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Robot Embodied Dynamic Tactile Perception of Liquid in Containers

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

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

A Sociable Robotic Platform to Make Career Advices for Undergraduates 22nd

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A Sociable Robotic Platform to Make Career Advices for Under...

22nd International Conference on Human Computer Interaction,HCII 2020

作者： Mithsara, W. K. Malithi Manawadu, Udaka A. De Silva, P. Ravindra S. Robotics and Intelligent Systems Laboratory Department of Computer Science University of Sri Jayewardenepura Nugegoda Sri Lanka Robot Engineering Laboratory Graduate School of Computer Science and Engineering University of Aizu Aizuwakamatsu Japan

ISBN: (纸本)9783030601164

Most of the undergraduates couldn’t figure out a proper direction for their future careers since advising depends on the person and students show aversion in revealing their information to other humans. This research focused on creating a social robotic platform to interact with undergraduates in the field of computer science to realize possible career paths, as recent researches show social companion robots tend to form a stronger bond, which helps the addressee to share their information easily. The robot was created as a tabletop robot employing minimalistic design with a friendly view attached with speech synthesis for communicating purposes. Data was collected from 202 persons, who followed a degree in computer science. Data contains their experience, qualifications, and skills. Thereafter an artificial neural network was created using supervised learning to predict the career path with 95% performance accuracy. The experiment was performed by engaging 15 students from the final year who are a doing degree in computer science and they were asked to provide feedback on the interactions with the robot. The gathered responses highlighted robot animacy, interaction, technology and usefulness. Therefore, from the results it can be concluded that a majority of the students accepted the robot, interacted without hesitation and had friendly conversations with the robot where they valued the generated output from the robot. © 2020, Springer Nature Switzerland AG.

关键词： Neural networks

CPnP: Consistent Pose Estimator for Perspective-n-Point Problem with Bias Elimination

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arXiv 2022年

作者： Zeng, Guangyang Chen, Shiyu Mu, Biqiang Shi, Guodong Wu, Junfeng School of Data Science Chinese University of Ho ng Kong Shenzhen Shenzhen China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Australian Center for Field Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney NSW2008 Australia

The Perspective-n-Point (PnP) problem has been widely studied in both computer vision and photogrammetry societies. With the development of feature extraction techniques, a large number of feature points might be available in a single shot. It is promising to devise a consistent estimator, i.e., the estimate can converge to the true camera pose as the number of points increases. To this end, we propose a consistent PnP solver, named CPnP, with bias elimination. Specifically, linear equations are constructed from the original projection model via measurement model modification and variable elimination, based on which a closed-form least-squares solution is obtained. We then analyze and subtract the asymptotic bias of this solution, resulting in a consistent estimate. Additionally, Gauss-Newton (GN) iterations are executed to refine the consistent solution. Our proposed estimator is efficient in terms of computations—it has O(n) computational complexity. Experimental tests on both synthetic data and real images show that our proposed estimator is superior to some well-known ones for images with dense visual features, in terms of estimation precision and computing time. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear equations

Fixed-time Adaptive Neural Control for Physical Human-Robot Collaboration with Time-Varying Workspace Constraints

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arXiv 2023年

作者： Sun, Yuzhu Van, Mien McIlvanna, Stephen Nhat, Nguyen Minh McLoone, Seán Ceglarek, Dariusz Ge, Shuzhi Sam The Centre for Intelligent Autonomous Manufacturing Systems School of Electronics Electrical Engineering and Computer Science Queen’s University Northern Ireland Belfast United Kingdom The Warwick Manufacturing Group University of Warwick Coventry United Kingdom The Department of Electrical and Computer Engineering Social Robotics Laboratory National University of Singapore Singapore

