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International Conference on Ubiquitous Robots (UR)

作者： Junhwan Choi Eunseok Song Hyunkyu Park Kyoungchul Kong Jung Kim Department of Mechanical Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea Robotics Program Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea

Exoskeletons have been developed to increase joint performance by providing assistance torque. Soft exosuits, which use light and soft materials for human-robot interaction, are a growing trend in the orthosis. However, wearable motion capture technology for biomechanical data collection remains a challenge. Traditional measurement systems such as motion capture camera (Mocap), inertial measurement units (IMUs), and ground reaction force and moment (GRF&M) are computationally intensive and collected in restricted laboratory environments. To address this challenge, we propose a sensing brace to monitor knee joint kinetics using an elastic strain sensor and a pressure-based mechanomyography (pMMG) sensor. The pMMG sensor improves comfort, reliability, linearity, and durability, and does not need to attach directly to the skin. Linear regression model of knee angle and moment are conducted with high reliability which showed $p-value < 0.05$ and $R^{2}$ is over 0.98. We used a linear regression model to estimate knee angle and torque during squatting with $\text{RMSE} =4.4\ deg$ in knee angle estimation and $\text{RMSE}=3.4\ Nmm/kg$ in knee moment estimation.

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A Slices Perspective for Incremental Nonparametric Inference in High Dimensional State Spaces

A Slices Perspective for Incremental Nonparametric Inference...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Moshe Shienman Ohad Levy-Or Michael Kaess Vadim Indelman Technion Autonomous Systems Program (TASP) Technion - Israel Institute of Technology Haifa Israel Robotics Institute Carnegie Mellon University Pittsburgh PA USA Department of Aerospace Engineering Technion - Israel Institute of Technology Haifa Israel

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

We introduce an innovative method for incremental nonparametric probabilistic inference in high-dimensional state spaces. Our approach leverages slices from highdimensional surfaces to efficiently approximate posterior distributions of any shape. Unlike many existing graph-based methods, our slices perspective eliminates the need for additional intermediate reconstructions, maintaining a more accurate representation of posterior distributions. Additionally, we propose a novel heuristic to balance between accuracy and efficiency, enabling real-time operation in nonparametric scenarios. In empirical evaluations on synthetic and real-world datasets, our slices approach consistently outperforms other state-of-the-art methods. It demonstrates superior accuracy and achieves a significant reduction in computational complexity, often by an order of magnitude.

关键词： Surface reconstruction Accuracy Shape Probabilistic logic Real-time systems Iterative methods Computational complexity Intelligent robots

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End-to-End Reinforcement Learning for Torque Based Variable Height Hopping

arXiv

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arXiv 2023年

作者： Soni, Raghav Harnack, Daniel Isermann, Hauke Fushimi, Sotaro Kumar, Shivesh Kirchner, Frank Department of Electronics Engineering Indian Institute of Technology Banaras Hindu University Varanasi India DFKI GmbH Robotics Innovation Center Bremen Germany Undergraduate Course Program of Mechanical and Systems Engineering Kyoto University Kyoto Japan

Legged locomotion is arguably the most suited and versatile mode to deal with natural or unstructured terrains. Intensive research into dynamic walking and running controllers has recently yielded great advances, both in the optimal control and reinforcement learning (RL) literature. Hopping is a challenging dynamic task involving a flight phase and has the potential to increase the traversability of legged robots. Model based control for hopping typically relies on accurate detection of different jump phases, such as lift-off or touch down, and using different controllers for each phase. In this paper, we present a end-to-end RL based torque controller that learns to implicitly detect the relevant jump phases, removing the need to provide manual heuristics for state detection. We also extend a method for simulation to reality transfer of the learned controller to contact rich dynamic tasks, resulting in successful deployment on the robot after training without parameter tuning. © 2023, CC BY-NC-SA.

关键词： Reinforcement learning

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Exploring Multimodal Gait Rehabilitation and Assistance through an Adaptable Robotic Platform

Exploring Multimodal Gait Rehabilitation and Assistance thro...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Sophia Otálora Sergio D. Sierra M. Felipe Ballén-Moreno Marcela Múnera Carlos A. Cifuentes Graduate Program of Electrical Engineering Federal University of Espirito Santo Vitoria Brazil Bristol Robotics Laboratory University of the West of England Bristol UK Robotics & Multibody Mechanics (R&MM) Reasearch Group Vrije Universiteit Brussel Brussels Belgium Flanders Make Brussels Belgium Biomedical Engineering Program Colombian School of Engineering Julio Garavito Bogotá D.C. Colombia The School of Engineering Science and Technology Universidad del Rosario Bogotá D.C. Colombia

