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Efficient Multi-sensor Aided Inertial Navigation with Online Calibration

Efficient Multi-sensor Aided Inertial Navigation with Online...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Lee, Woosik Yang, Yulin Huang, Guoquan Univ Delaware Robot Percept & Nav Grp RPNG Newark DE 19716 USA

ISBN: (纸本)9781728190778

In this paper, we design a versatile multi-sensor aided inertial navigation system (MINS) that can efficiently fuse multi-modal measurements of IMU, camera, wheel encoder, GPS, and 3D LiDAR along with online spatiotemporal sensor calibration. Building upon our prior work [1]-[3], in this work we primarily focus on efficient LiDAR integration in a sliding-window filtering fashion. As each 3D LiDAR scan contains a large volume of 3D points which poses great challenges for real-time performance, we advocate using plane patches, which contain the environmental structural information, extracted from the sparse LiDAR point cloud to update/calibrate the system efficiently. The proposed LiDAR plane patch processing algorithm (including extraction, data association, and update) is shown to he efficient and consistent. Both Extensive Monte-Carlo simulations and real-world datasets with large-scale urban driving scenarios have been used to verify the accuracy and consistency of the proposed MINS algorithm.

关键词： Laser radar Three-dimensional displays Monte Carlo methods Wheels Inertial navigation Robot sensing systems Real-time systems

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Deep Hierarchical Rotation Invariance Learning with Exact Geometry Feature Representation for Point Cloud Classification

Deep Hierarchical Rotation Invariance Learning with Exact Ge...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Lin, Jianjie Rickert, Markus Knoll, Alois Tech Univ Munich Dept Informat Robot Artificial Intelligence & Real Time Syst Munich Germany

ISBN: (纸本)9781728190778

Rotation invariance is a crucial property for 3D object classification, which is still a challenging task. State-of-the-art deep learning-based works require a massive amount of data augmentation to tackle this problem. This is however inefficient and classification accuracy suffers a sharp drop in experiments with arbitrary rotations. We introduce a new descriptor that can globally and locally capture the surface geometry properties and is based on a combination of spherical harmonics energy and point feature representation. The proposed descriptor is proven to fulfill the rotation-invariant property. A limited bandwidth spherical harmonics energy descriptor globally describes a 3D shape and its rotation-invariant property is proven by utilizing the properties of a Wigner D-matrix, while the point feature representation captures the local features with a KNN to build the connection to its neighborhood. We propose a new network structure by extending PointNet++ with several adaptations that can hierarchically and efficiently exploit local rotation0invariant features. Extensive experimental results show that our proposed method dramatically outperforms most state-of-the-art approaches on standard rotation-augmented 3D object classification benchmarks as well as in robustness experiments on point perturbation, point density, and partial point clouds.

关键词： Geometry Three-dimensional displays Shape Perturbation methods Neural networks Harmonic analysis Robustness

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Efficient Real-time Detection of Complex Tire Cord Defects on Airjet Loom

Efficient Real-time Detection of Complex Tire Cord Defects o...

引用

automation, Control and robotics Engineering (CACRE), international conference on

作者： Juan Du Yuhan Li School of Automation Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350350302

ISBN: (纸本)9798350350319

The paper presents an improved YOLOv8 model tailored for the detection of defects in air-jet loom tire cord fabric, a critical component in the automotive industry. Current defect detection methods, predominantly manual, are inefficient and prone to errors, necessitating an automated solution. Leveraging advancements in machine vision and deep learning, this study presents an vision-based defect detection model that tackles the distinctive obstacles posed by the complex background, variations in target sizes, and indistinct defect features of tire cord fabric defects. The proposed model integrates a novel BiFPN (Bidirectional Feature Pyramid Network) into the YOLOv8 framework to enhance feature fusion and employs a Dynamic Head mechanism to improve scale, spatial, and task awareness. Additionally, a new loss function, Wise-Inner-Shape IoU, is introduced to refine the localization of defects. The model's effectiveness is assessed utilizing a custom dataset of air-jet loom tire cord fabric, demonstrating a notable 4.0 % enhancement over the original YOLOv8 architecture in the mean Average Precision (mAP) at a threshold of 0.5. The results show that the model is effective in achieving precise defect detection, which is essential for upholding quality standards of tire cord fabric in industrial production.

关键词： Location awareness Atmospheric modeling Feature extraction Tires Fabrics Magnetic heads Vehicle dynamics

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MDSTC: A Dynamic Approach to Multi-Robot Coverage Path Planning

MDSTC: A Dynamic Approach to Multi-Robot Coverage Path Plann...

