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IEEE International Conference on Mechatronics and Automation

作者： Yanfeng Zhao Kaijun Lin Haotian Yang Xinyi Dong Tao Sun Qing Shi Qiang Huang Huaping Wang Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institude of Technology Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Encapsulating single cells in hydrogels provides them with a relatively autonomous and controllable three-dimensional extracellular environment. This environment can facilitate cell interaction and proliferation, making it highly valuable in fields such as tissue engineering and single-cell research. However, traditional methods of single-cell encapsulation encounter limitations in terms of single-cell encapsulation rate and topography control, which hinder of the progress of single-cell encapsulation research. In this paper, we propose a novel method for printing microgel-encapsulated single cells using deep learning-guided DLP printing. By leveraging deep learning algorithms, we accurately capture and translate the real-time positional data of single cells into the printing system’s coordinate framework. Based on the single-cell positional information, a virtual digital mask is created via the OpenCV algorithm, which is then input into the digital microscope to complete the single-cell encapsulation. The single-cell encapsulation rate achieved by this method is 86.3%, which is about 2.87 times higher than that of the traditional method. Experimental results show that our method achieves high accuracy in single-cell encapsulation and the fabrication of microgels with arbitrary shapes, which hold significant importance for biological and medical applications.

关键词： Encapsulation Printing Fabrication Deep learning Three-dimensional displays Shape Hydrogels

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Towards the Application of Operational Design Domain Based Scene Generation for Artificial Intelligence Training in Railway Automation The project “Rail Automation with Artificial Intelligence for Detection of Exceptional Situations” (RailAIxs) received funding in the mFUND conveyor line by the German Federal Ministry for Digital and Transport under the funding code 19FS2031A-D.

Towards the Application of Operational Design Domain Based S...

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International Conference on Intelligent Transportation

作者： Till Mersmann Friedrich Betz Julian Eichenbaum Fabian Hampel Simon Klamt Yannick Otten Ingrid Scholl Christian Schindler Faculty of Mechanical Engineering Institute for Rail Vehicle and Transport Systems (IFS) RWTH Aachen University Aachen Germany Faculty of Electrical Engineering and Information Technology Mobile Autonomous Systems and Cognitive Robotics Institute (MASCOR) FH Aachen University of Applied Sciences Aachen Germany

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

For automated, driverless rail transportation applications in open environments, Artificial Intelligence (AI)-based methods are gaining importance, especially in computer vision and perception tasks. The safe operation of complex automated systems requires validation processes. For this purpose, the concept of Operational Design Domains (ODDs), driven by recent developments in the automotive industry, is gaining momentum, allowing to describe different aspects of operating conditions as scenes and scenarios. With regard to safety and authorization using AI-based vision systems, data coverage is needed, which can be enhanced by employing virtual reality in different forms. The creation of virtual scenes and sensor models allows the generation of synthetic sensor data and metadata that can be used as a database for the training of the vision system.

关键词： Training Solid modeling Automation Machine vision Virtual reality Rail transportation Safety Artificial intelligence Standards Synthetic data

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Like Robots, Like Humans: Pupil Dilation During Collaborative Object Manipulation

Like Robots, Like Humans: Pupil Dilation During Collaborativ...

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International Conference on Advanced robotics (ICAR)

作者： Linda Lastrico Nuno Ferreira Duarte Alessandro Carfí Francesco Rea Fulvio Mastrogiovanni José Santos-Victor Alessandra Sciutti Cognitive Architecture for Collaborative Technologies Unit (CONTACT) Italian Institute of Technology Genoa Italy Institute for Systems and Robotics (ISR) Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS)

