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

作者： Zhengcai Cao Junnian Li Jie Niu MengChu Zhou State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China College of Information Science and Technology Beijing University of Chemical Technology Beijing China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Although remarkable advances have been achieved in generic object detection, small object detection (SOD) remains challenging owing to small objects’ information loss and noisy representation caused by their non-uniform distribution. Their limited width and height, scale variations, and redundant computation make SOD hard. To overcome them, this work proposes a new SOD method based on sparse convolutional network (SCNet) and Query Mechanism called QuerySOD. First, an extended feature pyramid network is constructed for extracting feature maps of small objects with more regional details. Then, a Sparse Head is neatly designed by using SCNet for accelerating the interfering speed and obtaining weights of each layer. After that, a Query Mechanism is innovatively introduced for harvesting the benefit of sparse value feature maps from the Sparse Head. QuerySOD is evaluated on public benchmarks including COCO and VisDrone. Finally, we apply it on ‘Jinghai’ unmanned survey vehicles and receive excellent SOD performance from this real-world application.

关键词： Surveys Accuracy Object detection Seaports Lakes Feature extraction Prediction algorithms Convolutional neural networks Security Noise measurement

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Improving safety in physical human-robot collaboration via deep metric learning

arXiv

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arXiv 2023年

作者： Rezayati, Maryam Zanni, Grammatiki Zaoshi, Ying Scaramuzza, Davide van de Venn, Hans Wernher Institute of Mechatronics Systems Zurich University of Applied Sciences Winterthur Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Robotics and Perception Group University of Zurich Zurich Switzerland

Direct physical interaction with robots is becoming increasingly important in flexible production scenarios, but robots without protective fences also pose a greater risk to the operator. In order to keep the risk potential low, relatively simple measures are prescribed for operation, such as stopping the robot if there is physical contact or if a safety distance is violated. Although human injuries can be largely avoided in this way, all such solutions have in common that real cooperation between humans and robots is hardly possible and therefore the advantages of working with such systems cannot develop its full potential. In human-robot collaboration scenarios, more sophisticated solutions are required that make it possible to adapt the robot’s behavior to the operator and/or the current situation. Most importantly, during free robot movement, physical contact must be allowed for meaningful interaction and not recognized as a collision. However, here lies a key challenge for future systems: detecting human contact by using robot proprioception and machine learning algorithms. This work uses the Deep Metric Learning (DML) approach to distinguish between noncontact robot movement, intentional contact aimed at physical human-robot interaction, and collision situations. The achieved results are promising and show show that DML achieves 98.6% accuracy, which is 4% higher than the existing standards (i.e. a deep learning network trained without DML). It also indicates a promising generalization capability for easy portability to other robots (target robots) by detecting contact (distinguishing between contactless and intentional or accidental contact) without having to retrain the model with target robot data. © 2023, CC BY-NC-SA.

关键词： Risk assessment

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A Context-Enhanced Full-Resolution Floor Plan Segmentation Network for Topological Semantic Mapping

A Context-Enhanced Full-Resolution Floor Plan Segmentation N...

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

作者： Zhengcai Cao Yiyang Sun Zhe Ma MengChu Zhou State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China College of Information Science and Technology Beijing University of Chemical Technology Beijing China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Topological semantic maps provide a practical solution to enhance indoor navigation for the Partially Sighted or Visually Impaired (PSVI). Segmenting indoor floor plans and extracting boundaries are key to constructing these maps. The existing methods exhibit low accuracy in segmentation. To achieve desired high segmentation accuracy, we introduce a Context-Enhanced Full-Resolution Network (CEFRN) for floor plan segmentation. It is designed to harness the shallow detailed features and inter-category contextual dependencies inherent in floor plans. CEFRN integrates modified residual blocks to capture the low-stage full-resolution features while maintaining its compactness. A position attention module is employed to refine the deep-stage contextual information. We also propose a two-dimensional deep supervision method to merge features from both stages, which significantly boosts the feature representation ability of CEFRN. Finally, a practical topological semantic mapping method for PSVI indoor navigation is introduced. Experimental results demonstrate that CEFRN’s segmentation accuracy well exceeds the state-of-the-art methods’. It can be used to well support accurate topological semantic mapping.