Lower-limb exoskeletons and smart walkers are robotic devices to assist patients in regaining their autonomy after a stroke. The integration of these devices enables gait rehabilitation and functional compensation, promoting natural over-ground walking. This article presents the Adaptable Robotic Platform for Gait Rehabilitation and Assistance (AGoRA V2 platform), which integrates the new AGoRA V2 Smart Walker and the AGoRA V2 unilateral lower-limb exoskeleton. It was evaluated with 14 healthy subjects using physiological and kinematic variables and a perception assessment. The study entailed four conditions: Without exoskeleton (WOE), With Exoskeleton (WE&T), With Walker (WW), and With Platform (WP). Results indicate a reduction in the muscle activity of the Rectus Femoris (18%) and Vastus Lateralis (15%), comparing WE&T and WP, as well as walking without any device (WOE) and using any robotic device (WE&T, WW, WP). Results suggest the importance of combining the exoskeleton with the robotic walker and the assistance of each device independently. Moreover, using the complete platform induces slower gait patterns than the walker, as the mean impulse force and linear velocity decrease by 42% and 44%, respectively. These results demonstrate that the platform contributes to safety, and improvements in gait parameters and muscular activity, indicating the system's potential to act as a modular device according to users' conditions and therapeutic goals.

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Design, Field Evaluation, and Traffic Analysis of a Competitive Autonomous Driving Model in a Congested Environment

arXiv

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arXiv 2022年

作者： Lee, Daegyu Seong, Hyunki Kang, Gyuree Han, Seungil Shim, D. Hyunchul Yoon, Yoonjin Daejeon Korea Republic of Robotics Program KAIST Daejeon Korea Republic of Civil and Environmental Engineering KAIST Daejeon Korea Republic of

Recently, numerous studies have investigated cooperative traffic systems using the communication among vehicle-to-everything (V2X). Unfortunately, when multiple autonomous vehicles are deployed while exposed to communication failure, there might be a conflict of ideal conditions between various autonomous vehicles leading to adversarial situation on the roads. In South Korea, virtual and real-world urban autonomous multi-vehicle races were held in March and November of 2021, respectively. During the competition, multiple vehicles were involved simultaneously, which required maneuvers such as overtaking low-speed vehicles, negotiating intersections, and obeying traffic laws. In this study, we introduce a fully autonomous driving software stack to deploy a competitive driving model, which enabled us to win the urban autonomous multi-vehicle races. We evaluate module-based systems such as navigation, perception, and planning in real and virtual environments. Additionally, an analysis of traffic is performed after collecting multiple vehicle position data over communication to gain additional insight into a multi-agent autonomous driving scenario. Finally, we propose a method for analyzing traffic in order to compare the spatial distribution of multiple autonomous vehicles. We study the similarity distribution between each team's driving log data to determine the impact of competitive autonomous driving on the traffic environment. © 2022, CC BY-SA.

关键词： Autonomous vehicles

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Water Quality Index (WQI) Prediction Using Machine Learning Algorithms

Water Quality Index (WQI) Prediction Using Machine Learning ...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Kunyanuth Kularbphettong Phanu Waraporn Nareenart Raksuntorn Rujijan Vivhivanives Chanyapat Sangsuwon Chongrag Boonseng Computer Science Program Suan Sunandha Rajabhat University Bangkok Thailand Robotics Engineering Program Suan Sunandha Rajabhat University Bangkok Thailand Faculty of Science and Technology Suan Sunandha Rajabhat Universit Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798350361513

ISBN: (纸本)9798350372304

Water resources used by human activities ranges typically from personal and household, agricultural, industrial, recreational to environmental pursuits. The effects of these water utilizations are actually of great concerns by many due to various threats created by human functions and the nature itself, for instance, climate change, pollution, scarcity, and even conflicts. To mitigate these threats, implementation of water quality management based on recognized standards and guidelines not only will they provide solid framework and benchmark used in relation to the assessment of the water quality but will also enable the identification of corresponding classification indicated by the water quality index (WQI) pertinent and relevant to the surface water dataset. This paper aims at applying selected predictive modeling techniques that are highly optimized for use in semi-automating the work of the water quality classification (WQC) and the water quality index (WQI) that subsequently can be used in assisting the planning, problem-solving and/or decision-making processes. The preliminary results obtained are quite satisfactory as follow: predicting WQI using neural network model (NN) outperforms both the Multiple Linear Regression (MLR) and the Support Vector Machine (SVM) based on a mean absolute error (MAE) lower than the two models and predicting WQC using SVM, and ANN models based on accuracy score with SVM returns a favorable accuracy score higher than two others.

关键词： Water resources Water quality Indexes Classification algorithms Support vector machines Linear regression Climate change Predictive models Environmental monitoring Quality assessment Surface treatment

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A Novel Adaptive Actor-Critic Reinforcement Learning Controller for Constrained Robots

A Novel Adaptive Actor-Critic Reinforcement Learning Control...

