting methods. Using computer vision to choose hydroponic lettuce that is ready to be harvested and a robotic arm to bring the selected lettuce yields, the produced system aims to solve this problem. The system's computer vision algorithms include color space conversion, intensity transformation, and an algorithm for following borders. This project's research methodology combines a quantitative approach with an experimental approach, which consists of conducting several trials on the built system. Several trials demonstrated that the computer vision software was able to accomplish the specified objectives. The average success rate of the developed computer vision system is 93%, while the average success rate of the robot arm is 85%. © 2023 IEEE.

关键词： Lettuce

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Design and Validation of a Prosthetic Foot Adaptable to Sagittal Plane Ground Irregularity

Design and Validation of a Prosthetic Foot Adaptable to Sagi...

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2024 International Symposium on 3D Analysis of Human Movement, 3DAHM 2024

作者： Lagones, Marco Abarca, Victoria E. Elias, Dante A. Biomedical Engineering Program Pontificia Universidad Católica del Perú Biomedical Engineering Program Universidad Peruana Cayetano Heredia Lima Peru Pontificia Universidad Católica del Perú Biomechanics and Applied Robotics Research Laboratory Lima Peru

ISBN: (纸本)9798331521738

Amputation involves the removal of a limb or part of it. In the case of the lower extremity, amputation affects the ankle joint and the metatarsophalangeal and intertarsal joints, improving the foot's adaptability on uneven terrain. Therefore, based on state-of-the-art technology that does not address the other joints of the foot to a large extent, a prosthetic foot has been developed that integrates the support structure of the foot through a foot arch with an extension analogous to the forefoot and an elastic element that mimics the plantar fascia that deforms against obstacles and provides stability to the prosthetic arch foot. The work addresses the design of the prosthetic foot from its design to its prototyping and validation, where the foot was shown to withstand the loads recommended for the P5 category of the ISO 22675 standard and to provide a return energy equal to 95.72%, 77.13%, and 90.21 %, in the heel strike, mid-stance, and toe-off stages, respectively. In addition to showing linear stiffness properties (66.95 kN /m) and torsional stiffness properties(2.05 kN.m/rad) in the forefoot analog element, values that are comparable with those recorded in commercial tests such as LP Variflex categories 7 and 8. © 2024 IEEE.

关键词： Arches

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Stability Guaranteed Actor-Critic Learning for Robots in Continuous Time 20

Stability Guaranteed Actor-Critic Learning for Robots in Con...

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20th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2023

作者： Pantoja-Garcia, Luis Parra-Vega, Vicente Garcia-Rodriguez, Rodolfo Robotics and Adv. Manufacturing CINVESTAV Saltillo Mexico Universidad Politecnica Metropolitana de Hidalgo Postgraduate Program in Aerospacial Engineering Tolcayuca Mexico

ISBN: (纸本)9798350306767

Actor-Critic (AC) architecture has the salient feature, for the plethora of Reinforcement Learning schemes, that two intertwining neural networks (NN) collaborate to deploy a motor learning mechanism that oversees and evaluates the action to control a dynamical system as the robot manipulator. Since such NNs can be studied as dynamical systems to learn how to control the robot dynamics with a given performance, it has finally paved the way to deal with stability analysis, unfortunately, few works have addressed it. In this paper, we propose a Critic-NN whose approximation of the value function substantiates the decision making mechanism that collaborates to tune the Actor-NN action (approximation of inverse dynamics). The novel proposed design of the adaptation of the neural weights yields Lyapunov stability that provides explicit conditions for an attractive invariant set that render a stable regime using a quite simple NN with one hidden layer. Numerical simulations show the performance of the proposed approach. In addition, robustness is analyzed when the robot is subject to Liptchitz disturbances, interestingly showing relaunching of the learning mechanism when needed. Finally, a discussion on dealing with asymptotic stability, robustness issues, and the learning mechanism from a reward provided by the expert user is addressed. © 2023 IEEE.

关键词： Asymptotic stability

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Damping Injection Learning for Robots with Actor-Critic Driven by Integral Sliding Manifolds 20

Damping Injection Learning for Robots with Actor-Critic Driv...