It is well known that pupil dilation reflects an increase in cognitive load during tasks that demand mental effort. Inspired by this, we questioned whether the same effect occurs during social interactions, particularly in joint object manipulation tasks that require attention and coordination for successful completion. In the first scenario, we measure pupil dilation during human-human interactive actions (handover), either as the object giver or receiver, against an individual action (pick-and-place). In the second scenario, pupillometry is associated with human-to-robot handovers. We present the two publicly available datasets collected in these contexts. Our results show a strong link between pupil dilation and collaborative actions, with both humans and robots. Indeed, an increment in pupil size corresponds to the object handover, which presents the added complexity of mutual coordination. Conversely, individual actions do not elicit the same physiological response at the level of the pupil. In both scenarios, we used objects with different physical properties, cups with varying water levels, but this factor influenced pupil dilation (and presumably, the mental workload) only during the interaction with the robot. With this study, we explore pupillometry as a physiological metric to evaluate cognitive load for collaborative object manipulation during human-human or human-robot collaborations.

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Logarithmically Quantized Distributed Optimization over Dynamic Multi-Agent Networks

arXiv

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arXiv 2024年

作者： Doostmohammadian, Mohammadreza Pequito, Sérgio Mechatronics Department Faculty of Mechanical Engineering Semnan University Semnan Iran Department of Electrical and Computer Engineering Institute for Systems and Robotics Instituto Superior Técnico University of Lisbon Portugal

Distributed optimization finds many applications in machine learning, signal processing, and control systems. In these real-world applications, the constraints of communication networks, particularly limited bandwidth, necessitate implementing quantization techniques. In this paper, we propose distributed optimization dynamics over multi-agent networks subject to logarithmically quantized data transmission. Under this condition, data exchange benefits from representing smaller values with more bits and larger values with fewer bits. As compared to uniform quantization, this allows for higher precision in representing near-optimal values and more accuracy of the distributed optimization algorithm. The proposed optimization dynamics comprise a primary state variable converging to the optimizer and an auxiliary variable tracking the objective function's gradient. Our setting accommodates dynamic network topologies, resulting in a hybrid system requiring convergence analysis using matrix perturbation theory and eigenspectrumanalysis. © 2024, CC BY.

关键词： Support vector machines

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Real-Time-Capable Muscle Force Estimation for Monitoring Robotic Rehabilitation Therapy in the Intensive Care Unit

Real-Time-Capable Muscle Force Estimation for Monitoring Rob...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Kim K. Peper Alexander Aasmann Elisabeth R. Jensen Sami Haddadin Technical University of Munich Germany Department of Computer Engineering Chair of Robotics and Systems Intelligence TUM School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence (MIRMI)

In this paper, a method is proposed to enable real-time monitoring of muscle forces during robotic rehabilitation therapy in the ICU. This method is solely based on sensor information provided by the rehabilitation robot. In current clinical practice, monitoring primarily takes place in the later stages of rehabilitation, but it would also be highly beneficial during early stages. Musculoskeletal models have large, mostly unrealized potential to support and improve patient monitoring. The method presented in this paper is based on a state-of-the-art muscle-tendon path model, which is applied to the use case of the robotic rehabilitation device VEMOTION. The muscle force estimation is validated against surface electromyography measurements of lower limb muscles from 12 healthy volunteers The results show an overall correlation of R = 0.70 0.25 for the single-joint muscle m. iliopsoas, which has a ±major contribution to hip flexion. Given this correlation, the proposed model could be used for real-time monitoring of active patient participation.

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Heuristic Online Path Planning for Mobile Air Purification and Disinfection Robot In Regulating Indoor Air Quality

Heuristic Online Path Planning for Mobile Air Purification a...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Zhong, Qingnan Chen, Kai Duan, Jianghua Ma, Jun Hkust Shenzhen-Hong Kong Collaborative Innovation Research Institute Robotics and Human Machine Interface Laboratory Futian China The Hong Kong University of Science and Technology Department of Mechanical and Aerospace Engineering Hong Kong Robotics and Autonomous Systems Thrust Guangzhou China The Hong Kong University of Science and Technology Division of Emerging Interdisciplinary Areas Hong Kong Hkust Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian China

