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STEM Education Conference (iSTEM-Ed), International

作者： Sethakarn Prongncuh Aphirak Thitinaruemit Patinya Sang-Aroon Dept. of Robotics Engineering Faculty of Industrial Technology Suan Sunandha Rajabhat University Dusit Bangkok Thailand Dept. of Industrial Design and Packaging Faculty of Industrial Technology Suan Sunandha Rajabhat University Dusit Bangkok Thailand

This paper introduces an online robotics learning STEM activities for the robot and packaging design camp to develop soft skills in knowledge management. The main purpose is to contribute the understanding of robotics and packaging design, how to use product and packaging design principles to build a Tiger cardboard robot, that can inspire youth to pursue education in robotics engineering with industrial design and packaging. This camp is two course content from online STEM activities such as: robot and packaging design contents. There are 18 students participating in the camp. Pretest and posttest on the content of both courses are used in project-based learning evaluation. The results demonstrate that learner outcomes can reach the 15.6% baseline.

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R-SIEL: a physics-informed learning algorithm for discovering dynamics of serial manipulators

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Expert Systems with Applications 2025年 287卷

作者： Omar, Mohamed Li, Ruifeng Wang, Ke Asker, Ahmed State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China Production and Mechanical Design Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Zhengzhou Research Institute Harbin Institute of Technology Zhengzhou450000 China School of Engineering and Applied Sciences Nile University Giza12573 Egypt

Dynamic modeling is essential for serial manipulators’ design and control. Multi-body dynamics simulators, such as SIMULINK and Adams, can precisely replicate a robot's dynamic behavior but are incapable of determining its nonlinear dynamic model. This work aims to identify serial manipulators’ dynamics from simulation data, which extends the capability of using an algorithm that combines the Euler-Lagrange method and sparse identification of nonlinear dynamics (SINDy) with physics-based constraints and Akaike Information Criterion (AICc). The algorithm is called Recursive SINDy Euler-Lagrange (R-SIEL). The Euler-Lagrange method is used to construct a preliminary library of symbolic functions. The physics-informed constraints, which utilize the similarities between the symbolic coefficients of the dynamic equations, are employed to decrease the number of functions in the library to get a constrained library. SINDy is equipped with three kinds of constraints: the constrained library, the dataset, and similarities between links’ dynamic equations. The generation of the three distinct datasets—training, constraints, and testing data—uses simulated data from the MATLAB® SIMULINK environment. The algorithms of sequential threshold least-squares and L0-minimization are coded with the convex optimization package CVX to determine the best dynamic model. The resulting models are tested with the testing dataset, and based on the lowest AICc, the best model is chosen. The proposed algorithm is applied to different serial manipulators: 2DOF robot, Stanford, PRR robot, ABB SCARA, UR5, and KUKA KR4 AGILUS robots. The resulting model is validated with the dynamic model derived using the conventional Euler-Lagrange method. For the considered manipulators, the R-SIEL algorithm shows a high capability to identify the dynamic model compared to the one identified compared to the previous algorithm. © 2025 Elsevier Ltd

关键词： Lagrange multipliers

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Conceptual design and Kinematic Analysis of Hybrid Parallel Robot for Accurate Position and Orientation

Conceptual Design and Kinematic Analysis of Hybrid Parallel ...

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SICE Annual Conference

作者： K. M. Asy Ahmed B. Zaky Haitham El-Hussieny Hiroyuki Ishii Mahmoud Elsamanty Mechatronics and Robotics Department School of Innovative Design Engineering Egypt Japan University for Science and Technology Alexandria Egypt Production and Mechanical Design Engineering Department Faculty of Engineering Menoufia University Computer Science and Information Technology Programs (CSIT) Egypt Japan University for Science and Technology Alexandria Egypt Electrical Department Faculty of Engineering at Shoubra Benha University Cairo Egypt School of Creative Science and Engineering Waseda University Japan Mechanical Department Faculty of Engineering at Shoubra Benha University Cairo Egypt

