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检索条件"机构=Robotics Engineering Program"

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Augmented Grasps: Supernumerary Robotic Fingers and Their Practical Uses 9th

Augmented Grasps: Supernumerary Robotic Fingers and Their Pr...

引用

9th Latin American Congress on Biomedical engineering, CLAIB 2022 and 28th Brazilian Congress on Biomedical engineering, CBEB 2022

作者： Maguina, Alyssa N. Mio, Renato Laboratory of Biomechanics and Applied Robotics Pontifical Catholic University of Peru Lima Peru Biomedical Engineering Program PUCP-UPCH Universidad Peruana Cayetano Heredia Lima Peru

ISBN: (纸本)9783031494062

Research on supernumerary robotic fingers has grown in recent years, both for augmentation and assistance purposes. Unlike other wearable robotics, these devices do not replace a missing finger or assist finger movement, but provide an additional one to extend the workspace and enhance grasp capabilities in healthy patients. In addition, patients with physical disabilities can use them as assistive devices for basic grasping in activities of daily living. While research in this field moves forward consistently, a classification of the grasps performed with extra fingers has not yet been proposed. In the literature, we find a number of papers that focus on improving finger control but do not present which grips are similar to a natural five finger grasp. In this paper, a brief classification of supernumerary grasps will be made based on the most common ones developed in the literature. In addition, the usefulness of these additional robotic fingers will be described in order to encourage more researchers to opt for the development and improvement of these devices. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Wearable technology

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A Real-time Affordance-based Object Pose Estimation Approach for Robotic Grasp Pose Estimation

A Real-time Affordance-based Object Pose Estimation Approach...

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2023 International Conference on System Science and engineering, ICSSE 2023

作者： Wong, Shang-Wen Chiu, Yu-Chen Tsai, Chi-Yi Tamkang University Doctoral Program in Robotics College of Engineering New Taipei City Taiwan Tamkang University Department of Electrical and Computer Engineering New Taipei City Taiwan

ISBN: (纸本)9798350322941

This paper proposes a pose estimation system for robot grasping based on a novel Object Affordance Detection and Segmentation (OADS) network. The proposed system consists of four modules: (1) OADS network;(2) point cloud extraction;(3) object pose estimation;(4) grasp pose estimation. Based on the OADS network, the proposed system achieves affordance-based object pose estimation results. The proposed grasp pose estimation system is evaluated on a laboratory-made dual-arm robot. Experimental results show that the proposed system achieves high detection rate and high accuracy in affordance detection and segmentation tasks, leading to a high success rate in object grasping tasks with lab-made dual-arm robot. © 2023 IEEE.

关键词： Object detection

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

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

引用

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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DeepCat: A Deep Learning Approach to Understand Your Cat's Body Language 11

DeepCat: A Deep Learning Approach to Understand Your Cat's B...

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11th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Fawzy, Arwa Hassan, Ahmed Gad, Ahmed Zaky, Ahmed B. El-Hussieny, Haitham Alexandria Egypt program Alexandria Egypt Benha University Faculty of Engineering at Shoubra Electrical Engineering Department Egypt Egypt-Japan University of Science and Technology Department of Mechatronics and Robotics Engineering Alexandria Egypt

ISBN: (纸本)9798350369328

Pet owners experience difficulty in understanding their pets' body language and its implications for animal welfare, given that animals cannot utilize human speech to communicate their emotions and health conditions. However, previous experiments for analyzing cat behavior have demonstrated that cats are precisely expressive. DeepCat, a deep-learning approach developed in this study, translates cats' body language signals, enabling owners to discern their feline companions' intended messages and emotional states. Our DeepCat model was trained on a dataset comprising 10,000 cat images, implementing automatic labeling to track key features, including the tail, eyes, and mouth. Presented as a Flutter application, DeepCat can function everywhere, allowing owners to easily monitor their cats and make informed decisions in situations that require caution. This paper discusses the potential benefits and limitations of DeepCat and provides suggestions for future research in this domain. © 2023 IEEE.

关键词： Emotion Recognition

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MOSFET Based Active Rectifier Design in Wireless Capacitive Power Transfer for Electric Vehicles 1

MOSFET Based Active Rectifier Design in Wireless Capacitive ...

引用

1st International Conference on Smart Mechatronics, ICSMech 2024

作者： Dragon, Klemens Raphael Lie, Zener Sukra Muharam, Aam Ika, Juan Luciano Perdana, Muhamad Arjuna Amin Hapid, Abdul Binus Aso School of Engineering Bina Nusantara University Automotive and Robotics Program Computer Engineering Department Jakarta Indonesia Badan Riset dan Inovasi Nasional Research Center for Transportation Technology Jawa Barat Bandung Indonesia

ISBN: (纸本)9798350368918

While the rise of electric vehicles reflects a push for clean energy as envisioned by international agreements like the Paris Agreement, wireless charging's convenience can overcome limitations and hasten their adoption, making it a strategic step alongside regulations such Permenhub No. 15/2022 and SDG 9 'Industry, Innovation, and Infrastructure' for cleaner air and sustainable transportation. The aim is to convert the high frequency AC voltage from the coupling capacitor into a stable DC voltage. Further increase the system sensitivity to the load variations, by designing a MOSFET based active rectifier. By changing load parameters, the accuracy of PFC adaptation to the battery State of Charge (SOC) obtained is at 98% with 2% tolerance for errors. It is concluded that the research is said to be successful because it has met the parameters of general research hypothesis where the system is able to convert high frequency AC voltage into a stable DC voltage. © 2024 IEEE.

关键词： State of charge

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Online 3D Bin Packing for Novel Objects Based on Deep Reinforcement Learning

Online 3D Bin Packing for Novel Objects Based on Deep Reinfo...

引用

2023 International Conference on Advanced robotics and Intelligent Systems, ARIS 2023

作者： Chien, Shao-Yu Wong, Ching-Chang Tamkang University College of Engineering Doctoral Program in Robotics New Taipei City25137 Taiwan Tamkang University Electrical and Computer Engineering Department New Taipei City25137 Taiwan

ISBN: (纸本)9798350302714

In this paper, a method using deep reinforcement learning is proposed to deal with the 3D online bin packing problem. The packing objects are not limited to several specific or fixed cuboid objects, but are composed of more than a thousand objects and randomly generated cuboids, which make the trained policy network can handle novel unknown objects. In addition, the posture of the object in the box can be any angle, not limited to horizontal and vertical. In the proposed method, four voxel maps are used as inputs, and a Soft Actor-Critic (SAC) algorithm is used to train a policy network. On the other hand, in order to deal with various objects with irregular shapes, a packing task simulator with physics engine enable the policy network to learn the state of falling and stacking objects. In terms of training environment of deep reinforcement learning, the proposed method can be applied to boxes of different sizes because of the scalable image information. Moreover, a reward function and a training strategy with gradually increasing difficulty are proposed to effectively improve the learning of policy network. In terms of experimental results, the results on a random object bin packing task in a simulator illustrate the effectiveness of the proposed method. © 2023 IEEE.

关键词： Reinforcement learning

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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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