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IEEE SENSORS

作者： Jian Gao Zihao Pu Ruixin Qiu Ying Li Xiulun Yin Kieran Morton Sadan Wani Justin Wyss Michael Steszyn Ryusuke Ishizaki Fumiya Hamatsu Takeshi Ohsato John D.W. Madden Department of Electrical & Computer Engineering Advanced Material Processing Engineering Laboratory University of British Columbia Vancouver Canada i-generator Portland USA Frontier Robotics Honda R&D Co. Ltd Saitama Japan

Foot plantar force monitoring has been explored using in-shoe soft insole measurements for application in diabetic foot and to assess footwear performance. Sensors are impractical for regular use due to bulky electronics and short battery life. They typically lack shear sensing. A soft flexible and custom configurable 3-axis capacitive sensing array is demonstrated through a customizable arrangement of 16-shear and normal force taxels completely imbedded in a shoe insole that wirelessly updates at 50 $\mathrm{n}_{2}$ . A patterned silicone dielectric mounted on a flex printed circuit board enables normal and shear sensitivities of 19.9 and 2.8 $\text{kPa}^{-1}$ . The 20 $\text{mW}$ power consumption includes transmission of data to a nearby mobile device, which is satisfied for more than 50 hours using a built-in 350 mAh LiPo battery. Operation in a shoe is demonstrated.

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Conceptual Design and Simulation of a BCF Swimming Soft Robotic Fish for Deep-Sea

Conceptual Design and Simulation of a BCF Swimming Soft Robo...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Zhang, Yanyan Ding, Menglong Zhu, Shiqiang Liang, Yiming Bai, Yunhe Xie, Keren Wan, Minhong Gu, Jason Li, Tiefeng Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H4R2 Canada Center for X-mechanics Zhejiang University Hangzhou310027 China Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Department of Engineering Mechanics Zhejiang University Hangzhou310027 China

ISBN: (纸本)9781665481090

This work provides a design of a deep-sea soft robotic fish propelled by the body and/or caudal fin (BCF) mode. The robotic fish consists of a bionic fish head and a BCF propulsion body. To enhance the swimming performance of the first soft robot fish in the deep sea, the design combines the advantages of high efficiency in BCF propulsion with the large deformation angle of dielectric elastomers (DE) flexible joints. The connection of the bionic fish head and BCF is cast in one piece of silicone to achieve deep-sea pressure self-adaptation of the soft robotic fish. The swimming performance was simulated using the CFD method, and the BCF propulsion behaviors were analyzed to evaluate the design proposed. The design goal was verified through numerical calculation, and a further enhanced strategy for the thrust ability of the fish was presented as well. This work can provide a low-cost bionic soft robot fish solution for deep-sea exploration. © 2022 IEEE.

关键词： Conceptual design

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FRAME: Fast and Robust Autonomous 3D point cloud Map-merging for Egocentric multi-robot exploration

arXiv

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arXiv 2023年

作者： Stathoulopoulos, Nikolaos Koval, Anton Agha-Mohammadi, Ali-Akbar Nikolakopoulos, George Robotics and AI Group Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå971 87 Sweden Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States

This article presents a 3D point cloud map-merging framework for egocentric heterogeneous multi-robot exploration, based on overlap detection and alignment, that is independent of a manual initial guess or prior knowledge of the robots’ poses. The novel proposed solution utilizes state-of-the-art place recognition learned descriptors, that through the framework’s main pipeline, offer a fast and robust region overlap estimation, hence eliminating the need for the time-consuming global feature extraction and feature matching process that is typically used in 3D map integration. The region overlap estimation provides a homogeneous rigid transform that is applied as an initial condition in the point cloud registration algorithm Fast-GICP, which provides the final and refined alignment. The efficacy of the proposed framework is experimentally evaluated based on multiple field multi-robot exploration missions in underground environments, where both ground and aerial robots are deployed, with different sensor configurations. Copyright © 2023, The Authors. All rights reserved.

