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检索条件"任意字段=International Conference on Methods and Models in Automation and Robotics"

共 24622 条记录，以下是201-210 订阅

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Ditto in the House: Building Articulation models of Indoor Scenes through Interactive Perception

Ditto in the House: Building Articulation Models of Indoor S...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Hsu, Cheng-Chun Jiang, Zhenyu Zhu, Yuke Univ Texas Austin Dept Comp Sci Austin TX 78712 USA

ISBN: (纸本)9798350323658

Virtualizing the physical world into virtual models has been a critical technique for robot navigation and planning in the real world. To foster manipulation with articulated objects in everyday life, this work explores building articulation models of indoor scenes through a robot's purposeful interactions in these scenes. Prior work on articulation reasoning primarily focuses on siloed objects of limited categories. To extend to room-scale environments, the robot has to efficiently and effectively explore a large-scale 3D space, locate articulated objects, and infer their articulations. We introduce an interactive perception approach to this task. Our approach, named Ditto in the House, discovers possible articulated objects through affordance prediction, interacts with these objects to produce articulated motions, and infers the articulation properties from the visual observations before and after each interaction. It tightly couples affordance prediction and articulation inference to improve both tasks. We demonstrate the effectiveness of our approach in both simulation and real-world scenes. Code and additional results are available at https://***/HouseDitto/

关键词： Robot programming

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Trash Detection in Water Bodies Using Yolo With Explainable AI Insight 6

Trash Detection in Water Bodies Using Yolo With Explainable ...

引用

6th international conference on robotics and automation in Industry, ICRAI 2024

作者： Rasheed, Shafaq Mirza, Amal Saeed, Muhammad Saad Yousaf, Muhammad Haroon University of Engineering and Technology Department of Computer Engineering Taxila Pakistan National Center of Robotics and Automation Swarm Robotics Lab Taxila Pakistan

ISBN: (纸本)9798350355703

Waste disposal into water bodies is a serious concern for environmental engineers, often resulting in urban flooding, soil degradation in agricultural areas, and freshwater pollution. Additionally, trash accumulation in water bodies threatens ma-rine life by introducing contaminants through storm drains, rivers, and other channels. Furthermore, the unique character-istics of underwater environments, including high noise levels, low visibility, and color variation, pose challenges for trash detection. Thus, addressing this issue is crucial for environmental sustainability. The work focuses on both floating and submerged trash in water bodies. Typically, the detection of trash on water bodies is impacted by environmental variability, diverse appearance, and background clutter. These conditions also make usual object detection techniques less effective. Deep learning methods have shown promise in handling challenging visual tasks, therefore researchers have turned to them to get over these challenges. In this work, we focus on applying different versions of YOLO to the problem of water trash detection. To assess these models' performance, we use four different datasets: Trash Flow rate Dataset, DeepTrash, FloW, and TrashICRA. Each dataset represents various trash types under different conditions such as underwater trash and floating trash. Our study attempts to identify the best method suitable for the detection of aquatic waste. Our results highlight the effectiveness of YOLO variants in identifying the presence of trash in water ecosystems. By leveraging Grad-CAM we gain insights into the interpretability and transparency of deep learning models thus enabling Explainable AI. This approach will also contribute to the understanding and trustworthiness of artificial systems deployed for trash detection applications by providing a transparent view of decision making process of deep learning models. © 2024 IEEE.

关键词： Storm sewers

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Real-Time Adaptive Safety-Critical Control with Gaussian Processes in High-Order Uncertain models

Real-Time Adaptive Safety-Critical Control with Gaussian Pro...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Zhang, Yu Wen, Long Yao, Xiangtong Bing, Zenshan Kong, Linghuan He, Wei Knoll, Alois Tech Univ Munich Dept Informat Munich Germany Univ Macau Fac Sci & Technol Dept Elect & Comp Engn Taipa Macau Peoples R China Univ Sci & Technol Beijing Sch Intelligence Sci & Technol Beijing 100083 Peoples R China Univ Sci & Technol Beijing Key Lab Intelligent Bion Unmanned Syst Minist Educ Beijing 100083 Peoples R China

ISBN: (纸本)9798350384581;9798350384574

This paper presents an adaptive online learning framework for systems with uncertain parameters to ensure safety-critical control in non-stationary environments. Our approach consists of two phases. The initial phase is centered on a novel sparse Gaussian process (GP) framework. We first integrate a forgetting factor to refine a variational sparse GP algorithm, thus enhancing its adaptability. Subsequently, the hyperparameters of the Gaussian model are trained with a specially compound kernel, and the Gaussian model's online inferential capability and computational efficiency are strengthened by updating a solitary inducing point derived from newly samples, in conjunction with the learned hyperparameters. In the second phase, we propose a safety filter based on high order control barrier functions (HOCBFs), synergized with the previously trained learning model. By leveraging the compound kernel from the first phase, we effectively address the inherent limitations of GPs in handling high-dimensional problems for real-time applications. The derived controller ensures a rigorous lower bound on the probability of satisfying the safety specification. Finally, the efficacy of our proposed algorithm is demonstrated through real-time obstacle avoidance experiments executed using both simulation platform and a real-world 7-DOF robot.