ISBN: (纸本)9798350325706

In light of past epidemics, people have become more aware of the possibility of viruses on object surfaces and in the air. As a result, there is an increasing demand for robots capable of autonomously disinfecting surfaces and purifying the air. This paper focuses on a mobile robot equipped with air purification and UV-C LED disinfection to combat airborne pathogens and environmental pollution. Through the onboard sensors, the proposed algorithm uses gaussian process model to predict the high potential air pollution area in real-time without the requirement of a distributed array of sensors, and it avoids environmental modifications. Then we propose the hybrid artificial potential field (HAPF) by integrating the predicted result with the artificial potential field method, to generate a heuristic path that can navigate the robot to the affected areas with obstacle avoidance. Experimental testing in simulated and real-world environments demonstrates the effectiveness of the proposed approach in rapidly planning purification paths, marking significant progress in automated air purification, and disinfection technology. © 2023 IEEE.

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Lifted Bilinear Model-based Linear Model Predictive Control with Scalability

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IFAC-PapersOnLine 2023年第2期56卷 9405-9410页

作者： Masaki Kanai Masaki Yamakita Controls and Robotics Innovation Center Research & Development Group Ltd. Hitachi Ibaraki Japan Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo Japan

We propose a novel linear model predictive control (MPC) using a lifted bilinear model based on Koopman theory, which is computationally scalable against the dimension of the target system and the prediction horizon length. In MPC, the accuracy of the prediction model determines control performance, but it is a challenge to reduce the computational cost especially when considering nonlinearity of the model. To address this, a method has been proposed which represents the nonlinear input affine system as a lifted bilinear model and utilizes linear approximation and prediction error correction regarding the lifted state to achieve a low-computational-cost linear MPC with equivalent performance to nonlinear MPC. However, although the previous studies have shown its effectiveness for relatively low-order systems, it has not been applied to practical systems with higher dimensions. In this study, we extend the conventional method and propose a scalable linear MPC using a lifted bilinear model and apply it to higher-order nonlinear systems. In this paper, a quadrotor system operating in three-dimensional space is considered and its analytical lifted bilinear model is derived. In the formulation of linear MPC using the lifted bilinear model, an error correction method is newly introduced to feedback error for adjustment of the numerical relationships among the elements in the lifted state. The effectiveness of the proposed method is demonstrated through numerical simulations.

关键词： Model predictive control Koopman theory Lifting linearization Bilinear system

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GEOPHYSICAL EXPLORATION OF ANTIMONY MINERALIZATIONS: ALTO DO SOBRIDO AND RIBEIRO DA SERRA MINES (PORTUGAL) 22

GEOPHYSICAL EXPLORATION OF ANTIMONY MINERALIZATIONS: ALTO DO...

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22nd International Multidisciplinary Scientific Geoconference: Science and Technologies in Geology, Exploration and Mining, SGEM 2022

作者： Carvalho, Ana Ribeiro, Ricardo Moura, Rui Lima, Alexandre Department of Geosciences Environment and Spatial Planning Faculty of Sciences University of Porto Portugal Institute of Earth Sciences Pole of University of Porto Portugal INESC TEC Centre for Robotics and Autonomous Systems Superior Institute of Engineering of Porto Portugal

