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2024 IEEE International Conference on Real-Time Computing and robotics, RCAR 2024

作者： Zhu, Kaiwei Kong, Shihan Wu, Fang Sun, Jinan Yu, Junzhi College of Engineering Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics Beijing100871 China SPIC Nuclear Energy Co. Ltd. China Peking University National Engineering Research Center for Software Engineering Beijing100871 China

ISBN: (纸本)9798350372601

In this paper, a set of methods of autonomous obstacle exploration, avoiding, and target-tracking control in the uncertain environment is proposed. First, a secure and reliable obstacle exploration framework via multi-sensors information fusion is developed for USV to autonomously perceive obstacles in unknown environments. Second, a modified dynamic artificial potential field-based path planning method is proposed to dynamically generate a safe and feasible path even without complete prior obstacle position information. Third, a robust sliding mode control law with extended state observer is designed to track expectational target in the dynamical and uncertain environment. Finally, the target-tracking control law has been validated through the simulation experiments. Besides, the proposed methods of obstacle exploration, path planning, and target-tracking control are comprehensively evaluated through the simulation experiments in WEBOTS software, which is closer to the real world. © 2024 IEEE.

关键词： Target tracking

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Thermal Casting for Monolithic Soft Actuators

Thermal Casting for Monolithic Soft Actuators

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Wang, Huacen Yang, Boyu Mei, Junling Dai, Yujin Xia, Yixuan Wang, Hongqiang Southern University of Science and Technology Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Shenzhen518055 China Guangdong Prov. Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Shenzhen518055 China Guangzhou510000 China

ISBN: (纸本)9781665481090

Soft robotics has significantly impacted many fields in the last decades. However, their current fabrication methodologies are limited by the assemble complexity, limited materials, or long duration. A monolithic thermal casting method for fabrication of elastomer and soft robotics based on thermoset polymer is introduced. In this work, we used simple heating cores to manufacture elastomers and soft actuators. By controlling the heating current and duration, we can adjust the thermal field and consequently change the configuration of the generated elastomer. Due to its short duration (several minutes), this method can benefit the mass production of soft robots for high efficiency. Also, this work offers a new way to design soft robotics without complex molds. © 2022 IEEE.

关键词： Elastomers

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DROPLET-BASED MICROFLUIDIC SYNTHESIS OF FUNCTIONAL VASCULARIZED HYDROGEL MICROSPHERES 26

DROPLET-BASED MICROFLUIDIC SYNTHESIS OF FUNCTIONAL VASCULARI...

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26th International Conference on Miniaturized systems for Chemistry and Life Sciences, MicroTAS 2022

作者： Zhan, Zhen Liu, Zeyang Li, Jianjie Zhang, Wenxuan Hu, Chengzhi Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Southern University of Science and Technology China

ISBN: (纸本)9781733419048

This paper proposes a droplet-based microfluidic method to generate hydrogel microspheres with interior undulated wavy or spiral canals for 3D cell culture. Human umbilical vein endothelial cells (HUVECs) were cultured in the interior canals of the hydrogel microspheres and formed monolayers on the surface of the spiral canals, mimicking native blood vessel configurations. The fabricated heterogeneous spheroids with spiral blood vessel lumen were also demonstrated to build spirally vascularized bone tissue. © 2022 MicroTAS 2022 - 26th International Conference on Miniaturized systems for Chemistry and Life Sciences. All rights reserved.

关键词： Microfluidics

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A High-Precision Edge Detection Algorithm Based on Improved Sobel Operator-Assisted HED 14

A High-Precision Edge Detection Algorithm Based on Improved ...

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14th IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent systems, CYBER 2024

作者： Yuan, Yeborui Chen, Wenyuan Tang, Jie Yang, Jia School of Electrical Engineering and Automation Tianjin University of Technology Tianjin300384 China Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang State Key Laboratory of Robotics 110016 China University of Chinese Academy of Sciences Beijing100049 China School of Electrical Engineering and Automation Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin300384 China

ISBN: (纸本)9798331506056

In response to the problem of poor detection performance of the traditional Sobel operator in edge detection, a high-precision edge detection algorithm based on Sobel operator-assisted Holistically-nested Edge Detection (HED) network was proposed, which is named ISAHED here. The algorithm is designed as follows. Firstly, an improved Sobel operator based on the traditional Sobel operator was proposed through adding gradient direction. Then, the image output by the improved Sobel operator is taken as the ground-truth image and as the end-to-end comparison object in a HED-like network. Finally, the ISAHED algorithm outputs edge images with better quality through iterative learning of the HED-like neural network. The experimental results prove that the proposed ISAHED algorithm has better edge detection performance by visually observing the final edge images and further evaluating the values of FOM. This work provides an important reference for the further development of the edge detection technology. © 2024 IEEE.