引用

international conference on Control, automation, robotics and Vision (ICARCV)

作者： Weimin Mo Zhiyun Lin School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology Shenzhen China Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Maximizing the efficiency of multi-robot systems is one of the primary objectives in solving the multi-robot coverage path planning (mCPP) problem. During coverage tasks, unexpected imbalances in the multi-robot systems, such as changes in speed, can lead to suboptimal utilization of the system's capabilities, subsequently reducing the efficiency of task execution. In this paper, we developed a multi-robot dynamic spanning tree coverage (MDSTC) algorithm, an online area-division-based approach that adjusts region allocation among robots based on their coverage status through an exchange-based mechanism to enhance multi-robot system efficiency. To validate the effectiveness of the proposed algorithm, we conducted extensive numerical simulations. The results demonstrate that our algorithm achieves a superior solution in scenarios where robot efficiency remains balanced, closely matching the performance of state-of-the-art mCPP methods. Furthermore, when differences in efficiency arise among the robots during task execution, the proposed algorithm provides a dynamic solution that enhances overall coverage efficiency.

关键词： automation Heuristic algorithms Numerical simulation Dynamic scheduling Path planning Multi-robot systems Resource management Robots

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Visual Servoing of Cable-Driven Parallel Robots with Tension Management

Visual Servoing of Cable-Driven Parallel Robots with Tension...

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IEEE international conference on robotics and automation (ICRA)

作者： Zake, Zane Chaumette, Francois Pedemonte, NicolO Caro, Stephane Lab Sci Numer Nantes UMR CNRS 6004 1 Rue Noe F-44321 Nantes France IRT Jules Verne Chemin Chaffault F-44340 Bouguenais France Univ Rennes IRISA CNRS INRIA Rennes France Ctr Natl Rech Sci CNRS 1 Rue Noe F-44321 Nantes France

ISBN: (纸本)9781728190778

Cable-driven parallel robots (CDPRs) are a type of parallel robots, where cables are used instead of rigid links. This leads to many advantages, such as large workspace, low mass in motion and simple reconfiguration. The drawbacks are accuracy issues and complex cable management. Indeed, it is usual that cables become slack. That can be caused by, for example, cable mass, uncertainties in the system, and a higher number of cables than the number of degrees of freedom of the moving-platform. This reduces CDPR stiffness and degree of actuation. While visual servoing provides good accuracy and is robust to different perturbations in the system and to modeling errors, it does not deal with cable slackness. Thus, a CDPR with visual servoing can become underactuated due to cable slack. We propose in this paper to enrich visual servoing with a tension correction algorithm. Experimental results show reduction of slackness and thus avoiding slackness-related trajectory perturbations and loss of stability.

关键词： Parallel robots Feedback loop Uncertainty Perturbation methods Stability criteria Pose estimation Visual servoing

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What Influence Does the Type of Extracted Audio Features Have on Emotional States?

What Influence Does the Type of Extracted Audio Features Hav...

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IEEE international conference on automation, Quality and Testing, robotics, AQTR

作者： Toma Telembici Lorena Muscar Corneliu Rusu Signal Processing Group Faculty of Electronics Telecommunications and Information Technology Technical University of cluj-Napoca Cluj-Napoca Romania

ISBN: (数字)9798350361933

ISBN: (纸本)9798350361940

This paper presents the way in which different audio emotional states are classified using various feature extraction types. Our audio soundbase for robot service application, in Romanian language, alongside with four established soundbases, Crema, Ravdess, Savee and Emo-DB were used in this paper. We analyzed the influence of different feature extraction methods upon classification accuracy. We focus on Mel Frequency Cepstral Coefficients (MFCC), Linear Predictive Coding (LPC), Magnitude-based Spectral Root Cepstral Coefficients (MSRCC), Normalized Gammachirp Cepstral Coefficients (NGCC) and Gammatone Frequency Cepstral Coefficients (GFCC). One classifier, k-nearest neighbor (k-NN), is utilized to evaluate the effectiveness of each feature set in recognizing emotional states. We compared the correct classification rates to determine which features have the best results in the emotional recognition field. We show that by using $30-\mathrm{NGCC}$ and 5 -nearest neighbors we reach an overall correct classification rate of $96.46 \%$ in the testing phase. Computational time was also incorporated into the analysis, including the testing and training phases. This study makes contributions to the field of emotion recognition from audio signals, facilitating advancements in human-computer interaction.

关键词： Training Human computer interaction Emotion recognition automation Accuracy Feature extraction Linear predictive coding

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A Comprehensive Study on Quantization Techniques for Large Language models

A Comprehensive Study on Quantization Techniques for Large L...