Physical human-robot collaboration (pHRC) requires both compliance and safety guarantees since robots coordinate with human actions in a shared workspace. This paper presents a novel fixed-time adaptive neural control methodology for handling time-varying workspace constraints that occur in physical human-robot collaboration while also guaranteeing compliance during intended force interactions. The proposed methodology combines the benefits of compliance control, time-varying integral barrier Lyapunov function (TVIBLF) and fixed-time techniques, which not only achieve compliance during physical contact with human operators but also guarantee time-varying workspace constraints and fast tracking error convergence without any restriction on the initial conditions. Furthermore, a neural adaptive control law is designed to compensate for the unknown dynamics and disturbances of the robot manipulator such that the proposed control framework is overall fixed-time converged and capable of online learning without any prior knowledge of robot dynamics and disturbances. The proposed approach is finally validated on a simulated two-link robot manipulator. Simulation results show that the proposed controller is superior in the sense of both tracking error and convergence time compared with the existing barrier Lyapunov functions based controllers, while simultaneously guaranteeing compliance and safety. Copyright © 2023, The Authors. All rights reserved.

关键词： Lyapunov functions

Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data

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Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

Annual Conference of Industrial Electronics Society

作者： Haibo Cheng Yunpeng He Peng Zeng Shichao Li Valeriy Vyatkin State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Automation Aalto University Helsinki Finland

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction.

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CA-SpaceNet: Counterfactual Analysis for 6D Pose Estimation in Space

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arXiv 2022年

作者： Wang, Shunli Wang, Shuaibing Jiao, Bo Yang, Dingkang Su, Liuzhen Zhai, Peng Chen, Chixiao Zhang, Lihua Academy for Engineering & Technology Fudan University China Engineering Research Center of AI and Robotics Ministry of Education China Jilin Provincial Key Laboratory of Intelligence Science & Engineering China Artifical Intelligence and Unmanned Systems Engineering Research Center of Jilin Province China

Reliable and stable 6D pose estimation of uncooperative space objects plays an essential role in on-orbit servicing and debris removal missions. Considering that the pose estimator is sensitive to background interference, this paper proposes a counterfactual analysis framework named CA-SpaceNet to complete robust 6D pose estimation of the space-borne targets under complicated background. Specifically, conventional methods are adopted to extract the features of the whole image in the factual case. In the counterfactual case, a non-existent image without the target but only the background is imagined. Side effect caused by background interference is reduced by counterfactual analysis, which leads to unbiased prediction in final results. In addition, we also carry out low-bit-width quantization for CA-SpaceNet and deploy part of the framework to a Processing-In-Memory (PIM) accelerator on FPGA. Qualitative and quantitative results demonstrate the effectiveness and efficiency of our proposed method. To our best knowledge, this paper applies causal inference and network quantization to the 6D pose estimation of space-borne targets for the first time. The code is available at https://***/Shunli-Wang/CA-SpaceNet. Copyright © 2022, The Authors. All rights reserved.

关键词： Space debris

Intention-Aware Planner for Robust and Safe Aerial Tracking

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arXiv 2023年

作者： Ren, Qiuyu Yu, Huan Dai, Jiajun Zheng, Zhi Meng, Jun Xu, Li Xu, Chao Gao, Fei Cao, Yanjun The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China Huzhou Institute Zhejiang University Huzhou313000 China Huzhou Laboratory of Autonomous Intelligent Systems Huzhou313000 China The Robotics Institute Zhejiang University Yuyao315400 China College of Electrical Engineering Zhejiang University Hangzhou310027 China

Autonomous target tracking with quadrotors has wide applications in many scenarios, such as cinematographic follow-up shooting or suspect chasing. Target motion prediction is necessary when designing the tracking planner. However, the widely used constant velocity or constant rotation assumption can not fully capture the dynamics of the target. The tracker may fail when the target happens to move aggressively, such as sudden turn or deceleration. In this paper, we propose an intention-aware planner by additionally considering the intention of the target to enhance safety and robustness in aerial tracking applications. Firstly, a designated intention prediction method is proposed, which combines a user-defined potential assessment function and a state observation function. A reachable region is generated to specifically evaluate the turning intentions. Then we design an intention-driven hybrid A* method to predict the future possible positions for the target. Finally, an intention-aware optimization approach is designed to generate a spatial-temporal optimal trajectory, allowing the tracker to perceive unexpected situations from the target. Benchmark comparisons and real-world experiments are conducted to validate the performance of our method. Copyright © 2023, The Authors. All rights reserved.