关键词： Accuracy Hospitals Indoor navigation Semantics Feature extraction Mobile robots Floors Intelligent robots Autonomous vehicles Drones

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Spatio-temporal Motion Planning for Autonomous Vehicles with Trapezoidal Prism Corridors and Bézier Curves

Spatio-temporal Motion Planning for Autonomous Vehicles with...

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American Control Conference (ACC)

作者： Srujan Deolasee Qin Lin Jialun Li John M. Dolan Department of Computer Science & Information Systems Birla Institute of Technology and Science Pilani Rajasthan India Department of Electrical Engineering and Computer Science Cleveland State University Cleveland OH USA Dajiang Innovation Technology Co. Ltd Shenzhen China Robotics Institute Carnegie Mellon University Pittsburgh PA USA

Safety-guaranteed motion planning is critical for self-driving cars to generate collision-free trajectories. A layered motion planning approach with decoupled path and speed planning is widely used for this purpose. This approach is prone to be suboptimal in the presence of dynamic obstacles. Spatial-temporal approaches deal with path planning and speed planning simultaneously; however, the existing methods only support simple-shaped corridors like cuboids, which restrict the search space for optimization in complex scenarios. We propose to use trapezoidal prism-shaped corridors for optimization, which significantly enlarges the solution space compared to the existing cuboidal corridors-based method. Finally, a piecewise Bézier curve optimization is conducted in our proposed corridors. This formulation theoretically guarantees the safety of the continuous-time trajectory. We validate the efficiency and effectiveness of the proposed approach in numerical and CommonRoad simulations.

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Vivim: a Video Vision Mamba for Medical Video Segmentation

arXiv

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arXiv 2024年

作者： Yang, Yijun Xing, Zhaohu Yu, Lequan Huang, Chunwang Fu, Huazhu Zhu, Lei Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology Guangzhou China The Department of Statistics and Actuarial Science The University of Hong Kong Hong Kong Hong Kong Guangdong Provincial People’s Hospital Guangzhou China The Institute of High Performance Computing A*STAR Singapore Robotics and Autonomous Systems Thrust Hong Kong University of Science and Technology Guangzhou China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong

Medical video segmentation gains increasing attention in clinical practice due to the redundant dynamic references in video frames. However, traditional convolutional neural networks have a limited receptive field and transformer-based networks are mediocre in constructing long-term dependency from the perspective of computational complexity. This bottleneck poses a significant challenge when processing longer sequences in medical video analysis tasks using available devices with limited memory. Recently, state space models (SSMs), famous by Mamba, have exhibited impressive achievements in efficient long sequence modeling, which develops deep neural networks by expanding the receptive field on many vision tasks significantly. Unfortunately, vanilla SSMs failed to simultaneously capture causal temporal cues and preserve non-casual spatial information. To this end, this paper presents a Video Vision Mamba-based framework, dubbed as Vivim, for medical video segmentation tasks. Our Vivim can effectively compress the long-term spatiotemporal representation into sequences at varying scales with our designed Temporal Mamba Block. We also introduce an improved boundary-aware affine constraint across frames to enhance the discriminative ability of Vivim on ambiguous lesions. Extensive experiments on thyroid segmentation, breast lesion segmentation in ultrasound videos, and polyp segmentation in colonoscopy videos demonstrate the effectiveness and efficiency of our Vivim, superior to existing methods. The code is available at: https://***/scottyjyang/Vivim. The dataset will be released once accepted. © 2024, CC BY.