关键词： Antennas

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MIMIR-UW: A Multipurpose Synthetic Dataset for Underwater Navigation and Inspection

MIMIR-UW: A Multipurpose Synthetic Dataset for Underwater Na...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Olaya Álvarez-Tuñón Hemanth Kanner Luiza Ribeiro Marnet Huy Xuan Pham Jonas le Fevre Sejersen Yury Brodskiy Erdal Kayacan Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (AiRLab) Aarhus University Aarhus C Denmark EIVA a/s Skanderborg Denmark Automatic Control Group (RAT) Paderborn University Paderborn Germany

This paper presents MIMIR-UW, a multipurpose underwater synthetic dataset for SLAM, depth estimation, and object segmentation to bridge the gap between theory and application in underwater environments. MIMIR-UW integrates three camera sensors, inertial measurements, and ground truth for robot pose, image depth, and object segmentation. The underwater robot is deployed within a pipe exploration scenario, carrying artificial lights that create uneven lighting, in addition to natural artefacts such as reflections from natural light and backscattering effects. Four environments totalling eleven tracks are provided, with various difficulties regarding light conditions or dynamic elements. Two metrics for dataset evaluation are proposed, allowing MIMIR-UW to be compared with other datasets. State-of-art methods on SLAM, segmentation and depth estimation are deployed and benchmarked on MIMIR-UW. Moreover, the dataset's potential for sim-to-real transfer is demonstrated by leveraging the segmentation and depth estimation models trained on MIMIR-UW in a real pipeline inspection scenario. To the best of the authors' knowledge, this is the first underwater dataset targeted for such a variety of methods. The dataset is publicly available online. https://***/remaro-network/MIMIR-UW/

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Steerable 3D Spherical Neurons

arXiv

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arXiv 2021年

作者： Melnyk, Pavlo Felsberg, Michael Wadenbäck, Mårten Computer Vision Laboratory Department of Electrical Engineering Linköping University Sweden

Emerging from low-level vision theory, steerable filters found their counterpart in prior work on steerable convolutional neural networks equivariant to rigid transformations. In our work, we propose a steerable feed-forward learning-based approach that consists of neurons with spherical decision surfaces and operates on point clouds. Such spherical neurons are obtained by conformal embedding of Euclidean space and have recently been revisited in the context of learning representations of point sets. Focusing on 3D geometry, we exploit the isometry property of spherical neurons and derive a 3D steerability constraint. After training spherical neurons to classify point clouds in a canonical orientation, we use a tetrahedron basis to quadruplicate the neurons and construct rotation-equivariant spherical filter banks. We then apply the derived constraint to interpolate the filter bank outputs and, thus, obtain a rotation-invariant network. Finally, we use a synthetic point set and real-world 3D skeleton data to verify our theoretical findings. The code is available at https://***/pavlo-melnyk/steerable-3d-neurons. Copyright © 2021, The Authors. All rights reserved.

关键词： Filter banks

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Optimized Admittance Control for Manipulators Interacting with Unknown Environment

Optimized Admittance Control for Manipulators Interacting wi...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Haiyi Kong Guangzhu Peng Guang Li Chenguang Yang Department of Electrical and Electronic Engineering University of Manchester Oxford Rd Manchester UK Bristol Robotics Laboratory University of the West of England Bristol UK School of Automation Nanjing University of Information Science and Technology Nanjing China Department of Computer Science University of Liverpool Liverpool UK

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

This paper considers the study scenario that the end-effector of a manipulator follows a desired trajectory and interacts with external environment. To maximize the interaction performance, admittance control is combined with adaptive dynamic programming (ADP). The optimal admittance parameters can be learned online without prior knowledge of the environment. A data-driven Hybrid Iteration is employed in the ADP, which can relax the initial stabilizing requirement and at the same time has a faster convergence rate compared with Value Iteration. In addition, a more accurate environment model is considered in the system control design, where a general iterative expression is proposed to describe the varying contour of the environment. At last, simulation and experimental studies are given to verify the effectiveness of the proposed method.

关键词： Adaptation models Control design Heuristic algorithms End effectors Dynamic programming Trajectory Iterative methods

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Advances in artificial joint bio-friction and wear simulation assessment technology

Advances in artificial joint bio-friction and wear simulatio...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Zhang, Yanyan Liang, Yiming Zhu, Shiqiang Ding, Menglong Bai, Yunhe Du, Haoyuan Gu, Jason Li, Tiefeng Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H4R2 Canada Center for X-mechanics Zhejiang University Hangzhou310027 China Zhejiang University Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Department of Engineering Mechanics Hangzhou310027 China

ISBN: (纸本)9781665481090

Natural joints have excellent low-friction properties that positively inspire the frictional wear properties of artificial joints. Replacing damaged biological joints with artificial ones can restore bio-function and reduce the pain of arthritic patients. Improvements in the frictional wear properties of artificial joints will effectively increase the life of the prosthesis, especially for young arthritic patients. This paper reviews the lubrication mechanism of natural joints before systematically introducing the development of lubrication and wear simulation techniques for artificial joints, offering references and insights for further studies of artificial joints and the optimization of tribological properties of commercial artificial joint prostheses. © 2022 IEEE.

