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8th IEEE International Conference on Advanced robotics and Mechatronics, ICARM 2023

作者： Dong, Yi Duan, Jinjun Liu, Yangjun Dai, Zhendong Manoonpong, Poramate Institute of Bio-inspired Structure and Surface Engineering College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Information Science & Technology Vidyasirimedhi Institute of Science & Technology Bio-inspired Robotics & Neural Engineering Lab Rayong21210 Thailand

ISBN: (纸本)9798350300178

Packaging is usually a multi-object manipulation task involving objects of different softness and dimensions. The flexibility of paper and the variety of shoe types and states are major challenges for packaging tasks. In this study, we propose a complete solution for a shoe packaging task involving paper, which makes full use of extrinsic resources to improve its efficiency. Firstly, according to the different characteristics of the manipulated objects and task requirements, visual perception is developed to obtain the grasp poses of different objects. Furthermore, paper grasping strategies by exploiting environmental constraints and shoe reorientation method with gravity are proposed, which are necessary for a shoe packaging task. Finally, the complete pipeline for a shoe packaging task is introduced and qualitative and quantitative experiments verify its feasibility and robustness. © 2023 IEEE.

关键词： Packaging

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Robotic Shoe Packaging Strategies Based on a Single Soft-Gripper System and Extrinsic Resources

Robotic Shoe Packaging Strategies Based on a Single Soft-Gri...

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Yi Dong Jinjun Duan Yangjun Liu Zhendong Dai Poramate Manoonpong Institute of Bio-inspired Structure and Surface Engineering College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Bio-inspired Robotics & Neural Engineering Lab School of Information Science & Technology Vidyasirimedhi Institute of Science & Technology Rayong Thailand

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neural Control and Learning with a Turning Strategy of a Gecko-inspired Robot with a Bendable Body

Neural Control and Learning with a Turning Strategy of a Gec...

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IEEE International Conference on Control and robotics engineering (ICCRE)

作者： Worasuchad Haomachai Zhendong Dai Yang Li Poramate Manoonpong State Key Laboratory of Mechanics and Control for Aerospace Structures College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Bio-inspired Robotics & Neural Engineering Lab School of Information Science & Technology Vidyasirimedhi Institute of Science & Technology Rayong Thailand

ISBN: (数字)9798350372694

ISBN: (纸本)9798350372700

Sprawling animals leverage flexible trajectories during turning to navigate challenging and narrow environments effectively. Motivated by this natural behavior, a gecko-inspired robot with a bendable body is employed to investigate and implement an efficient turning strategy. A neural central pattern generator (CPG) with a radial basis function (RBF)-based premotor neuron network (CPG-RBF network) with black-box optimization (BBO) was used to control and train the robot for walking and consequently exploring the turning strategies based on the established walking policy to facilitate turning without the need for additional learning time. Through simulated robot experiments in circular and track environments, the robot with an undulated C-shaped body strategy achieved a lower radius of the turning curve and consequently gained faster locomotion speed by approximately 23% compared to a fixed C-shaped body strategy. This enhanced performance is attributed to the undulated body's effectiveness in facilitating turning maneuvers.

关键词： Legged locomotion Navigation Animals Neurons Closed box Turning Generators

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Editorial: The roles of self-organization and sensory adaptation for locomotion in animals and robots

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Frontiers in Neurorobotics 2024年 18卷 1372772页

作者： Sándor, Bulcsú Gros, Claudius Manoonpong, Poramate Department of Physics Babes-Bolyai University Cluj-Napoca Romania Institute for Theoretical Physics Goethe University Frankfurt Frankfurt am Main Germany Bio-inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science and Technology Rayong Thailand Embodied Artificial Intelligence and Neurorobotics Lab SDU Biorobotics The Mærsk Mc-Kinney Møller Institute University of Southern Denmark Odense Denmark

Editorial on the Research Topic The roles of self-organization and sensory adaptation for locomotion in animals and robots 1 Introduction Self-organization is the emergence of a global spatial or dynamical pattern in a system with interacting components governed by simple local rules (Gros, 2015). The investigated leg and body coordination strategies include (i) the local leg and body coordination at the segment level (LCS) in a manner similar to millipedes, simultaneous leg amplitude reduction in response to different turning directions (SAR, like insects), and the global phase reversal of legs inside of turning curve (GPR, typical engineering approach). While Wang et al. mostly focus on variants of particle swarm optimization algorithms, various bio-inspired heuristic optimization methods are also discussed together with the active challenges of robot olfaction research. 3 Concluding remarks The present Research Topic brings together studies focusing on different aspects of self-organized locomotion starting from the role of load feedback signals, through bio-inspired control strategies for intra- and inter-leg coordination, biologically realistic neural networks for obstacle avoidance and maze navigation tasks, to odor source localization by swarms. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

关键词： robotic locomotion motion patterns swarms of robots control and learning algorithms sensory feedback loop