关键词： Deep neural networks

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Automated Disassembly Sequence Prediction for Industry 4.0 Using Enhanced Genetic Algorithm

引用

computers, Materials & Continua 2021年第11期69卷 2531-2548页

作者： Anil Kumar Gulivindala M.V.A.Raju Bahubalendruni R.Chandrasekar Ejaz Ahmed Mustufa Haider Abidi Abdulrahman Al-Ahmari Department of Mechanical Engineering Industrial Robotics and Manufacturing Automation LaboratoryNational Institute of TechnologyKariakal609609India Department of Computer Science Engineering National Institute of TechnologyKariakal609609India Raytheon Chair for Systems Engineering Advanced Manufacturing InstituteKing Saud UniversityRiyadh11421Saudi Arabia Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The evolution of Industry 4.0 made it essential to adopt the Internet of Things(IoT)and Cloud Computing(CC)technologies to perform activities in the new age of *** technologies enable collecting,storing,and retrieving essential information from the manufacturing *** collected at sites are shared with others where execution automatedly *** obtained information must be validated at manufacturing to avoid undesirable data losses during the de-manufacturing ***,information sharing from the assembly level at the manufacturing stage to disassembly at the product end-of-life state is a major *** current research validates the information optimally to offer a minimum set of activities to complete the disassembly *** optimal disassembly sequence plan(DSP)can possess valid information to organize the necessary actions in ***,finding an optimal DSP is complex because of its combinatorial *** genetic algorithm(GA)is a widely preferred artificial intelligence(AI)algorithm to obtain a near-optimal solution for the DSP *** converging nature at local optima is a limitation in the traditional *** study improvised the GA workability by integrating with the proposed priori crossover *** optimality function is defined to reduce disassembly effort by considering directional changes as *** enhanced GA method is tested on a real-time product to evaluate the *** obtained results reveal that diversity control depends on the operators employed in the disassembly *** proposed method’s solution can be stored in the cloud and shared through IoT devices for effective resource allocation and disassembly for maximum recovery of the *** effectiveness of the proposed enhanced GA method is determined by making a comparative assessment with traditional GA and other AI methods at different population sizes.

关键词： Automation internet of things disassembly sequence planning priori cross over operator enhanced GA disassembly predicates

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Biomechanical models in the lower-limb exoskeletons development: A review

TechRxiv

引用

TechRxiv 2023年

作者： Firouzi, Vahid Seyfarth, Andre Song, Seungmoon Von Stryk, Oskar Sharbafi, Maziar Ahmad Lauflabor Locomotion Laboratory Institute of Sport Science TU Darmstadt Germany Simulation Systems Optimization and Robotics Group Department of Computer Science TU Darmstadt Germany Mechanical and Industrial Engineering Northeastern University United States

Lower limb exoskeletons serve multiple purposes, like supporting and augmenting movement. Biomechanical models are practical tools to understand human movement, and motor control. This review explores the applications of biomechanical gait models in the development of lower-limb exoskeletons. Biomechanical models can be broadly categorized as conceptual and detailed models. Biomechanical models can be used for the design, control, and assessment of exoskeletons. Also, these models can be used to estimate unmeasurable or hard-to-measure variables, which is also useful within the aforementioned applications. The analysis of applications and research areas aims to inspire further advancements in this field and contribute to the development of more effective exoskeletons. We highlight validating simulation studies, improving the accuracy and fidelity of biomechanical models, and benefiting from exoskeletons to enhance or validate biomechanical models as future research areas related to biomechanical models. We also emphasize the exploration of model-based design and control approaches for exoskeletons targeting pathological gait, and utilizing biomechanical models for diverse design objectives of exoskeletons. Also, increasing the availability of open-source resources accelerates exoskeleton and biomechanical model advancement. Our review reveals existing needs and cracks new perspectives for the development of more usable and human-compatible exoskeletons based on biomechanical models. © 2023, CC BY.

关键词： Exoskeleton (robotics)

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A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learning

arXiv

引用

arXiv 2023年

作者： Valdeira, Pedro Chi, Yuejie Soares, Cláudia Xavier, João CMU IST United States CMU United States NOVA IST Department of Electrical and Computer Engineering Carnegie Mellon University United States Institute for Systems and Robotics Instituto Superior Técnico Portugal Department of Computer Science NOVA School of Science and Technology Portugal

Communication efficiency is a major challenge in federated learning (FL). In client-server schemes, the server constitutes a bottleneck, and while decentralized setups spread communications, they do not necessarily reduce them due to slower convergence. We propose Multi-Token Coordinate Descent (MTCD), a communication-efficient algorithm for semi-decentralized vertical federated learning, exploiting both client-server and client-client communications when each client holds a small subset of features. Our multi-token method can be seen as a parallel Markov chain (block) coordinate descent algorithm and it subsumes the client-server and decentralized setups as special cases. We obtain a convergence rate of O(1/T) for nonconvex objectives when tokens roam over disjoint subsets of clients and for convex objectives when they roam over possibly overlapping subsets. Numerical results show that MTCD improves the state-of-the-art communication efficiency and allows for a tunable amount of parallel communications. © 2023, CC BY.