关键词： Contrastive Learning

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Multi-Object Graph Affordance Network: Goal-Oriented Planning through Learned Compound Object Affordances

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IEEE Transactions on Cognitive and Developmental systems 2024年

作者： Girgin, Tuba Uǧur, Emre Bogazici University Department of Computer Engineering Istanbul Turkey TUBITAK BILGEM Robotics and Autonomous Systems Laboratory Kocaeli41470 Turkey

Learning object affordances is an effective tool in the field of robot learning. While the data-driven models investigate affordances of single or paired objects, there is a gap in the exploration of affordances of compound objects composed of an arbitrary number of objects. We propose the Multi-Object Graph Affordance Network which models complex compound object affordances by learning the outcomes of robot actions that facilitate interactions between an object and a compound. Given the depth images of the objects, the object features are extracted via convolution operations and encoded in the nodes of graph neural networks. Graph convolution operations are used to encode the state of the compounds, which are used as input to decoders to predict the outcome of the object-compound interactions. After learning the compound object affordances, given different tasks, the learned outcome predictors are used to plan sequences of stack actions that involve stacking objects on top of each other, inserting smaller objects into larger containers and passing through ring-like objects through poles. We showed that our system successfully modeled the affordances of compound objects that include concave and convex objects, in both simulated and real-world environments. We benchmarked our system with a baseline model to highlight its advantages. © 2016 IEEE.

关键词： Adversarial machine learning

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DEGAS - Deformation Estimation based on Magnetic Gradient Tensor Contraction for Soft Robot Shape Servoing

DEGAS - Deformation Estimation based on Magnetic Gradient Te...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Jelena Vuletić Bruno Marić Matko Orsag LARICS Laboratory for Robotics and Intelligent Control Systems University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (数字)9798331520205

ISBN: (纸本)9798331520212

This work proposes the first derivation, implementation, and experimental validation of magnetic-based proprioceptive sensing method for soft robotic applications. In our proposed approach, the magnetic sensing system measures gradient tensor contractions that can be directly related to the shape of a deformable plastering tool. Custom-designed and 3D-printed plastering tool embeds two identical permanent magnets that generate a non-symmetric magnetic field tracked by the proposed sensor. Seamless real-time control is enabled with the sensor sampling rate of 1 kHz. Classical linear control is synthesized for the scraper bending angle and orientation control. The tool, sensor, and the proposed control system are validated on robotic plastering and painting tasks.

关键词： Magnetic field measurement Tensors Deformation Shape Propioception Estimation Bending Soft robotics Sensors Painting

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Dynamic Obstacle Avoidance for Magnetic Helical Microrobots Based on Deep Reinforcement Learning

Dynamic Obstacle Avoidance for Magnetic Helical Microrobots ...

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2024 IEEE International Conference on Real-Time Computing and robotics, RCAR 2024

作者： Qiu, Yukang Hou, Yaozhen Yang, Haotian Gao, Yigao Huang, Hen-Wei Shi, Qing Huang, Qiang Wang, Huaping Beijing Institute of Technology Intelligent Robotics Institute School of Mechatronical Engineering Beijing100081 China Harvard Medical School Laboratory for Translational Engineering CambridgeMA02139 United States Beijing Institute of Technology Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing100081 China Ministry of Education Key Laboratory of Biomimetic Robots and Systems Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9798350372601

Magnetic helical microrobots hold immense promise in biomedical domains owing to their compact size and efficient propulsion capabilities. However, navigating these microrobots through dynamic and unstructured environments, particularly when encountering numerous dynamic obstacles, remains a formidable challenge. In the study, a control framework based on deep reinforcement learning (DRL) with the objective of guiding a microrobot through dynamic obstacles towards specified target goals is introduced. Initially, we design and fabricate a microdrill capable of propulsion via external magnetic rotating fields produced by our magnetic actuation system. Subsequently, we construct a custom training environment, adhering to the OpenAI gym interface, to serve as the simulator for training purposes. Utilizing the proximal policy optimization algorithm, we conduct training of the navigation policy within this simulator. Simulations and experimental validations conducted in dynamic environments affirms the efficacy of the proposed method. © 2024 IEEE.