The proposed work presents an innovative hybrid parallel robot, combining a delta robot with a spherical parallel manipulator (SPM) to achieve 6-DoF motion. The conceptual design was developed using SolidWorks, showcasing agile movement with required angles. A comprehensive electrical layout design was initiated for ensuring efficient system control, integrating various components such as actuators, sensors, and control units for seamless operation. A forward kinematic model was mathematically developed and implemented in MATLAB, generating trained data by simulating the robot’s motion for various input parameters. The trained data represents the robot’s position and orientation for different movement types, including linear, circular, pure, and mixed multiple orientation movements. The robot’s performance was evaluated using metrics such as position accuracy, orientation error, and response time. This study analyzed the robot’s kinematics for precise position and orientation control, conducting tests with square path and helix trajectory movements, as well as sudden and sinusoidal wave motor angle changes. Results demonstrated the robot’s platform moved with a pure roll angle, fixed yaw and pitch angles with repetitive roll angle changes, and a small range of roll and pitch angle with a yaw angle, achieving less than 0.2% error in position accuracy and orientation. These findings offer valuable insights for designing and controlling hybrid parallel robots for high-accuracy positioning, highlighting the importance of a well-designed electrical layout, a thorough understanding of the kinematic model, and rigorous performance evaluation for effective system control.

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Collaborative Learning in Bilateral Academic Training on robotics design and Development

Collaborative Learning in Bilateral Academic Training on Rob...

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STEM Education Conference (iSTEM-Ed), International

作者： Patinya Sang-Aroon Sethakarn Prongnuch Tadchanon Chuman Aphirak Thitinaruemit Kalaphath Kounlaxay Chanida Niyomtum Dept. of Industrial Design and Packaging Faculty of Engineering and Industrial Technology Suan Sunandha Rajabhat University Dusit Bangkok Thailand Dept. of Robotics Engineering Faculty of Engineering and Industrial Technology Suan Sunandha Rajabhat University Dusit Bangkok Thailand Dept. of Electrical Technology Faculty of Engineering and Industrial Technology Suan Sunandha Rajabhat University Dusit Bangkok Thailand Dept. of Computer Engineering Faculty of Engineering Souphanouvong University Luangprabang City Lao PDR Education Automation System Co. Ltd. Bangplee Samutprakran Thailand

ISBN: (数字)9798350365979

ISBN: (纸本)9798350365986

This paper presents how collaborative learning can facilitate academic training in robotics design and development, with the objective of establishing a foundation in robotics structure design and microcontroller programming for controlling robots, particularly for bilateral undergraduate students. The students comprise 20 engineering students and 20 architecture students, who are divided into groups of two, consisting of an engineering students and an architecture students each. Group activities focusing on robot structure design and robot Development are provided for the students. The results demonstrate that collaborative learning can effectively facilitate the training activities.

关键词： Training Educational robots Federated learning Microcontrollers Focusing engineering students Programming profession

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Contacts from Motion: Learning Discrete Features for Automatic Contact Detection and Estimation from Human Movements *

Contacts from Motion: Learning Discrete Features for Automat...

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

作者： Hibiki Miyake Ko Ayusawa Ryusuke Sagawa Eiichi Yoshida Department of Medical and Robotic Engineering Design Faculty of Advanced Engineering Tokyo University of Science Tokyo Japan Human Augmented Research Center (Kashiwa Chiba) CNRS-AIST JRL (Joint Robotics Laboratory) IRL Artificial Intelligence Research Center (Tsukuba Ibaraki) National Institute of Advanced Industrial Science and Technology (AIST) Japan

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper presents a novel method for detecting and estimating contact forces only from human motions using machine learning techniques. Knowing the location of the contacts with the environment and the magnitude of the exerted force is critical for dynamic human motion analysis. However, their annotation is usually made manually from captured motion data especially in case of multiple contacts even if the data includes force measurement. Moreover, most existing human motion datasets do not include contact force. To overcome these bottlenecks, we introduce a network that leverages vector-quantized variational autoencoder (VQ-VAE) and self-attention that learns a small set of discrete feature values representing various contact states. These feature values, called contact codes, allow human motions to be converted to contact states and resulting forces. By applying an optimization for contact estimation with a reduced set of manual annotations, the existence of contacts can be automatically determined, which is essential information for dynamic analysis. We validated the effectiveness and potential usefulness of the proposed method with a human walking gait dataset, by converting the human motions into contact sequences and forces and applying the estimated contacts to dynamic motion analysis.