引用

Artificial Intelligence, robotics, and Communication (ICAIRC), IEEE international conference on

作者： Jiedong Lang Zhehao Guo Shuyu Huang Data Science Northeastern University Boston USA Information Science University of Pittsburgh Pittsburgh USA Data Science Columbia University New York USA

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

Large Language models (LLMs) have been extensively researched and used in both academia and industry since the rise in popularity of the Transformer model, which demonstrates excellent performance in AI. However, the computational demands of LLMs are immense, and the energy resources required to run them are often limited. For instance, popular models like GPT-3, with 175 billion parameters and a storage requirement of 350 GB, present significant challenges for deployment on resource-constrained IoT devices and embedded systems. These systems often lack the computational capacity to handle such large models. Quantization, a technique that reduces the precision of model values to a smaller set of discrete values, offers a promising solution by reducing the size of LLMs and accelerating inference. In this research, we provide a comprehensive analysis of quantization techniques within the machine learning field, with a particular focus on their application to LLMs. We begin by exploring the mathematical theory of quantization, followed by a review of common quantization methods and how they are implemented. Furthermore, we examine several prominent quantization methods applied to LLMs, detailing their algorithms and performance outcomes.

关键词： Performance evaluation Quantization (signal) Machine learning algorithms Reviews Computational modeling Large language models Machine learning Transformers Internet of Things Robots

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Fast Few-Shot Classification by Few-Iteration Meta-Learning

Fast Few-Shot Classification by Few-Iteration Meta-Learning

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IEEE international conference on robotics and automation (ICRA)

作者： Tripathi, Ardhendu Shekhar Danelljan, Martin Van Gool, Luc Timofte, Radu Swiss Fed Inst Technol Comp Vis Lab Dept Elect Engn Zurich Switzerland

ISBN: (纸本)9781728190778

Autonomous agents interacting with the real world need to learn new concepts efficiently and reliably. This requires learning in a low-data regime, which is a highly challenging problem. We address this task by introducing a fast optimization-based meta-learning method for few-shot classification. It consists of an embedding network, providing a general representation of the image, and a base learner module. The latter learns a linear classifier during the inference through an unrolled optimization procedure. We design an inner learning objective composed of (i) a robust classification loss on the support set and (ii) an entropy loss, allowing transductive learning from unlabeled query samples. By employing an efficient initialization module and a Steepest Descent based optimization algorithm, our base learner predicts a powerful classifier within only a few iterations. Further, our strategy enables important aspects of the base learner objective to be learned during meta-training. To the best of our knowledge, this work is the first to integrate both induction and transduction into the base learner in an optimization-based meta-learning framework. We perform a comprehensive experimental analysis, demonstrating the speed and effectiveness of our approach on four few-shot classification datasets.

关键词： Codes conferences Prediction algorithms Inference algorithms Entropy Reliability Iterative methods

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Hybrid Disassembly Line Balancing for Human-robot Collaborative Remanufacturing

Hybrid Disassembly Line Balancing for Human-robot Collaborat...

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IEEE international conference on automation Science and Engineering (CASE)

作者： Feng Guo XiWang Guo MengChu Zhou Weitian Wang ShuJin Qin Qi Kang College of Information and Control Engineering Liaoning Petrochemical University Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA Collaborative Robotics and Smart Systems Laboratory (CRoSS Lab) Montclair State University Montclair NJ USA College of Economics and Management Shangqiu Normal University Shangqiu P R. China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

As technologies advance rapidly, the urgency of remanufacturing is escalating. Efficient disassembly processes are crucial at the outset of remanufacturing. This work proposes an innovative disassembly scheme aimed at amplifying efficiency through the utilization of parallelization and human-robot collaboration in the context of a hybrid disassembly line. A single-objective mixed-integer programming model is developed to optimize disassembly profit. A discrete salp swarm algorithm is proposed to solve it since it is NP-hard. We have proposed and integrated uniform variation and two-point crossover operators in this algorithm. After comparing its results with those of the exact solver and other intelligent optimization methods, we conclude its competitive performance in both solution quality and efficiency.

关键词： Computer aided software engineering automation Optimization methods Human-robot interaction Collaboration Programming

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Comparison of methods for Predicting Muscle Activations and Knee Joint Contact Forces During Squatting Using OpenSim 1

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3rd international conference on Intelligent Manufacturing and automation, ICIMA 2022

作者： Kothurkar, Rohan Lekurwale, Ramesh Gad, Mayuri Department of Mechanical Engineering K. J. Somaiya College of Engineering Mumbai India St. Xavier’s Gait Lab Xavier Institute of Engineering Mumbai India

ISBN: (数字)9789811979712

ISBN: (纸本)9789811979705

Musculoskeletal modeling and simulation software available give a non-invasive prediction of joint contact forces. However, the results are very sensitive to optimization techniques and model choice. In this study, muscle activations and knee joint contact forces (KJCF) were predicted for squatting using two different optimization techniques along with two musculoskeletal models. OpenSim 4.2 was used to simulate squatting using motion capture data and ground reaction forces. Electromyography (EMG) signals were filtered, rectified, normalized, and compared with predicted muscle activations. Good agreement was observed between predicted muscle activations using OpenSim and the EMG with few exceptions using both models. KJCF showed a significantly large difference using both models and optimization techniques. The study demonstrates the sensitivity of results to optimization techniques and model choice. The study also shows the incapability of musculoskeletal models to predict KJCF during squatting. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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