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Linear Auto-calibration of Pan-Tilt-Zoom Cameras With Rotation Center Offset

Linear Auto-calibration of Pan-Tilt-Zoom Cameras With Rotati...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Yu Liu Hui Zhang Department of Computer Science Hong Kong Baptist University Hong Kong China Department of Computer Science United International College BNU-HKBU Zhuhai China Department of Computer Science United International College Beijing Normal University-Hong Kong Baptist University Zhuhai China

This paper addresses the linear auto-calibration problem of a pan-tilt-zoom (PTZ) camera. Unlike existing methods, we take full advantage of the offset of the camera center from the rotation center, which is usually non-negligible in bullet-type PTZ cameras. Without any prior assumption, we propose a linear method to recover all intrinsic parameters. First, we successively acquired at least four images using the zoom and rotation capabilities of the PTZ camera. Second, using the homography of two images at the same location but different scales, the principal point and zoom scalar can be linearly recovered. Finally, based on the unknown offset of the camera center and rotation center, we propose a linear method to solve the scale factor in the Kruppa equation and recover the remaining camera intrinsic parameters, namely focal lengths and skew. Synthetic and real experiments demonstrate the feasibility of our approach.

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Aspect-Ratio and Scale Aware Rotated Defect Detection for Fiber-reinforced Composites

Aspect-Ratio and Scale Aware Rotated Defect Detection for Fi...

引用

Computer Vision, Image and Deep Learning (CVIDL), international conference on

作者： Yuanye Xu Yuanhao Liu Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Key Laboratory of Networked Control System Shenyang Institute of Automation Chinese Academy of Sciences Shenyang Institute of Automation University of Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

Detecting surface defects in carbon fiber-reinforced composites (FRCs) during prepreg stacking is crucial for ensuring product quality. Traditional methods using horizontal bounding boxes (HBBs) for defect detection often lead to overlapping boxes and inaccurate results. This paper proposes an Aspect-Ratio and Scale Aware Detector network (ARSADet) specifically designed for rotated defect detection in FRCs. The network incorporates a Gabor convolutional network for feature extraction, an aspect-ratio and scale-aware label assignment method, and a newly established FRCs Oriented Defect Detection dataset (FODD). Evaluation on the FODD dataset demonstrates superior performance compared to existing methods.

关键词： Deep learning Stacking Detectors Optical fiber networks Feature extraction Product design Quality assessment

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Generative Modeling of Residuals for Real-Time Risk-Sensitive Safety with Discrete-Time Control Barrier Functions

Generative Modeling of Residuals for Real-Time Risk-Sensitiv...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Ryan K. Cosner Igor Sadalski Jana K. Woo Preston Culbertson Aaron D. Ames Department of Mechanical and Civil Engineering California Institute of Technology Pasadena CA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

A key source of brittleness for robotic systems is the presence of model uncertainty and external disturbances. Most existing approaches to robust control either seek to bound the worst-case disturbance (which results in conservative behavior), or to learn a deterministic dynamics model (which is unable to capture uncertain dynamics or disturbances). This work proposes a different approach: training a state-conditioned generative model to represent the distribution of error residuals between the nominal dynamics and the actual system. In particular we introduce the Online Risk-Informed Optimization controller (ORIO), which uses Discrete-Time Control Barrier Functions, combined with a learned, generative disturbance model, to ensure the safety of the system up to some level of risk. We demonstrate our approach in simulations and hardware, and show that our method can learn a disturbance model that is accurate enough to enable risk-sensitive control of a quadrotor flying aggressively with an unmodelled slung load. We use a conditional variational autoencoder (CVAE) to learn a state-conditioned dynamics residual distribution, and find that the resulting controller can run at 100Hz on an embedded computer and exhibits less conservative behavior while retaining theoretical safety properties.

关键词： Training Uncertainty Computational modeling Stochastic systems Real-time systems Data models Safety

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DESIGN TARGET ACHIEVEMENT INDEX: A DIFFERENTIABLE METRIC TO ENHANCE DEEP GENERATIVE models IN MULTI-OBJECTIVE INVERSE DESIGN

DESIGN TARGET ACHIEVEMENT INDEX: A DIFFERENTIABLE METRIC TO ...