关键词： Legged locomotion Codes Annotations Force Dynamics Estimation Manuals Feature extraction Motion analysis Optimization

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Fitts’ List Revisited: An Empirical Study on Function Allocation in a Two-Agent Physical Human-Robot Collaborative Position/Force Task

arXiv

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arXiv 2025年

作者： Mol, Nicky Prendergast, J. Micah Abbink, David A. Peternel, Luka Department of Cognitive Robotics Faculty of Mechanical Engineering Delft University of Technology Delft Netherlands Department of Sustainable Design Engineering Faculty of Industrial Design Engineering Delft University of Technology Delft Netherlands

In this letter, we investigate whether the classical function allocation holds for physical Human-Robot Collaboration, which is important for providing insights for Industry 5.0 to guide how to best augment rather than replace workers. This study empirically tests the applicability of Fitts’ List within physical Human-Robot Collaboration, by conducting a user study (N=26, within-subject design) to evaluate four distinct allocations of position/force control between human and robot in an abstract blending task. We hypothesize that the function in which humans control the position achieves better performance and receives higher user ratings. When allocating position control to the human and force control to the robot, compared to the opposite case, we observed a significant improvement in preventing overblending. This was also perceived better in terms of physical demand and overall system acceptance, while participants experienced greater autonomy, more engagement and less frustration. An interesting insight was that the supervisory role (when the robot controls both position and force control) was rated second best in terms of subjective acceptance. Another surprising insight was that if position control was delegated to the robot, the participants perceived much lower autonomy than when the force control was delegated to the robot. These findings empirically support applying Fitts’ principles to static function allocation for physical collaboration, while also revealing important nuanced user experience trade-offs, particularly regarding perceived autonomy when delegating position control. © 2025, CC BY.

关键词： Collaborative robots

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Development of ROV Simulator based on Real Oceanographic Data

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IFAC-PapersOnLine 2023年第2期56卷 11590-11595页

作者： Ryusei KAMEWARI Yusuke FUJISHIMA Kuniaki KAWABATA Kenta SUZUKI Norimitsu SAKAGAMI Fumiaki TAKEMURA Satoru TAKAHASHI Graduate School of Science for Creative Emergence Kagawa University 2217-20 Hayashi-Machi Takamatsu Kagawa 761-0396 Japan Area of Mechanical Systems Engineering Faculty of Engineering and Design Kagawa University Kagawa Japan Sector of Fukushima Research and Development Japan Atomic Energy Agency Fukushima Japan Mechanical Engineering and Robotics Course Faculty of Advanced Science and Technology Ryukoku University Shiga Japan Department of Mechanical Systems Engineering National Institute of Technology Okinawa College Okinawa Japan

In recent years, demand for efficient oceanographic research for the development of marine resources has been increasing on a global scale. In general, research and operation training on underwater robotics in the ocean is very costly, and the climate and conditions of the ocean may make it impossible to conduct experiments. In this paper, we introduce an ocean simulator using real oceanographic data that can avoid these issues. First, we treat Choreonoid which is an integrated software for robots applied by National Institute of Advanced Industrial Science and Technology (AIST), as the basis of the simulator software. Furthermore, to demonstrate its feasibility in a more realistic ocean environment, we introduce the seabed data obtained from oceanographic research and a function to reproduce ocean currents on the simulator. In this paper, self-localization experiment is conducted to demonstrate the effectiveness of the simulator for oceanographic research.

关键词： underwater robot oceanographic research Ocean simulator self-localization estimation

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Distributed Cascade Force Control of Soft-Tactile-Based Multi-robot System for Object Transportation

Distributed Cascade Force Control of Soft-Tactile-Based Mult...

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IEEE/SICE International Symposium on System Integration

作者： Duy Anh Nguyen Nhat Minh Dinh Le Nhan Huu Nguyen Pham Duy Hung Van Anh Ho Trung Dung Ngo More-Than-One Robotics Laboratory Faculty of Sustainable Design Engineering University of Prince Edward Island Charlottetown Canada Soft Haptics Lab. School of Materials Science Japan Advanced Institute of Science and Technology Nomi Ishikawa Japan University of Engineering and Technology Vietnam Japan Science and Technology Agency PRESTO Kawaguchi Saitama Japan

In this paper, we present a distributed cascade force control system (DCFC) for multiple robots with the aim of pushing a rigid object towards a desired moving target without their inter-robot communication. These mobile robots are equipped with 360-degree vision-based soft tactile sensors utilized to determine contact location and resultant impact force. By investigating the dynamics of moving rigid objects on the flat, we proposed a distributed cascade control. The inner loop control incorporates contact force and positioning, ensuring the robots' pushing contact and applying the desired force to the object. The outer loop control coordinates the robots to push the object in a desired direction without inter-robot communication, regardless of unknown object mass and friction uncertainty. The stability and convergence of the control system are verified using the Lyapunov stability theory. We also conducted simulation and real-world experiments to validate the performance of the proposed control method, and the experimental results showcase the successful coordination of multiple robots in pushing an object towards a moving desired direction.