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Nature's All-in-One: Multitasking Robots inspired by Dung Beetles

arXiv

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arXiv 2024年

作者： Leung, Binggwong Gorb, Stanislav Manoonpong, Poramate Bio-inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science and Technology Rayong21210 Thailand Functional Morphology and Biomechanics Zoological Institute Kiel University Kiel24118 Germany Embodied AI and Neurorobotics Lab SDU Biorobotics The Mærsk Mc-Kinney Møller Institute The University of Southern Denmark Odense5230 Denmark

Dung beetles impressively coordinate their six legs simultaneously to effectively roll large dung balls. They are also capable of rolling dung balls varying in the weight on different terrains. The mechanisms underlying how their motor commands are adapted to walk and simultaneously roll balls (multitasking behavior) under different conditions remain unknown. Therefore, this study unravels the mechanisms of how dung beetles roll dung balls and adapt their leg movements to stably roll balls over different terrains for multitasking robots. We synthesize a modular neural-based loco-manipulation control inspired by and based on ethological observations of the ball-rolling behavior of dung beetles. The proposed neural-based control contains various neural modules, including a central pattern generator (CPG) module, a pattern formation network (PFN) module, and a robot orientation control (ROC) module. The integrated neural control mechanisms can successfully control a dung beetle-like robot (ALPHA) with biomechanical feet to perform adaptive robust (multitasking) loco-manipulation (walking and ball-rolling) on various terrains (flat and uneven). It can also deal with different ball weights (2.0 and 4.6 kg) and ball types (soft and rigid). The control mechanisms can serve as guiding principles for solving complex sensory-motor coordination for multitasking robots. Furthermore, this study contributes to biological research by enhancing our scientific understanding of sensory-motor coordination for complex adaptive (multitasking) loco-manipulation behavior in animals. © 2024, CC BY.

关键词： Modular robots

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Online adaptive resistance control of an arm exercise exoskeleton 23rd

Online adaptive resistance control of an arm exercise exoske...

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23rd International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2020

作者： Xiong, Xiaofeng Manoonpong, Poramate Odense5230 Denmark Bio-Inspired Robotics and Neural Engineering Lab The School of Information Science and Technology Vidyasirimedhi Institute of Science and Technology Rayong21210 Thailand

ISBN: (纸本)9781916449046

Resistance training is an effective means of strengthening musculoskeletal capabilities;thereby preventing musculoskeletal disorders (MSDs). However, most existing exoskeletons fail to provide active and affordable resistance training. To address this problem, we develop a resistance-as-needed (RAN) controller and an arm exercise (RISE) exoskeleton for resistance training. The exoskeleton is designed and manufactured in a portable, lightweight (0.425 kg), and 3D print manner. Such control adaptability and design portability are facilitated by the RAN controller whose implementation relies only on internal (e.g., joint position) sensing, rather than external (i.e., EMG, electromyography) sensing and physical compliant mechanisms (e.g., springs). The active resistance torque to the RISE exoskeleton is generated by the RAN controller, in which impedance gains Kp and Kd are online tuned. As a result, the RAN controller enables the RISE exoskeleton to produce more adaptive and effective resistance motions that lead to higher training efficiency, compared to conventional constant impedance controllers. The proposed RAN controller and designed RISE exoskeleton pave a way for providing portable and affordable resistance training. © CLAWAR Association Ltd.

关键词： Controllers

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Self-supervised Learning Through Scene Observation for Selective Item Identification in Conveyor Belt Systems 21st

Self-supervised Learning Through Scene Observation for Selec...

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21th Annual Conference on Towards Autonomous robotics, TAROS 20120

作者： Scimeca, Luca Iida, Fumiya Bio-Inspired Robotics Lab. Cambridge University Department of Engineering Trumpington St CambridgeCB2 1PZ United Kingdom

ISBN: (纸本)9783030634858

Conveyor belts are core components in several industries. Often, workers need to operate on conveyor belts, selectively picking objects based on visual features following inspection. In such cases it is desirable to be able to selectively identify such items automatically. The need of tailored systems discourages solutions based on common data collection and lab.ling, as well as vision systems based on known object features. We device a novel framework to learn from existing human lab.r, without need of explicit data gathering or lab.ling. The framework autonomously detects, tracks and learns the visual features of salient objects in conveyor belt-based systems. The system is comprised of two cameras, a Convolutional neural Network and a novel training regime, devised to support learning on on-line visual feeds. We show the framework is capable of autonomously learning the visual features of the objects picked by human operators. The system trains entirely through visual observation of human lab.r and achieves detection accuracy of over 97% on a set of 7 different objects after only 10 min of operation, without any knowledge of the objects at priori. © 2020, Springer Nature Switzerland AG.