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20th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2023

作者： Tevera-Ruiz, Alejandro Garcia-Rodriguez, Rodolfo Parra-Vega, Vicente Robotics and Advanced Manufacture-CINVESTAV Saltillo Mexico Univ. Politécnica Metropolitana de Hidalgo Postgraduate Program in Aerospace Engineering Mexico

ISBN: (纸本)9798350306767

Reinforcement Learning (RL) is characterized by enforcing a reward collection mechanism that qualifies task performance aimed at optimizing a value function that tunes the action (control). In contrast to control theory, RL satisfies two metrics simultaneously: the task metric commonly, defined as the task error, and the temporal difference (TD) error. Thus, the RL has been empowered to learn how to fulfill control tasks based on a learning stage that collects rewards that evaluate a second metric of the robot's performance. In this way, including RL in control systems is an advantageous scheme for some critical tasks nowadays. Consequently, the stability can be guaranteed, largely missing in the RL's literature, to safe system operation.. In this paper, we propose an Actor-Critic (AC) scheme using the Critic Neural Network (C-NN) to approximate a value function using the TD error reduction, whereas the adaptive tuning of damping injection gain is provided by the Actor NN (A-NN) to enforce stability in closed-loop finally;achieving asymptotic stability with an underlying integral sliding mode loop. In this way, our proposal contributes to a novel model-free RL scheme that yields local exponential convergence with a smooth control action. Numeric simulations for a 2-DoF robot manipulator are presented and discussed under different conditions.. © 2023 IEEE.

关键词： Asymptotic stability

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Development of a Mixed-Reality Dual-User Training System for Robot Laparoscopic Surgery

Development of a Mixed-Reality Dual-User Training System for...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Chen, Ting-You Young, Kuu-Young Graduate Degree Program of Robotics National Yang Ming Chiao Tung University Hsinchu Taiwan National Yang Ming Chiao Tung University Department of Electrical Engineering Hsinchu Taiwan

ISBN: (纸本)9781665410205

As robot surgery becomes more popular these days, it consequently solicits a high demand on its training system. Currently, most of the systems are intended for personal training, while it may be more effective if an experienced expert can also be involved. It thus motivates us to propose a dual-user robotic surgical training system that can closely link the mentor and trainee together. The system is developed in a mixed-reality environment that integrates the information from both the virtual and real worlds. The haptic feedback and virtual fixtures are employed for assistance during skill transfer. The proposed system can assess the proficiency level of the trainee for the mentor to provide proper assistance during training. The experimental results demonstrate that the proposed system is capable of enhancing the trainee's ability for surgery. Especially, similar effectiveness is observed during comparative studies with direct hands-on guidance frequently adopted for skill transfer during sport training. © 2024 IEEE.

关键词： Robotic surgery

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Designing an End-to-End UAV System for Insulator Inspection Under Transmission Tower Environments

Designing an End-to-End UAV System for Insulator Inspection ...

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2024 International Conference on Electronics, Information, and Communication, ICEIC 2024

作者： Jeong, Jinyeong Park, Seongsu Lee, Sanghoon Youn, Donghyeon Kim, Min Jun School of Electrical Engineering KAIST Daejeon Korea Republic of KAIST Robotics Program Daejeon Korea Republic of KEPCO Research Institute Korea Electric Power Corporation Daejeon Korea Republic of

ISBN: (纸本)9798350371888

This paper proposes an unmanned aerial vehicle (UAV) equipped with a rigid contact module and two supplementary forward-direction motors, designed for the contact-based inspection of porcelain insulators. During the free-flight, this aerial robot is controlled by a geometric tracking controller. When approaching and contacting the insulators, the impedance controller generates enough force to sustain contact by using two forward-direction motors, while the geometric tracking controller maintains its orientation. This platform is validated through simulations, demonstrating its effectiveness in performing contact-based inspection tasks. © 2024 IEEE.

关键词： Inspection

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Continuous Adversarial Text Representation Learning for Affective Recognition

Continuous Adversarial Text Representation Learning for Affe...

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7th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2025

作者： Son, Seungah Saurez, Andres Har, Dongsoo Daejeon34141 Korea Republic of Robotics Program Daejeon34141 Korea Republic of

ISBN: (纸本)9798331507022

While pre-trained language models excel at semantic understanding, they often struggle to capture nuanced affective information critical for affective recognition tasks. To address these limitations, we propose a novel framework for enhancing emotion-aware embeddings in transformer-based models. Our approach introduces a continuous valence-arousal labeling system to guide contrastive learning, which captures subtle and multi-dimensional emotional nuances more effectively. Furthermore, we employ a dynamic token perturbation mechanism, using gradient-based saliency to focus on sentiment-relevant tokens, improving model sensitivity to emotional cues. The experimental results demonstrate that the proposed framework outperforms existing methods, achieving up to 15.5% improvement in the emotion classification benchmark, highlighting the importance of employing continuous labels. This improvement demonstrates that the proposed framework is effective in affective representation learning and enables precise and contextually relevant emotional understanding. © 2025 IEEE.