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IEEE International Conference on Automatica (ICA-ACCA)

作者： Luis Pantoja-Garcia Rodolfo Garcia-Rodriguez Vicente Parra-Vega Robotics and Advanced Manufacturing Department Research Center for Advanced Studies (CINVESTAV) Saltillo Coah Mexico Aeronautical Engineering Program and Postgraduate Program in Aerospacial Engineering Universidad Politecnica Metropolitana de Hidalgo Tolcayuca Mexico

ISBN: (纸本)9781665494090

Force-position control for robot manipulators has become a basic control regime for advanced applications such as interaction or cooperative tasks that imply contact to environment or external object. Thus, for many applications environmental information is evaluated continuously to generate adequate control actions to complete the task. There has been proposed techniques such as adaptive or intelligent control to cover some of these requirements, however it seems insufficient given the uncertainty involved when dealing with the environment. The emergence of deep learning has recently renewed interest in these tasks from the perspective of reinforcement learning algorithms. In this paper, an actor-critic scheme is proposed for simultaneous control of contact force and joint positions while moving along an environmental rigid surface. The critic stage evaluates information of the environment through an extended error manifold, trying to minimize the cumulative future rewards by a neural network. The actor stage, which receives environment information from the critic stage, is used to approximate inverse robot dynamics. Together with two continuous orthogonal sliding PID controllers gives rise to simultaneous tracking of position and force, with stability analysis. Numerical simulations show the performance of the proposed approach under several conditions.

关键词： Upper bound Uncertainty Force Reinforcement learning Robot sensing systems Stability analysis Trajectory

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Reconfigurable Inflated Soft Arms

Reconfigurable Inflated Soft Arms

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IEEE International Conference on robotics and Automation (ICRA)

作者： Nam Gyun Kim Jee-Hwan Ryu Robotics Program Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea Department of Civil and Environmental Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea

Inflatable structures have attracted considerable research attention in many fields owing to their numerous advantages, such as being light and able to engage in interactions safely. However, in most cases, the inflatable structure can only have one stable configuration, which is undesirable for robotic arms. This study proposes a novel inflatable structure that can be easily reconfigured into multiple stable configurations, even with single-body inflation. In the proposed mechanism, the structure length can be freely adjusted, and its respective joints can be set in the desired directions to facilitate the reconfiguration of its pose. An additional advantage of the proposed mechanism is that it can withstand external forces as well as its own weight. This study analyzes and experimentally validates the shape locking and load-carrying properties of the proposed mechanism. Further, the fabrication process and design guidelines for the proposed mechanism are presented. Through a suitable demonstration, the proposed mechanism is shown to exhibit multiple stable configurations and lock its poses.

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Enhancing Contact Stability in Admittance-Type Haptic Interaction Using Bidirectional Time-Domain Passivity Control

Enhancing Contact Stability in Admittance-Type Haptic Intera...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Seong-Su Park Huseyin Tugcan Dinc Kwang-Hyun Lee Jee-Hwan Ryu Department of Civil and Environmental Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea Robotics Program Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea

The present paper proposes a novel strategy to enhance admittance-type haptic interaction using bidirectional time-domain passivity control. While admittance-type haptic interaction is widely employed in human-robot collaboration, its ability to render low virtual inertia can be limited, leading to unstable interactions with rigid environments. The proposed strategy seeks to stabilize a lower range of virtual inertia while maintaining responsive behavior in free space. The Franka Emika Collaborative robot was utilized in various experiments to test the approach, and the results indicate that the bidirectional time-domain passivity controller can improve interaction performance relative to the conventional unidirectional time-domain passivity approach. This technique may aid in reducing operator fatigue during the manipulation of heavy, non-back drivable industrial robots while preserving a lightweight feel.

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A YOLOv7-Based Method for Detecting Buttons in Service Robots during Autonomous Elevator-Taking Tasks

A YOLOv7-Based Method for Detecting Buttons in Service Robot...

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International Conference on Advanced robotics and Intelligent Systems (ARIS)

作者： Ming-Hsin Chen Wei-Hsiang Huang Tzuu-Hseng S. Li aiRobots Lab. MS Degree Program on AI Robotics National Cheng Kung University Tainan Taiwan Dept. of Electrical Engineering aiRobots Lab. NCKU Tainan Taiwan

This paper presents a novel visual algorithm based on YOLOv7, designed to enhance the robustness of automated elevator-taking service robots in detecting elevator buttons. Traditional solutions for elevator interaction, such as image processing methods, feature selection, or wireless communication protocols, have inherent limitations related to communication security issues and additional equipment costs. In order to overcome these challenges, our method leverages a physical robotic arm and the YOLOv7 object detection neural network to improve the accuracy of elevator button detection and enable effective robot-elevator interaction. To identify elevator buttons, we employ single-stage object detection algorithms along with multiple cameras to capture comprehensive environmental information during experimental trials. By utilizing these techniques, our proposed method ensures the safety and reliability of the automated elevator-taking process for service robots. Experimental results demonstrate the effectiveness of our algorithm in accurately detecting elevator buttons across various testing scenarios. Overall, our method offers a more suitable solution for service robots to autonomously navigate elevators and perform their intended tasks.

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