引用

ASME international Design Engineering Technical conferences / 42nd Annual Computers and Information in Engineering conference (IDETC-CIE) / 48th Design automation conference (DAC)

作者： Regenwetter, Lyle Ahmed, Faez MIT Dept Mech Engn Cambridge MA 02139 USA

ISBN: (纸本)9780791886236

Deep Generative Machine Learning models have been growing in popularity across the design community thanks to their ability to learn and mimic complex data distributions. While early works are promising, further advancement will depend on addressing several critical considerations such as design quality, feasibility, novelty, and targeted inverse design. We propose the Design Target Achievement Index (DTAI), a differentiable, tunable metric that scores a design's ability to achieve designer-specified minimum performance targets. We demonstrate that DTAI can drastically improve the performance of generated designs when directly used as a training loss in Deep Generative models. We apply the DTAI loss to a Performance-Augmented Diverse GAN (PaDGAN) and demonstrate superior generative performance compared to a set of baseline Deep Generative models including a Multi-Objective PaDGAN and specialized tabular generation algorithms like the Conditional Tabular GAN (CTGAN). We further enhance PaDGAN with an auxiliary feasibility classifier to encourage feasible designs. To evaluate methods, we propose a comprehensive set of evaluation metrics for generative methods that focus on feasibility, diversity, and satisfaction of design performance targets. methods are tested on a challenging benchmarking problem: the FRAMED bicycle frame design dataset featuring mixed-datatype parametric data, heavily skewed and multimodal distributions, and ten competing performance objectives.

关键词： Design Bicycles Machine learning Algorithms

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Region of Interest Loss for Anonymizing Learned Image Compression

Region of Interest Loss for Anonymizing Learned Image Compre...

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IEEE international conference on automation Science and Engineering (CASE)

作者： Christoph Liebender Ranulfo Bezerra Kazunori Ohno Satoshi Tadokoro Graduate School of Information Sciences Tohoku University Japan Robotics Group of Julius Maximilian University Würzburg Germany

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

The use of AI in public spaces continually raises concerns about privacy and the protection of sensitive data. An example is the deployment of detection and recognition methods on humans, where images are provided by surveillance cameras. This results in the acquisition of great amounts of sensitive data, since the capture and transmission of images taken by such cameras happens unaltered, for them to be received by a server on the network. However, many applications do not explicitly require the identity of a given person in a scene; An anonymized representation containing information of the person’s position while preserving the context of them in the scene suffices. We show how using a customized loss function on region of interests (ROI) can achieve sufficient anonymization such that human faces become unrecognizable while persons are kept detectable, by training an end-to-end optimized autoencoder for learned image compression that utilizes the flexibility of the learned analysis and reconstruction transforms for the task of mutating parts of the compression result. This approach enables compression and anonymization in one step on the capture device, instead of transmitting sensitive, nonanonymized data over the network. Additionally, we evaluate how this anonymization impacts the average precision of pre-trained foundation models on detecting faces (MTCNN) and humans (YOLOv8) in comparison to non-ANN based methods, while considering compression rate and latency. The code and model weights for this approach, including usage examples are publicly available on GitHub: https://***/BRN-Hub/anon-compression

关键词： Training Data privacy Image coding Surveillance Transform coding Transforms Cameras Information filtering Faces Information integrity

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Safety-Critical Control for Underwater Vehicles with Model Uncertainties and External Disturbances

Safety-Critical Control for Underwater Vehicles with Model U...

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

作者： Yizong Chen Zhiqiang Miao Weiwei Zhan Yaonan Wang College of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center of Robot Visual Perception and Control Technology Changsha China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Safety is a crucial issue for underwater vehicles, which may be affected by narrow terrain and multiple obstacles. In addition, the model of the underwater vehicles are uncertain and susceptible to external disturbances such as water flow. This article utilizes model predictive control (MPC) and incremental nonlinear dynamic inversion (INDI) to design a robust control scheme for underwater vehicles. The position loop controller employs MPC to generate the required speed commands for the velocity loop controller. The velocity loop is designed with an INDI control scheme incorporating a second-order low-pass filter, effectively mitigating model uncertainties and external disturbances on the vehicles. Based on exponential control barrier functions (ECBFs), the input constraint and obstacle avoidance problems of underwater vehicles are solved. The results indicate that the proposed control scheme not only exhibits robustness but also effectively ensures safe obstacle avoidance.

关键词： Uncertainty Three-dimensional displays Trajectory tracking Numerical simulation Robustness Safety Numerical models Collision avoidance Vehicle dynamics Underwater vehicles

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Domain Adaptation in LiDAR Semantic Segmentation by Aligning Class Distributions 18

Domain Adaptation in LiDAR Semantic Segmentation by Aligning...

