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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Ioannis Xygonakis Riccardo Seganfreddo Mazin Hamad Samuel Schneider Axel Schröder Sandro M. Krieg Bernhard Meyer Lorenzo Chiari Melissa Zavaglia Sami Haddadin Technical University of Munich TUM School of Computation Information and Technology Chair of Robotics and Systems Intelligence Munich Institute of Robotics and Machine Intelligence (MIRMI) Germany Department of Electrical Electronic and Information Engineering University of Bologna Italy Department of Neurosurgery Klinikum rechts der Isar Technical University of Munich School of Medicine Germany Department of Neurosurgery Heidelberg University Hospital Germany Centre for Tactile Internet with Human-in-the-Loop (CeTI) Technische Universität Dresden Germany

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Ahstract- Transcranial Magnetic Stimulation (TMS) is a non-invasive method for brain stimulation, commonly employed in depression treatment and pre-operative planning. With an average session duration of 30 minutes, the manual handling and positioning of the TMS coil during the treatment may become strenuous for the operator. Robotized TMS systems can help automate the overall process. Our study introduces the TMS Robotic Assistant (TMS-RA) as an end-to-end system integrated with a commercial navigated TMS. TMS-RA utilizes online calibration, eliminating additional calibration steps needed before each TMS session. Furthermore, it can be adapted for use with mobile-based nTMS systems commonly found in clinics. We have implemented an error-compensation scheme to mitigate positioning errors, effectively constraining both position and orientation errors to final mean values of 1.18 mm and $0.57^{\circ}$ . Comparing our system with a highly experienced medical expert, it is more precise and has significantly lower orientation errors, with a $3^{\circ}$ difference, while the position errors were slightly worse but remained comparable.

关键词： Coils Accuracy Transcranial magnetic stimulation Navigation Biomechatronics Depression Calibration Planning Brain stimulation Robots

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Solving Colored Traveling Salesman Problem via Multi-neighborhood Simulated Annealing Search 18

Solving Colored Traveling Salesman Problem via Multi-neighbo...

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18th IEEE International Conference on Networking, Sensing and Control, ICNSC 2021

作者： Zhou, Yangming Xu, Wenqiang Fu, Zhang-Hua Zhou, Mengchu East China University of Science and Technology Department of Computer Science and Engineering Shanghai200237 China Macau Institute of Systems Engineering Macau University of Science and Technology 999078 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China Institute of Robotics and Intelligent Manufacturing The Chinese University of Hong Kong Shenzhen Shenzhen518172 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States Department of Cyber-Physical Systems St. Petersburg State Marine Technical University St. Petersburg198262 Russia

ISBN: (纸本)9781665440486

A colored traveling salesman problem (CTSP) is an important variant of the well-known multiple traveling salesman problem, which uses colors to differentiate salesmen's accessibility to individual cities to be visited. As a highly useful model for some complex scheduling problems, CTSP is NP-hard. A Multi-neighborhood Simulated Annealing Search (MSAS) approach is proposed to solve it in this paper. Starting from an initial solution, it iterates through two complementary neighborhoods: intra-route and inter-route neighborhoods. Experiments on three groups of 60 widely-used benchmark instances show that it achieves highly competitive performance compared to state-of-the-art algorithms. Moreover, MSAS can be integrated into other search methods to further improve performance, which is demonstrated by using a recently proposed iterated two-phase local search. © 2021 IEEE.

关键词： Traveling salesman problem

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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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Development of a Robotic Hand Glove System for Secure Grasp with AI Wireless Sensor Data

Development of a Robotic Hand Glove System for Secure Grasp ...

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IEEE International Conference on Mechatronics and Automation

作者： Ahmed Almassri Ken’Ichi Koyanagi Chikamune Wada Keiichi Horio Wan Zuha Wan Hasan Department of Intelligent Robotics Toyama Prefectural University 5180 Kurokawa Imizu Toyama Japan Israa Institute for Scientific Research (IISR) Israa University Gaza Palestine Department of Life Science and Systems Engineering Kyushu Institute of Technology 2-4 Hibikino Kitakyushu Fukuoka Japan Department of Electrical and Electronic Universiti Putra Malaysia Selangor Malaysia

Robotic Hand glove is one of the most commonly used technique in the rehabilitation systems. In this paper, we developed a robotic hand system with a proposed sensing mechanism-based AI algorithm, which can acquire grasping forces from human fingers. It is composed of five low-cost force sensors attached to the glove’s fingertips and wireless data logger. Several experiments including grasping a plastic bottle and squeezing a tennis ball are implemented to verify the efficiency of the proposed system using the developed glove. As a result, it accurately estimates the forces applied by each finger with the aim of achieving a secure grasp comparison with conventional methods.

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Robot Embodied Dynamic Tactile Perception of Liquid in Containers

Robot Embodied Dynamic Tactile Perception of Liquid in Conta...