关键词： Emotion Recognition

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Actor-Critic Learning of Variable Damping Injection for Quadrotor Attitude Robust Control 20

Actor-Critic Learning of Variable Damping Injection for Quad...

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20th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2023

作者： Tevera-Ruiz, Alejandro Urzúa-Correa, Sergio E. Parra-Vega, Vicente Sanchez-Orta, Anand Garcia-Rodriguez, Rodolfo CINVESTAV Robotics and Adv. Manufacturing Saltillo Mexico Univ. Politécnica Metropolitana de Hidalgo Postgraduate Program in Aerospace Engineering Mexico

ISBN: (纸本)9798350306767

On the one hand, a class of model-free integral sliding modes requires damping injection to stabilize the closed-loop system to a certain bound, [1]. This technique has been extended to a variety of robots, including quadrotors. However, the damping matrix K value is difficult to calculate precisely. Henceforth, it is customary to overestimate it in practice, which is ritical for airborne robots, such as a lightweight quadrotor, which leads to a higher rate of dissipation with higher energy expenditure, which is highly inefficient. On the other hand, novel Reinforcement Learning (RL) schemes have also been developed for autotuning feedback gains, mainly using Actor-Critic (AC)-RL architectures. In this paper, we synergistically blend both schemes to learn the necessary K that enables the enforcement of integral sliding modes to control of the attitude of the quadrotors. Local robust exponential tracking is achieved without any pre-training of neural networks neither of adaptive parameters nor any knowledge of dynamics. A representative comparative simulation study is presented that shows the effectiveness of our scheme versus the fixed-gain case. The results show how critical it is K to complete the flight mission without wasting energy, then less battery consumption, an essential by-product of our formulation. As a future work, an experimental testbed is under development to test it in soft real-time, including its extension to position tracking. © 2023 IEEE.

关键词： Robust control

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Distributed accelerated primal-dual neurodynamic approaches for resource allocation problem

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Science China(Technological Sciences) 2023年第12期66卷 3639-3650页

作者： ZHAO You HE Xing YU JunZhi HUANG TingWen College of Electronic and Information Engineering Southwest UniversityChongqing 400715China State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China Science Program Texas A&M University at QatarDoha 2387Qatar

This paper investigates two distributed accelerated primal-dual neurodynamic approaches over undirected connected graphs for resource allocation problems(RAP)where the objective functions are generally *** the help of projection operators,a primal-dual framework,and Nesterov's accelerated method,we first design a distributed accelerated primal-dual projection neurodynamic approach(DAPDP),and its convergence rate of the primal-dual gap is O(1/(t^(2)))by selecting appropriate parameters and initial ***,when the local closed convex sets are convex inequalities which have no closed-form solutions of their projection operators,we further propose a distributed accelerated penalty primal-dual neurodynamic approach(DAPPD)on the strength of the penalty method,primal-dual framework,and Nesterov's accelerated *** on the above analysis,we prove that DAPPD also has a convergence rate O(1/(t^(2)))of the primal-dual *** with the distributed dynamical approaches based on the classical primal-dual framework,our proposed distributed accelerated neurodynamic approaches have faster convergence *** simulations demonstrate that our proposed neurodynamic approaches are feasible and effective.

关键词： accelerated primal-dual neurodynamic approaches RAP projection operators penalty method convergence rate O(1/(t^(2)))

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Recognition of Real-time Traffic Signals Using the Algorithm Fusion of On-board Graphic Chip-based Machine Learning and Deep Learning

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Journal of Institute of Control, robotics and Systems 2022年第2期28卷 88-94页

作者： Lee, Jaewook Baek, Seungbeom Choi, Eunji Moon, Heechang Industrial Convergence Interdepartmental Program Autonomous Vehicle·Intelligent Robotics Hongik University Korea Republic of Mechanical and System Design Engineering Hongik University Korea Republic of

The recognition of traffic light in unmanned autonomous driving is one of the key functions. In general, to achieve this goal, deep learning and image processing technology are mainly used in autonomous driving industry. Both technologies have high recognition rates alone, but misrecognition can occur because of interference of the environment. misrecognitions are considered important problem because it may lead to big accidents. In this paper, we suggest using machine learning based on LabVIEW and deep running grounded in Python to increase recognition rate at intersection. By using both technologies, we can minimize the impact of the environment on traffic light recognition and reduce misrecognition. © ICROS 2022.

关键词： Image processing

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