关键词： Machine learning

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A Variable Soft Finger Exoskeleton for Quantifying Fatigue-induced Mechanical Impedance

A Variable Soft Finger Exoskeleton for Quantifying Fatigue-i...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Xiaofeng Xiong Poramate Manoonpong SDU Biorobotics the Mærsk Mc-Kinney Møller Institute the University of Southern Denmark (SDU) Odense M Denmark Bio-Inspired Robotics and Neural Engineering Lab the School of Information Science and Technology Vidyasirimedhi Institute of Science and Technology Rayong Thailand

Interactive (mechanical) impedance and finger fatigues are important topics, which have not been well investigated. To tackle this problem, we developed a soft lightweight (0.25 kg) finger exoskeleton (TIE-EXO) for quantifying interactive impedance and finger fatigue. A resist-as-needed (RAN) controller was used to produce variable resistance in fingers’ exercises. The TIE-EXO’s feedback and RAN’s parameters were applied to quantify the relationship between interactive impedance and finger fatigue. This quantification was validated in the index and middle fingers of three subjects. This validation shows that the RAN control enables the TIE-EXO to produce online resistance adaptations to different subjects and finger fatigue. Moreover, it indicates a variation and invariance in finger impedance control. We argue that the proposed method provides a novel way for investigating interactive impedance and finger fatigue.

关键词： Resistance Automation Conferences Fingers Exoskeletons Fatigue Impedance

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Revealing preference in popular music through familiarity and brain response

arXiv

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arXiv 2021年

作者： Sangnark, Soravitt Autthasan, Phairot Ponglertnapakorn, Puntawat Chalekarn, Phudit Sudhawiyangkul, Thapanun Trakulruangroj, Manatsanan Songsermsawad, Sarita Assabumrungrat, Rawin Amplod, Supalak Ounjai, Kajornvut Wilaiprasitporn, Theerawit Bio-inspired Robotics and Neural Engineering Lab. School of Information Science and Technology Vidyasirimedhi Institute of Science & Technology Rayong Thailand Vision and Learning Lab. School of Information Science and Technology Vidyasirimedhi Institute of Science & Technology Rayong Thailand School of Energy Science and Engineering Vidyasirimedhi Institute of Science & Technology Rayong Thailand Biological Engineering program Faculty of Engineering Neuroscience Center for Research and Innovation Learning Institute King Mongkut’s University of Technology Thonburi Thailand Faculty of Electrical Engineering Sirindhorn International Institute of Technology Thammasat University Thailand School of Engineering Tohoku University Japan

Music preference was reported as a factor, which could elicit innermost music emotion, entailing accurate ground-truth data and music therapy efficiency. This study executes statistical analysis to investigate the distinction of music preference through familiarity scores, response times (response rates), and brain response (EEG). Twenty participants did self-assessment after listening to two types of popular music’s chorus section: music without lyrics (Melody) and music with lyrics (Song). We then conduct a music preference classification using a support vector machine, random forest, and k-nearest neighbors with the familiarity scores, the response rates, and EEG as the feature vectors. The statistical analysis and F1-score of EEG are congruent, which is the brain’s right side outperformed its left side in classification performance. Finally, these behavioral and brain studies support that preference, familiarity, and response rates can contribute to the music emotion experiment’s design to understand music, emotion, and listener. Not only to the music industry, the biomedical and healthcare industry can also exploit this experiment to collect data from patients to improve the efficiency of healing by music. © 2021, CC BY-NC-ND.

关键词： Decision trees

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Sleepposenet: Multi-View multi-task learning for sleep postural transition recognition using UWB

arXiv

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arXiv 2020年

作者： Piriyajitakonkij, Maytus Warin, Patchanon Lakhan, Payongkit Leelaarporn, Pitsharponrn Pianpanit, Theerasarn Niparnan, Nattee Mukhopadhyay, Subhas Chandra Wilaiprasitporn, Theerawit Bio-inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science & Technology Rayong Thailand Nakhon Nayok Thailand Computer Engineering Department Chulalongkorn University Bangkok Thailand School of Engineering Macquarie University SydneyNSW2109 Australia

Recognizing the movements during sleep is crucial for monitoring of patients with sleep disorders. However, the utilization of the deep learning approaches ultra-wideband (UWB)-based indoor applications for the classification of human sleeping postures has not been explored widely. This study investigates the performance of the off-the-shelf single antenna UWB in a novel application of sleep postural transition (SPT) recognition. The proposed Multi-View Multi-Task Learning, entitled SleepPoseNet or SPN, with time series data augmentation aims to classify four standard SPTs. SPN exhibits an ability to capture both time and frequency features, including the movement and direction of sleeping positions. Three cost functions for multi-views, multi-tasks, and classification are proposed to optimize SPN. The data recorded from 26 volunteers displayed that SPN with mean accuracy of 79.7±0.5% significantly outperformed the mean accuracy of 73.5 ± 0.7% obtained from deep convolution neural network (DCNN) in a recent state-of-the-art work on human activity recognition using UWB. Apart from UWB system, SPN with the data augmentation can ultimately be adopted to learn and classify time series data in various applications. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

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