关键词： Microrobots

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Graph-Based Decentralized Exploration and Semantic Inspection for Multi-Robot systems

Graph-Based Decentralized Exploration and Semantic Inspectio...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Nada E. Abbas Tamara Petrovic Faculty of Electrical Engineering and Computing LARICS Laboratory for Robotics and Intelligent Control Systems University of Zagreb Zagreb Croatia

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

This paper presents a decentralized graph-based exploration and inspection framework for Multi-Robot systems, designed to address challenges in subterranean and largescale environments. Unlike prior works that focus solely on exploration or inspection, this framework integrates volumetric exploration, semantic inspection, and dynamic task allocation into a unified decentralized system. A key novelty of this work is the seamless integration of these modules in a multi-robot setting, allowing UAVs to autonomously coordinate their tasks without relying on centralized control. The framework employs a hierarchical graph structure, utilizing a dense local graph for immediate navigation and a sparse global graph for long-term planning and repositioning. Extensive simulations in large-scale complex-shaped environments demonstrate that the proposed approach improves the completeness of the generated maps, reduces inconsistencies in the constructed mesh, and accelerates the overall exploration-inspection process compared to existing decentralized strategies.

关键词： Semantics Inspection Prediction algorithms Dynamic scheduling Aircraft navigation Robustness Planning Multi-robot systems Resource management Centralized control

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PediatricsGPT: Large Language Models as Chinese Medical Assistants for Pediatric Applications 38

PediatricsGPT: Large Language Models as Chinese Medical Assi...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Yang, Dingkang Wei, Jinjie Xiao, Dongling Wang, Shunli Wu, Tong Li, Gang Li, Mingcheng Wang, Shuaibing Chen, Jiawei Jiang, Yue Xu, Qingyao Li, Ke Zhai, Peng Zhang, Lihua Academy for Engineering and Technology Fudan University Shanghai China Tencent Youtu Lab Shanghai China Cognition and Intelligent Technology Laboratory Shanghai China Engineering Research Center of AI and Robotics Ministry of Education Shanghai China AI and Unmanned Systems Engineering Research Center of Jilin Province Changchun China

Developing intelligent pediatric consultation systems offers promising prospects for improving diagnostic efficiency, especially in China, where healthcare resources are scarce. Despite recent advances in Large Language Models (LLMs) for Chinese medicine, their performance is sub-optimal in pediatric applications due to inadequate instruction data and vulnerable training procedures. To address the above issues, this paper builds PedCorpus, a high-quality dataset of over 300,000 multi-task instructions from pediatric textbooks, guidelines, and knowledge graph resources to fulfil diverse diagnostic demands. Upon well-designed PedCorpus, we propose PediatricsGPT, the first Chinese pediatric LLM assistant built on a systematic and robust training pipeline. In the continuous pre-training phase, we introduce a hybrid instruction pre-training mechanism to mitigate the internal-injected knowledge inconsistency of LLMs for medical domain adaptation. Immediately, the full-parameter Supervised Fine-Tuning (SFT) is utilized to incorporate the general medical knowledge schema into the models. After that, we devise a direct following preference optimization to enhance the generation of pediatrician-like humanistic responses. In the parameter-efficient secondary SFT phase, a mixture of universal-specific experts strategy is presented to resolve the competency conflict between medical generalist and pediatric expertise mastery. Extensive results based on the metrics, GPT-4, and doctor evaluations on distinct downstream tasks show that PediatricsGPT consistently outperforms previous Chinese medical LLMs. The project and data will be released at https://***/ydk122024/PediatricsGPT. © 2024 Neural information processing systems foundation. All rights reserved.

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Numerical study on the flow field for deep-sea hydrothermal plume tracking simulation 17

Numerical study on the flow field for deep-sea hydrothermal ...

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17th Asian Congress of Fluid Mechanics, ACFM 2023

作者： Xu, Xue-Han Liu, Jing-Cheng Hou, Hui-Rang Meng, Qing-Hao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin300072 China Laboratory for High-speed Aerodynamics Department of Mechanics Tianjin University Tianjin300072 China

In this paper, the distribution of hydrothermal buoyancy plume in the deep-sea environment is studied numerically. Submarine hydrothermal vents are prominent manifestations of hydrothermal activities and are of great scientific and engineering importance. It is an important basis to obtain the temporal and spatial distribution information of different hydrothermal plumes for tracking and locating the source of hydrothermal plumes. Based on Reynolds-averaged Navier-Stokes equations and Realizable k-ϵ turbulence model, the plumes of hydrothermal vents at depths of 800 m, 1200 m, and 1400 m were calculated and studied to obtain the spatial distribution of variables such as velocity field, temperature field and turbulent kinetic energy. The research results of this paper provide an important basis for the simulation of deep-sea hydrothermal plume tracking based on autonomous underwater vehicles. © The Institution of engineering & Technology 2023.

关键词： Flow fields

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