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Automatic Anti-Swing Gantry Crane Based on Gradient Descent Optimization Proportional Integrated Derivative (PID): A Comparison Simulation and Real Experiment

Automatic Anti-Swing Gantry Crane Based on Gradient Descent ...

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International Conference on Electrical engineering and Informatics, ICEEI

作者： Munzir Qadri Danang Karuna Harjanto Jonathan Miharja Winda Astuti Yosica Mariana Sofyan Tan Mahmud Iwan Solihin Mechanical Engineering Department Engineering Faculty University of Muhammadiyah Jakarta Jakarta Indonesia Computer Engineering Department Automotive and Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Industrial Engineering Department Product Design program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Departement of Mechanical & Mechatronics Engineering UCSI University Kuala Lumpur Malaysia

Transporting payload with precise swing angles and transfer times poses a challenging task in building construction, which is why gantry crane systems are extensively utilized. Various control strategies, including open-loop and closed-loop systems, have been proposed to tackle this issue. However, most of these controllers rely on a model and parameters specific to the crane system, making the process of modeling and parameter identification complex and time-consuming. To address this challenge, this work presents a practical and simulation control method for automatic gantry cranes, which is evaluated simulation and experimentally. The lab-scale Gantry crane is developed in this work, which consists of mechanical and electrical parts. Proportional Integral and Derivative (PID) and Proportional Derivative (PD) controllers which applied optimization method are implemented in the electrical part of the system. The PID and PD controller applied in controlled the position and swing angle, respectively. The Gradient Decent optimization method is the best optimization method in determining the parameter of the controller, since it has the best simulation performance in the output of the system. The experimental result is developed and compared between simulation and lab-scale Gantry crane hardware implementation part. The performance of experimental results is shown appropriately approaching the performance of the simulation result, with the approximately settling time.

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Wandering around: A bioinspired approach to visual attention through object motion sensitivity

arXiv

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arXiv 2025年

作者： D’Angelo, Giulia Clerico, Victoria Bartolozzi, Chiara Hoffmann, Matej Furlong, P. Michael Hadjiivanov, Alexander Department of Cybernetics Faculty of Electrical Engineering Czech Technical University Prague Czech Republic IBM Research Europe Zurich Switzerland Event-Driven Perception for Robotics Italian Institute of Technology Genoa Italy National Research Council of Canada & Systems Design Engineering University of Waterloo Canada Advanced Concepts Team European Space Agency Noordwijk Netherlands Adapsent Leiden Netherlands

Active vision enables dynamic and robust visual perception, offering an alternative to the static, passive nature of feedforward architectures commonly used in computer vision, which depend on large datasets and high computational resources. Biological selective attention mechanisms allow agents to focus on salient Regions of Interest (ROIs), reducing computational demand while maintaining real-time responsiveness. Event-based cameras, inspired by the mammalian retina, further enhance this capability by capturing asynchronous scene changes, enabling efficient, low-latency processing. To distinguish moving objects while the event-based camera is also in motion, the agent requires an object motion segmentation mechanism to accurately detect targets and position them at the center of the visual field (fovea). Integrating event-based sensors with neuromorphic algorithms represents a paradigm shift, using Spiking Neural Networks (SNNs) to parallelise computation and adapt to dynamic environments. This work presents a Spiking Convolutional Neural Network (sCNN) bioinspired attention system for selective attention through object motion sensitivity. The system generates events via fixational eye movements using a Dynamic Vision Sensor (DVS) integrated into the Speck neuromorphic hardware, mounted on a Pan-Tilt unit, to identify the ROI and saccade toward it. The system, characterised using ideal gratings and benchmarked against the Event Camera Motion Segmentation Dataset (EVIMO), reaches a mean IoU of 82.2% and a mean SSIM of 96% in multi-object motion segmentation. Additionally, the detection of salient objects reaches an accuracy of 88.8% in office scenarios and 89.8% in challenging indoor and outdoor low-light conditions, as evaluated on the Event-Assisted Low-Light Video Object Segmentation Dataset (LLE-VOS). A real-time demonstrator showcases the system’s capabilities of detecting the salient object through object motion sensitivity in 0.124 s in dynamic scenes. Its l

关键词： Eye movements

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