关键词： Aircraft detection

A Tendon-Driven Actuator with Cantilever Initiated Variable Stiffness Used for Robotic Fingers

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SSRN

SSRN 2024年

作者： Li, Chenning Yang, Shangkui Branson, David T. Song, Zhibin Sun, Tao Dai, Jiansheng Kang, Rongjie Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education School of Mechanical Engineering Tianjin University Tianjin300072 China Advanced Manufacturing Technology Research Group Faculty of Engineering University of Nottingham NottinghamNG7 2RD United Kingdom Beijing Institute of Technology China Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems Southern University of Science and Technology Shenzhen518055 China

Variable stiffness actuators (VSAs) have emerged as a key actuation technology known for their bionic performance and task adaptability. However, current VSAs often exhibit relatively large sizes, making them possible for use in robotic arms and legs but less convenient for integrations into robotic hands. This paper introduces a compact design of a tendon-driven variable stiffness actuator (TVSA) based on an adjustable cantilever mechanism, which can be embedded into a robotic finger. This implementation endows the robotic finger with the decoupled regulation of joint position and stiffness. A concise and computationally efficient stiffness mapping model from the TVSA to the finger joints is then established, providing a theoretical foundation for the stiffness regulation of the tendon-driven fingers. A prototype of a robotic hand equipped with the presented TVSA demonstrates safe interactions with various objects with diverse shapes, weights and stiffness. © 2024, The Authors. All rights reserved.

关键词： Stiffness

Theoretical Model Construction of Deformation-Force for Soft Grippers Part I: Co-rotational Modeling and Force Control for Design Optimization

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arXiv 2023年

作者： Dong, Huixu Guo, Haotian Yang, Sihao Qiu, Chen Dai, Jiansheng Chen, I-Ming Zhejiang University Hangzhou310058 China Robotics Research Center Nanyang Technological University Singapore639798 Singapore Maider Medical Industry Equipment Co. Itd 317607 China Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems SUSTech Institute of Robotics Southern University of Science and Technology Shenzhen518055 China Centre for Robotics Research Department of Engineering King’s College London Strand LondonWC2R 2LS United Kingdom

Compliant grippers, owing to adaptivity and safety, have attracted considerable attention for unstructured grasping in real applications, such as industrial or logistic scenarios. However, accurately modeling the bidirectional relationship between shape deformation and contact force for such grippers, the Fin-Ray grippers as an example, remains stagnant to date. To address this research gap, this article devises, presents, and experimentally validates a universal bidirectional force-displacement mathematical model for compliant grippers based on the co-rotational concept, which endows such grippers with an intrinsic force sensing capability and offers a better insight into the design optimization. In Part I of the article, we introduce the fundamental theory of the co-rotational approach, where arbitrary large deformation of beam elements can be modeled. Its intrinsic principle allowing taking materials with varying stiffness, various connection types and key design parameters into consideration with few assumptions. Further, the force-displacement relationship is numerically derived, providing accurate displacement estimations of the gripper under external forces with minor computational loads. The performance of the proposed method is experimentally verified through comparison with Finite Element Analysis (FEA) in simulation, obtaining a fair degree of accuracy (6%), and design optimization of Fin-Ray grippers is systematically investigated. Part II of this article demonstrating the force sensing capabilities and the effects of representative co-rotational modeling parameters on model accuracy is released in Arxiv1 Copyright © 2023, The Authors. All rights reserved.

关键词： Grippers