引用

18th international conference on Informatics in Control, automation and robotics (ICINCO)

作者： Alonso, Inigo Riazuelo, Luis Montesano, Luis Murillo, Ana C. Univ Zaragoza DIIS I3A RoPeRt Grp Zaragoza Spain Bitbrain Zaragoza Spain

ISBN: (纸本)9789897585227

LiDAR semantic segmentation provides 3D semantic information about the environment, an essential cue for intelligent systems, such as autonomous vehicles, during their decision making processes. Unfortunately, the annotation process for this task is very expensive. To overcome this, it is key to find models that generalize well or adapt to additional domains where labeled data is limited. This work addresses the problem of unsupervised domain adaptation for LiDAR semantic segmentation models. We propose simple but effective strategies to reduce the domain shift by aligning the data distribution on the input space. Besides, we present a learningbased module to align the distribution of the semantic classes of the target domain to the source domain. Our approach achieves new state-of-the-art results on three different public datasets, which showcase adaptation to three different domains.

关键词： robotics Autonomous Systems LiDAR Deep Learning Semantic Segmentation Domain Adaptation

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Hand Gesture Classification using sEMG Signals and Ensemble Learning

Hand Gesture Classification using sEMG Signals and Ensemble ...

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IEEE international conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS)

作者： Ranjeesh R Chandran D. Devaraj Saurav Ashok Donfred Shaji Sam K Saju Department of Robotics and Automation Adishankara Institute of Engineering and Technology Kalady Kerala India Department of Electronics and Electrical Technology Kalasalingam Academy of Research and Education Srivilliputhur Tamil Nadu India Department of Robotics and Automation Adishankara Inst. of Eng and Tech. Kalady Kerala India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

Electromyographic signals offer details on a muscle activity of a person. In the case of hand movements various combinations of forearm muscles must be activated in order to carry out each gesture, which results in unique electrical patterns. On the other hand, identifying the motion being executed is made possible by the examination of these patterns of muscle activation, which are captured by EMG signals. This article presents a novel implementation on hand gesture classification utilizing Electromyography (EMG) signals recorded through a MYO Thalmic bracelet. The dataset consists of raw data collected from 36 subjects, each executing a series of static hand gestures. The classification model employed for this study is an ensemble of decision trees, achieving an impressive validation accuracy of 98.0% and a test accuracy of 98.1 %. The article outlines the dataset details, recording methodology, and provides an in-depth analysis of the model architecture, hyperparameters, and training results.

关键词： Training Wrist Muscles Sensor phenomena and characterization Predictive models Electromyography Robustness

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Output-feedback MPC for Robotic Systems under Bounded Noise 18

Output-feedback MPC for Robotic Systems under Bounded Noise

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18th international conference on Informatics in Control, automation and robotics (ICINCO)

作者： Pavelkova, Lenka Kuklisova Belda, Kvetoslav Czech Acad Sci Inst Informat Theory & Automat Pod Vodarenskou Vezi 4 Prague 18208 8 Czech Republic

ISBN: (纸本)9789897585227

The paper presents an output-feedback model predictive control applied to the motion control of a dynamic model of a parallel kinematic machine. The controlled system is described by a stochastic linear discrete-time model with bounded disturbances. An approximate uniform Bayesian filter provides set state estimates. The choice of the specific point estimate from this set is a part of the optimization. The cost function includes penalties on the tracking error and the actuation effort respecting increments. Illustrative examples show the effectiveness of the proposed approach and provide a comparison with previous results.

关键词： Model Predictive Control Output-Feedback Control Robot Manipulator State Estimation Bayes methods Bounded Uncertainty

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Object-Centric Instruction Augmentation for Robotic Manipulation

Object-Centric Instruction Augmentation for Robotic Manipula...

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IEEE international conference on robotics and automation (ICRA)

作者： Junjie Wen Yichen Zhu Minjie Zhu Jinming Li Zhiyuan Xu Zhengping Che Chaomin Shen Yaxin Peng Dong Liu Feifei Feng Jian Tang School of Computer Science East China Normal University China Midea Group China Department of Mathematics School of Science Shanghai University China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Humans interpret scenes by recognizing both the identities and positions of objects in their observations. For a robot to perform tasks such as "pick and place", understanding both what the objects are and where they are located is crucial. While the former has been extensively discussed in the literature that uses the large language model to enrich the text descriptions, the latter remains underexplored. In this work, we introduce the Object-Centric Instruction Augmentation (OCI) framework to augment highly semantic and information-dense language instruction with position cues. We utilize a Multi-modal Large Language Model (MLLM) to weave knowledge of object locations into natural language instruction, thus aiding the policy network in mastering actions for versatile manipulation. Additionally, we present a feature reuse mechanism to integrate the vision-language features from off-the-shelf pre-trained MLLM into policy networks. Through a series of simulated and real-world robotic tasks, we demonstrate that robotic manipulator imitation policies trained with our enhanced instructions outperform those relying solely on traditional language instructions.

关键词： Knowledge engineering Visualization Large language models Human intelligence Semantics Natural languages Manipulators

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