关键词： Gaussian distribution

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Interactive Object Segmentation in 3D Point Clouds

Interactive Object Segmentation in 3D Point Clouds

引用

IEEE international conference on robotics and automation (ICRA)

作者： Kontogianni, Theodora Celikkan, Ekin Tang, Siyu Schindler, Konrad Swiss Fed Inst Technol Zurich Switzerland ETH AI Ctr Zurich Switzerland Rhein Westfal TH Aachen Comp Vis Grp Aachen Germany

ISBN: (纸本)9798350323658

We propose an interactive approach for 3D instance segmentation, where users can iteratively collaborate with a deep learning model to segment objects directly in a 3D point cloud. Current methods for 3D instance segmentation are generally trained in a fully-supervised fashion, which requires large amounts of costly training labels, and does not generalize well to classes unseen during training. Few works have attempted to obtain 3D segmentation masks using human interactions. Existing methods rely on user feedback in the 2D image domain. As a consequence, users are required to constantly switch between 2D images and 3D representations, and custom architectures are employed to combine multiple input modalities. Therefore, integration with existing standard 3D models is not straightforward. The core idea of this work is to enable users to interact directly with 3D point clouds by clicking on desired 3D objects of interest (or their background) to interactively segment the scene in an open-world setting. Specifically, our method does not require training data from any target domain and can adapt to new environments where no appropriate training sets are available. Our system continuously adjusts the object segmentation based on the user feedback and achieves accurate dense 3D segmentation masks with minimal human effort (few clicks per object). Besides its potential for efficient labeling of large-scale and varied 3D datasets, our approach, where the user directly interacts with the 3D environment, enables new AR/VR and human-robot interaction applications.

关键词： Iterative methods

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Energy-Based models for Cross-Modal Localization using Convolutional Transformers

Energy-Based Models for Cross-Modal Localization using Convo...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Wu, Alan Ryoo, Michael S. MIT Lincoln Lab Lexington MA 02421 USA SUNY Stony Brook Dept Comp Sci Stony Brook NY USA

ISBN: (纸本)9798350323658

We present a novel framework using EnergyBased models (EBMs) for localizing a ground vehicle mounted with a range sensor against satellite imagery in the absence of GPS. Lidar sensors have become ubiquitous on autonomous vehicles for describing its surrounding environment. Map priors are typically built using the same sensor modality for localization purposes. However, these map building endeavors using range sensors are often expensive and time-consuming. Alternatively, we leverage the use of satellite images as map priors, which are widely available, easily accessible, and provide comprehensive coverage. We propose a method using convolutional transformers that performs accurate metric-level localization in a cross-modal manner, which is challenging due to the drastic difference in appearance between the sparse range sensor readings and the rich satellite imagery. We train our model end-to-end and demonstrate our approach achieving higher accuracy than the state-of-the-art on KITTI, Pandaset, and a custom dataset.

关键词： Satellite imagery

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Deep Learning-Based Weed Detection for Precision Agriculture Applications 6

Deep Learning-Based Weed Detection for Precision Agriculture...

引用

6th international conference on robotics and automation in Industry, ICRAI 2024

作者： Ahmed, Saaim Ali, Muhammad Waris Arshad, Muhammad Ali Khan, Shahbaz ARL National University of Sciences and Technology Aerial Robotics Lab Department of Mechanical Engineering Islamabad Pakistan ARL National University of Sciences and Technology Aerial Robotics Lab Department of Robotics and AI Islamabad Pakistan

ISBN: (纸本)9798350355703

Weed detection and management is an intricate and formidable challenge, affecting the efficiency of human agricultural practices. Object detection has merged as a key technique in precision-based agriculture for combatting this issue. This paper analyzes the performance of two cutting-edge object detection models namely YOLOv8x and Faster RCNN on a specialized weed-detection dataset. By leveraging transfer learning, these models proved computationally feasible and accurate. The models were built and trained in a Kaggle environment, promoting a more collaborative approach to developing and implementing deep-learning models. The Faster RCNN model showcased a comparatively better mAP as compared to the YOLOv8x model. This paper highlights the significance of advanced models like Faster RCNN and YOLOv8x for precision agriculture while advocating for further research in deep-learning techniques for promoting resource-efficient agricultural and farming practices. © 2024 IEEE.