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IEEE-RAS International Conference on Humanoid Robots

作者： Yingtian Xu Yichen Ma Waner Lin Zhenglong Sun Tianwei Zhang Ziya Wang School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen China Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education School of Physics and Optoelectronic Engineering Shenzhen University Shenzhen China Department of Micro-Nano Electronics School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

Humans perceive objects and environments actively and dynamically using tactile sensing facilitated by the skin. However, due to the gap in sensing capabilities between electronic skin and human skin, it remains a challenge for robots to achieve intricate tactile perception. For example, the task of liquid properties estimation within containers, demands sensing and comprehension of complex dynamic tactile signals during contact. This paper introduces a novel tactile fingertip inspired by human skin enabling both static tactile sensing facilitated by a layer of porous piezoresistive elastomer and dynamic tactile sensing enabled by condenser microphones. A data-driven methodology is employed to analyze liquids through multimodal signals devoid of physical modelling. The proposed robotic system engages in shaking various bottles filled with different volumes of water, allowing the tactile fingertip to detect changes in grasping force caused by shaking and small vibrations caused by the collision of the container with liquid. Five machine learning models trained on these tactile signals are analyzed and compared. Experimental results demonstrate the proficiency of the tactile system in distinguishing liquid fill percentages across bottles of different shapes and capacities with a remarkable accuracy of $\mathbf{9 8 \%}$. This paper holds promise for advancing the embodied intelligence of robots, enhancing their ability to perceive mixed tactile signals dynamically, thereby facilitating tasks such as liquid estimation and other intricate tactile operations in environments like kitchens and hospitals.

关键词： Vibrations Liquids Force Dynamics Piezoelectric transducers Machine learning Containers Robot sensing systems Sensors Robots

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TReCiM: Lower Power and Temperature-Resilient Multibit 2FeFET-1T Compute-in-Memory Design

arXiv

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arXiv 2025年

作者： Zhou, Yifei Kämpfe, Thomas Ni, Kai Amrouch, Hussam Zhuo, Cheng Yin, Xunzhao Zhejiang University Hangzhou China Fraunhofer IPMS Dresden Germany Department of Electrical Engineering University of Notre Dame Notre Dame United States Technical University of Munich TUM School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence Munich Germany Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province China

Compute-in-memory (CiM) emerges as a promising solution to solve hardware challenges in artificial intelligence (AI) and the Internet of Things (IoT), particularly addressing the"memory wall" issue. By utilizing nonvolatile memory (NVM) devices in a crossbar structure, CiM efficiently accelerates multiply-accumulate (MAC) computations, the crucial operations in neural networks and other AI models. Among various NVM devices, Ferroelectric FET (FeFET) is particularly appealing for ultra-low-power CiM arrays due to its CMOS compatibility, voltage-driven write/read mechanisms and high ION/IOFF ratio. Moreover, subthreshold-operated FeFETs, which operate at scaling voltages in the subthreshold region, can further minimize the power consumption of CiM array. However, subthreshold-FeFETs are susceptible to temperature drift, resulting in computation accuracy degradation. Existing solutions exhibit weak temperature resilience at larger array size and only support 1-bit. In this paper, we propose TReCiM, an ultra-low-power temperature-resilient multibit 2FeFET-1T CiM design that reliably performs MAC operations in the subthreshold-FeFET region with temperature ranging from 0°C to 85°C at scale. We benchmark our design using NeuroSim framework in the context of VGG-8 neural network architecture running the CIFAR-10 dataset. Benchmarking results suggest that when considering temperature drift impact, our proposed TReCiM array achieves 91.31% accuracy, with 1.86% accuracy improvement compared to existing 1-bit 2T-1FeFET CiM array. Furthermore, our proposed design achieves 48.03 TOPS/W energy efficiency at system level, comparable to existing designs with smaller technology feature sizes. Copyright © 2025, The Authors. All rights reserved.

关键词： Benchmarking

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Constructive barrier feedback for collision avoidance in leader-follower formation control

arXiv

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arXiv 2023年

作者： Tang, Zhiqi Cunha, Rita Hamel, Tarek Silvestre, Carlos Institute for Systems and Robotics Instituto Superior Técnico Universidade de Lisboa Portugal Division of Decision and Control Systems KTH Royal Institute of Technology Sweden I3S-CNRS Université Côte d'Azur Institut Universitaire de France France Department of Electrical and Computer Engineering The Faculty of Science and Technology The University of Macau China

This paper proposes a novel constructive barrier feedback for reactive collision avoidance between two agents. It incorporates this feature in a formation tracking control strategy for a group of 2nd-order dynamic robots defined in three-dimensional space. Using only relative measurements between neighboring agents, we propose an elegant decentralized controller as the sum of a nominal tracking controller and the constructive barrier feedback for leader-follower formations under a directed single-spanning tree graph topology. The key ingredient is the use of divergent flow as a dissipative term, which slows down the relative velocity in the direction of the neighboring robots without compromising the nominal controller's performance. Compared to traditional barrier function-based optimization controllers, the proposed constructive barrier feedback avoids feasibility issues and results in more computationally efficient control algorithms with systematic equilibrium analysis. Simulations are provided to validate the effectiveness of the proposed control method. © 2023, CC BY-NC-ND.