关键词： Efficiency

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Guest Editorial: Internet of things for e-health applications

引用

IEEE Internet of Things Magazine 2021年第3期4卷 4-5页

作者： Gupta, Deepak De Albuquerque, Victor Hugo C. Peng, Sheng-Lung Khanna, Ashish Nhu, Nguyen Gia Castillo, Oscar Wiley Expert Systems United States Brazil National Taipei University of Business Department of Creative Technologies and Product Design Taiwan Maharaja Agrasen Institute of Technology Under GGSIPU Department of Computer Science and Engineering Delhi India Graduate School Duy Tan University Viet Nam Tijuana Institute of Technology Graduate Division Tijuana Mexico

In recent years, the Internet of Things (IoT) has become an emerging technology, which is gaining popularity among researchers and practitioners. Due to its multidisciplinary approach, IoT has been phenomenal in revolutionizing many aspects of traditional healthcare paradigms. At the same time, traditional healthcare systems can no longer satisfy the needs of a continuously growing and developing society.

关键词： Special issues and sections Internet of Things Electronic healthcare Biomedical monitoring Medical services Smart healthcare

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A multiplexed FBG based sensor platform for flow and temperature measurements in the Baltic Sea

A multiplexed FBG based sensor platform for flow and tempera...

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2023 European Workshop on Optical Fibre Sensors, EWOFS 2023

作者： Dzipalski, A. Morton, J.A.S. Papachristou, N. Maier, R.R.J. MacPherson, W.N. Ristolainen, A. Kruusmaa, M. Reilent, E. Wolf, B.J. Pirih, P. van Netten, S.M. Shuhova, I. Lips, U. McFarlane, N. Macleod, R. Hendry, M. Sheehy, J. Almoghayer, M. Rojas, N. Davies, G. Hakim, A. Ng, A. Heriot-Watt University Institute of Photonics and Quantum Sciences Edinburgh United Kingdom Tallinn Institute of Technology Centre for Bio Robotics Tallinn Estonia University of Groningen Computer Science and Artificial Intelligence Groningen Netherlands Tallinn Institute of technology Department of Marine Systems Tallinn Estonia Hydrobond Engineering Aberdeen Energy Park Aberdeen United Kingdom Aquatera Ltd The Charles Coulson Building Stromness United Kingdom Orcades Marine Warness Park Kirkwall United Kingdom

ISBN: (纸本)9781510665002

In this paper a multiplexed multi-parameter marinized sensory array is described. This was deployed on the continental slope off the Keri Island marine observatory in the Gulf of Finland (Estonia). The sensor array is made up of 4 measurement stations which are connected in series. Across these measurement stations, a total of 16 temperature sensors, 4 attitude sensors (each consisting of 3 individual fiber sensors) and 16 flow sensors were successfully deployed. They were addressed over 1.1 km via 20 single-mode (SMF-28e) optical fibers contained in a single marine compliant ruggedized umbilical. The bio-inspired fiber optic flow sensors are designed to mimic the behavior of the superficial neuromasts naturally found as part of the lateral line sensory organ present in fish. The sensor is composed of optical fibers inscribed with Fiber Bragg Gratings glued together in a polymer matrix which are then encapsulated in a polyurethane shell. The sensors response has been tested in DC flows in a tow tank and have demonstrated the ability of measuring flow speed from 0.05 ms-1 to 2.5 ms-1. The main aim of the deployment was to demonstrate the capabilities of fiber sensor technology for long-term oceanographic applications. The measurement period described lasted over two months and the sensor system performed well in comparison with data was gathered from commercial instrumentation available. © 2023 SPIE.

关键词： Fiber Bragg gratings

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