关键词： Deep reinforcement learning

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Kolmogorov-Arnold Networks for Online Reinforcement Learning 24

Kolmogorov-Arnold Networks for Online Reinforcement Learning

引用

24th international conference on Control, automation and Systems

作者： Kich, Victor A. Bottega, Jair A. Steinmetz, Raul Grando, Ricardo B. Yorozu, Ayano Ohya, Akihisa Univ Tsukuba Intelligent Robot Lab Tsukuba Ibaraki Japan Univ Fed Santa Maria Technol Ctr Santa Maria RS Brazil Technol Univ Uruguay Robot & AI Lab Rivera Uruguay

ISBN: (纸本)9798331517939;9788993215380

Kolmogorov-Arnold Networks (KANs) have shown potential as an alternative to Multi-Layer Perceptrons (MLPs) in neural networks, providing universal function approximation with fewer parameters and reduced memory usage. In this paper, we explore the use of KANs as function approximators within the Proximal Policy Optimization (PPO) algorithm. We evaluate this approach by comparing its performance to the original MLP-based PPO using the DeepMind Control Proprio robotics benchmark. Our results indicate that the KAN-based reinforcement learning algorithm can achieve comparable performance to its MLP-based counterpart, often with fewer parameters. These findings suggest that KANs may offer a more efficient option for reinforcement learning models. Our implementations can be found in the following link: https://***/victorkich/Kolmogorov-PPO.

关键词： Deep Reinforcement Learning robotics Kolmogorov-Arnold Networks

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An Overview of methods of Industrial Anomaly Detection 7

An Overview of Methods of Industrial Anomaly Detection

引用

7th international conference on robotics, Control and automation Engineering, RCAE 2024

作者： Yang, Siqi Liu, Shu Shang, Penghui Wang, Hui Zhiyuan Research Institute Hangzhou City China

ISBN: (纸本)9798350355642

Industrial anomaly detection plays a crucial role in modern manufacturing and production processes. In order to help researchers and professionals in the manufacturing industry understand the developing research results in this field, this paper begins with an introduction of common methods of industrial anomaly detection such as neural networks, GAN, transformer with discussing the limitations in this domain. Subsequently, owing to the emergence of the fourth wave of artificial intelligence, Advanced anomaly detection techniques based on Large Language models (LLMs) and variants are explored, including AnomalyGPT, VisionGPT and LLMAD which have influenced a lot as the cutting-edge technologies. Additionally, based on the current developing trends of the LLMs-based method, this paper analyzes challenges and deficiencies existing in practice which have the potential to lead the way in future research. Conclusively, this overview exposes the bottleneck of traditional methods and emphasizes the revolutionary impact LLMs could have on multimodal scenes of anomaly detection while putting insight into the extend of methodical practicability. © 2024 IEEE.

关键词： Smart manufacturing

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An equivalent two section method for calculating the workspace of multi-segment continuum robots

An equivalent two section method for calculating the workspa...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Fan, Yeman Liu, Dikai Univ Technol Sydney Sydney NSW 2007 Australia

ISBN: (纸本)9798350323658

Obtaining the shape and size of a robot's workspace is essential for both its design and control. However, determining the accurate workspace of a multi-segment continuum robot by graphic or analytical methods is a challenging task due to its inherent flexibility and complex structure. Existing numerical methods have limitations when applied to a continuum robot. This paper presents an Equivalent Two Section (ETS) method for calculating the workspace of multi-segment continuum robots. This method is based on the forward kinematics and a piecewise constant curvature (PCC) model to determine the boundaries of the workspace. In order to verify the proposed method, simulation experiments are conducted using six different maximum bending angles and seven different number of segments. Results of the ETS method are compared to the true workspaces of these configurations estimated by an exhaustive approach. The results show that the proposed ETS method is both efficient and accurate, and has small estimation errors. Discussions on the advantages and limitations of the proposed ETS method are also presented.

关键词： Numerical methods

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MATERobot: Material Recognition in Wearable robotics for People with Visual Impairments

MATERobot: Material Recognition in Wearable Robotics for Peo...

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IEEE international conference on robotics and automation (ICRA)

作者： Zheng, Junwei Zhang, Jiaming Yang, Kailun Peng, Kunyu Stiefelhagen, Rainer Karlsruhe Inst Technol Inst Robot & Anthropomat D-76131 Karlsruhe Germany Univ Oxford Dept Engn Sci Oxford OX1 3PJ England Hunan Univ Sch Robot Changsha 410082 Peoples R China Hunan Univ Natl Engn Res Ctr Robot Visual Percept & Control Changsha 410082 Peoples R China

ISBN: (纸本)9798350384581;9798350384574

People with Visual Impairments (PVI) typically recognize objects through haptic perception. Knowing objects and materials before touching is desired by the target users but under-explored in the field of human-centered robotics. To fill this gap, in this work, a wearable vision-based robotic system, MATERobot, is established for PVI to recognize materials and object categories beforehand. To address the computational constraints of mobile platforms, we propose a lightweight yet accurate model MATEViT to perform pixel-wise semantic segmentation, simultaneously recognizing both objects and materials. Our methods achieve respective 40.2% and 51.1% of mIoU on COCOStuff-10K and DMS datasets, surpassing the previous method with +5.7% and +7.0% gains. Moreover, on the field test with participants, our wearable system reaches a score of 28 in the NASA-Task Load Index, indicating low cognitive demands and ease of use. Our MATERobot demonstrates the feasibility of recognizing material property through visual cues and offers a promising step towards improving the functionality of wearable robots for PVI. The source code has been made publicly available at MATERobot.

关键词： Semantic Segmentation

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