关键词： Collision avoidance

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Backstepping-Based Adaptive Cooperative Control For Multiple Euler-Lagrange systems Under Unknown Harmonic Disturbances

Backstepping-Based Adaptive Cooperative Control For Multiple...

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Chinese Automation Congress (CAC)

作者： Ying Chen Zi-Yi Zhao Peng Sun Xiang-Gui Guo Jian-Liang Wang Daniel Coutinho Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Collaborative Flying Robotics Hangzhou Innovation Institute of Beihang University HangZhou China Department of Automation and Systems Universidade Federal de Santa Catarina Florianópolis Brazil

This paper studies cooperative control problem for multiple Euler-Lagrange systems with unknown harmonic disturbances, where the disturbances are the superposition of sinusoidal components with unknown amplitude, phase, and frequency. A novel adaptive disturbance observer without requiring the disturbance frequency information is designed to estimate the disturbances. Over the directed network topology, a cooperative control scheme is proposed based on backstepping techniques, so that the tracking error is ultimately uniformly bounded. The scheme developed can be used to reject the harmonic disturbances in a uniform way, which means the characteristics of the disturbances can be described as a fixed parameter vector. Finally, an application is presented to demonstrate the effectiveness of the proposed control strategy in the context of a two-link manipulator system.

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Shifts in Brain Dynamics and Drivers of Consciousness State Transitions

arXiv

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arXiv 2024年

作者： Bodenheimer, Joseph Bogdan, Paul Pequito, Sérgio Ashourvan, Arian Department of Psychology The University of Kansas 1415 Jayhawk Blvd. LawrenceKS66045 United States Ming Hsieh Department of Electrical and Computer Engineering University of Southern California 3740 McClintock Ave Los AngelesCA90089 United States Institute for Systems and Robotics Instituto Superior Técnico Universidade de Lisboa Lisbon66045 Portugal

Understanding the neural mechanisms underlying the transitions between different states of consciousness is a fundamental challenge in neuroscience. Thus, we investigate the underlying drivers of changes during the resting-state dynamics of the human brain, as captured by functional magnetic resonance imaging (fMRI) across varying levels of consciousness (awake, light sedation, deep sedation, and recovery). We deploy a model-based approach relying on linear time-invariant (LTI) dynamical systems under unknown inputs (UI). Our findings reveal distinct changes in the spectral profile of brain dynamics – particularly regarding the stability and frequency of the system’s oscillatory modes during transitions between consciousness states. These models further enable us to identify external drivers influencing large-scale brain activity during naturalistic auditory stimulation. Our findings suggest that these identified inputs delineate how stimulus-induced co-activity propagation differs across consciousness states. Notably, our approach showcases the effectiveness of LTI models under UI in capturing large-scale brain dynamic changes and drivers in complex paradigms, such as naturalistic stimulation, which are not conducive to conventional general linear model analysis. Importantly, our findings shed light on how brain-wide dynamics and drivers evolve as the brain transitions towards conscious states, holding promise for developing more accurate biomarkers of consciousness recovery in disorders of consciousness. © 2024, CC BY-NC-ND.

关键词： Brain

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Graphene-Assisted Chemical Stabilization of Liquid Metal Nano Droplets for Liquid Metal Based Energy Storage

arXiv

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arXiv 2025年

作者： Sanati, Afsaneh L. Nikitin, Timur Fausto, Rui Majidi, Carmel Tavakoli, Mahmoud Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra3030–290 Portugal Dept. de Química Universidade de Coimbra Portugal Faculty of Sciences and Letters Dept. of Physics Istanbul Kultur University Ataköy Campus Bakirköy Istanbul34156 Turkey Soft Machines Lab Department of Mechanical Engineering Carnegie Mellon University PittsburghPA15213 United States

Energy storage devices with liquid metal electrodes have attracted interest in recent years due to their potential for mechanical resilience, self-healing, dendrite-free operation, and fast reaction kinetics. Gallium alloys such as Eutectic Gallium Indium (EGaIn) are particularly interesting due to their low melting point and high theoretical specific capacity. However, when exposed to a highly alkaline electrolyte, thin-film electrodes composed of EGaIn droplets are not stable due to the dissolution of Gallium oxide. In this letter, we address this bottleneck by introducing chemically stable films in which nanoscale droplets of EGaIn are coated with trace amounts of graphene oxide (GO). We demonstrated that a GO to EGaIn weight ratio as low as 0.01 provides enough protection for a thin film formed by GO@EGaIn nanocomposite against significantly acidic or alkaline environments (pH 1-14). We show that the GO coating significantly enhances the surface stability in such environments, and thus improves the energy storage capacity by over 10 times. We perform detailed microstructural analysis of various GO@EGaIn composites and analyze their electrochemical cycling performance under various conditions. We then use this technique to fabricate a thin-film supercapacitor. The results indicate that when coating the EGaIn by a GO to EGaIn ratio of 0.001, the areal capacitance is improved by 10x, reaching 20.02 mF/cm2. This discovery can lay the foundation for the development of the next generation of high-performance liquid metal based thin-film electrodes for energy storage applications. © 2025, CC BY-NC-ND.

关键词： Gallium alloys

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