ISBN: (纸本)9786197603484

The Alto do Sobrido and Ribeiro da Serra Mines, are two antimony-gold mines located in Gondomar, Portugal, and are part of the Dúrico-Beirão Mining District (DBMD). In this work, we use four geophysical methods, such as gravity, radiometry, magnetic and electromagnetic, to improve the knowledge about these mineralizations and the lithologies and structures associated with them. These four methods allowed us to study these mineralizations through different physical properties. The gravimetric method allowed us to add more data to the hypothesis that the gold-antimony mineralizations of the DBMD could be genetically associated with a non-outcropping granite intrusion, which functioned either as a heat engine that promoted the circulation of fluids and/or as a source of fluids and metals itself. A 2D model was proposed, created with both modelling and inversion, to show that the presence of this granite could indeed fit the response of the negative anomalies found in the residual anomaly. The radiometric method was used mainly as a mapping tool, where we were able to distinguish between some of the lithologies of the area, like the Lower Ordovician quartzites and the metasediments of the Middle Ordovician to the Carboniferous, but also to observe different element concentrations within the Pre-Ordovician Schist-Greywacke Complex (SGC). These variations within the SGC lithology don’t have a clear explanation as to why they occur. Still, we believe that they could occur due to the influence of the abovementioned non-outcropping granite. With the very low frequency (VLF) electromagnetic method, it was possible to locate areas of lower resistivities that correspond to the location of several known mineralized veins, as well as the probable location for a new vein. It was also possible to observe some weathered rocks near faults and fractures associated with the Dúrico-Beirão Shear Zone (DBSZ). With the magnetic method, it is possible to contribute to areas with the prevale

关键词： Radiometry

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A Tensegrity Hybrid Mobile Robot with Mechanical Impact Resistance

A Tensegrity Hybrid Mobile Robot with Mechanical Impact Resi...

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Annual Conference of Industrial Electronics Society

作者： Daniil Filimonov Altay Zhakataev Huseyin Atakan Varol Robotics Engineering SEDS Nazarbayev University Astana Kazakhstan Mechanical Engineering SEDS Nazarbayev University Astana Kazakhstan Institute of Smart Systems and Artificial Intelligence Nazarbayev University Astana Kazakhstan

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

In this work, we present a hybrid mobile robot with legged and wheeled locomotion modes and tensegrity components. Our work uniquely integrates classical robot structures with tensegrities to create a lightweight robot capable of resisting fall impacts and moving efficiently. After describing the mechanical and embedded system implementation of the robot in detail, we present a set of experiments to demonstrate the efficacy of our robot. Specifically, we dropped the robot from different heights and conducted cost of transport measurements for wheeled and legged modes at different speeds. The findings demonstrate that the use of tensegrity structures could be beneficial in terms of impact resistance and in different challenging applications, while double-type locomotion system could serve for more efficient locomotion and power consumption.

关键词： Resistance Legged locomotion Industrial electronics Power demand Embedded systems Service robots Electric shock Manufacturing Mobile robots Electrical resistance measurement

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Towards Autonomous Robotic Ultrasound Scanning Using the Reinforcement Learning-Based Volumetric Data Navigation Method 5

Towards Autonomous Robotic Ultrasound Scanning Using the Rei...

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5th World Robot Conference Symposium on Advanced robotics and Automation, WRC SARA 2023

作者： Shen, Cuifeng Deng, Zhaokun Wang, Jinzhi Wang, Shuangyi Chen, Chen Peking University State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Beijing100871 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong

ISBN: (纸本)9798350307320

Ultrasound scanning is an indispensable diagnostic tool in modern medicine, but obtaining an accurate standard plane relies heavily on the sonographer's skill and expertise. This paper presents a novel approach toward autonomous robotic ultrasound scanning using a reinforcement learning-based navigation method with volumetric ultrasound data. The proposed method enables an ultrasound robot to autonomously acquire high-quality 2D standard planes from patients, thereby reducing the need for human intervention. The core of the approach is a deep reinforcement learning algorithm that detects 2D standard planes within the 3D ultrasound volume and guides the ultrasound probe to extract high-quality standard planes. To assess the effectiveness of the 2D standard plane detection method, an evaluation was conducted using a realworld ultrasound dataset, and a comparison was made between its performance and that of other state-of-the-art autonomous approaches. The simulation experiment was also conducted to validate the autonomous robotic ultrasound acquisition workflow. Results demonstrate that the reinforcement learning-based volumetric data navigation method outperforms existing techniques in terms of standard plane accuracy and is very effective for volumetric data-based robotic navigation. © 2023 IEEE.

关键词： Diagnosis

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