关键词： Friction

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Predictor-Based Constrained Fixed-Time Sliding Mode Control of Multi-Uav Formation Flight

SSRN

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SSRN 2024年

作者： Khodaverdian, Maria Hajshirmohamadi, Shahram Hakobyan, Aleksandr Ijaz, Salman Center for Scientific Innovation and Education Aerial Robotics Laboratory of National Polytechnic University of Armenia Yerevan Armenia Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Key Laboratory of More Electric Aircraft and Control System Lab University of Nottingham Ningbo China

In this work, a predictor-based fixed-time sliding mode control is designed to tackle the problem of achieving precise trajectory tracking control of multiple unmanned aerial vehicles formation flight. The proposed approach simultaneously addresses various practical requirements, such as optimality, constraints, fixed-time convergence, external interferences, and computational ***, a constrained performance index is constructed based on the prediction of the fixed-time sliding surface, the reaching law, and the control input. Secondly, optimal control input is calculated by solving the constrained quadratic optimization problem. An outstanding feature of this study is the theoretical guarantee of closed-loop system stability in the presence of external disturbances and ***, the comparative evaluation with sliding mode predictive control law and fixed-time sliding mode control methods demonstrates that the proposed approach achieves a straightforward control method with improved performance and acceptable accuracy. Finally, a test is conducted to demonstrate the algorithm's potential for hardware implementation by employing Hardware-in-the-Loop (HIL) test. © 2024, The Authors. All rights reserved.

关键词： Sliding mode control

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Toward AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

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IEEE Internet of Things Journal 2025年第11期12卷 16990-16997页

作者： Imran Ahmed Misbah Ahmad Muftooh Ur Rehman Siddiqi Abdellah Chehri Gwangill Jeon School of Computing and Information Science Anglia Ruskin University Cambridge U.K. Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol U.K. Department of Animal and Agriculture Hartpury University Gloucester U.K. College of Engineering and Physical Sciences Aston University Birmingham U.K. Department of Mathematics and Computer Science Royal Military College of Canada Kingston ON Canada Department of Embedded Systems Engineering Incheon National University Incheon South Korea

In the rapidly advancing field of intelligent transportation systems, integrating artificial intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning-based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios-based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model’s effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model’s capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step toward recognizing the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology.

关键词： Computational modeling Object detection Edge computing Image edge detection Artificial intelligence computer architecture Data models Accuracy Servers Safety

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Bidirectional Search Strategy for Incremental Search-based Path Planning

Bidirectional Search Strategy for Incremental Search-based P...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Chenming Li Han Ma Jiankun Wang Max Q.-H. Meng Department of Electronic Engineering The Chinese University of Hong Kong Shatin N.T. Hong Kong SAR China Department of Electronic and Electrical Engineering Shenzhen Key Laboratory of Robotics Perception and Intelligence Southern University of Science and Technology Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Department of Electronic and Electrical Engineering Shenzhen Key Laboratory of Robotics Per-ception and Intelligence Southern University of Science and Technology Shenzhen China Emeritus in the Department of Electronic Engineering The Chinese University of Hong Kong in Hong Kong Department of Electrical and Computer Engineering University of Alberta in Canada

Planning a collision-free path efficiently among obstacles is crucial in robotics. Conventional one-shot unidirectional path planning algorithms work well in the static environment, but cannot respond to the environment changes timely in the dynamic environment. To tackle this issue and improve the search efficiency, we propose a bidirectional incremental search method, Bidirectional Lifelong Planning A* (BLPA*), which searches in the forward and backward directions and performs incremental search bidirectionally when the environment changes. Furthermore, inspired by the robot perception range limitation and BLPA*, we propose the fractional bidirectional D* Lite (fBD* Lite(d p )), which constraints the forward search to the robot perception range and uses the backward search to expand the rest area. Our simulation results demonstrate BLPA* and mD* Lite(d p ) can achieve superior performance in the dynamic environment. It reveals that the bidirectional incremental search strategy can be a general and efficient technique for graph-search-based robot path planning methods.

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