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

作者： Lin Chen Yaonan Wang Zhiqiang Miao Mingtao Feng Yuanzhe Wang Yang Mo Zhen Zhou Hesheng Wang Danwei Wang School of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center for Robot Visual Perception and Control Technology Changsha China School of Artificial Intelligence Xidian University Xian China School of Electrical and Electrical Engineering Nanyang Technological University Nanyang Avenue Singapore Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Navigating robots through dynamic multi-robot environments, avoiding collisions with both other robots and obstacles, has emerged as a central challenge in robotics. The existing approaches fall short in allowing the policy network to effectively capture spatial-temporal reciprocal collision avoidance in multi-robot environments, comprising both static and dynamic obstacles, resulting in inadequate safety and efficiency in directing robot movement. In this study, we introduce a novel policy neural network called Spatial-Temporal RetNet (STR), designed to encode reciprocal collision avoidance states between robots in spatial and temporal dimensions. The goal is to improve the safety and efficacy of the policy neural network in directing robots to complete assigned tasks. The spatial state encoder module is built upon a parallel RetNet structure, which strengthens the neural network's capacity in extracting reciprocal collision avoidance states between robots in spatial dimensions. This module addresses the limitations of position encoding in transformer-based multi-robot navigation policy neural networks. We design a temporal state encoder utilizing a recurrent RetNet structure. This innovation bolsters the multi-robot navigation policy neural network's capability to capture features in the temporal dimension of multi-robot movements. It addresses the limitations of transformer-based multi-robot navigation policy neural networks, particularly in recurrently inferring information across time dimensions. Simulation experiments were conducted to showcase the superior safety and effectiveness of our proposed method compared to previous state-of-the-art approaches in guiding robots to accomplish tasks.

关键词： Training Technological innovation Navigation Neural networks Transformers Feature extraction Encoding Safety Collision avoidance Robots

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GDN: A stacking network used for skin cancer diagnosis 13

GDN: A stacking network used for skin cancer diagnosis

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13th International Conference on Signal Processing Systems, ICSPS 2021

作者： Wei, Jingmin Shen, Haoyang Wang, Ziyi Zhang, Ziqian School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China Biosystem Engineering Zhejiang University Zhejiang Hangzhou310000 China Computer Science Department New York University New York10012 United States School of Remote Sensing and Information Engineering Wuhan University Hubei Wuhan430079 China

ISBN: (数字)9781510653184

ISBN: (纸本)9781510653177

Skin cancer, the primary type of cancer that can be identified by visual recognition, requires an automatic identification system that can accurately classify different types of lesions. This paper presents GoogLe-Dense Network (GDN), which is an image-classification model to identify two types of skin cancer, Basal Cell Carcinoma, and Melanoma. GDN uses stacking of different networks to enhance the model performance. Specifically, GDN consists of two sequential levels in its structure. The first level performs basic classification tasks accomplished by GoogLeNet and DenseNet, which are trained in parallel to enhance efficiency. To avoid low accuracy and long training time, the second level takes the output of the GoogLeNet and DenseNet as the input for a logistic regression model. We compare our method with four baseline networks including ResNet, VGGNet, DenseNet, and GoogLeNet on the dataset, in which GoogLeNet and DenseNet significantly outperform ResNet and VGGNet. In the second level, different stacking methods such as perceptron, logistic regression, SVM, decision trees and K-neighbor are studied in which Logistic Regression shows the best prediction result among all. The results proves that GDN, compared to a single network structure, has higher accuracy in optimizing skin cancer detection. © COPYRIGHT SPIE.

关键词： Decision trees

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The Emerging Intelligent Vehicles and Intelligent Vehicle Carriers Collaborative Systems

The Emerging Intelligent Vehicles and Intelligent Vehicle Ca...

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IEEE Symposium on Intelligent Vehicle

作者： Zhipeng Shen Chao Huang Hailong Huang Yutong Wang Fei-Yue Wang Abbas Jamalipour Duc Truong Pham Ljubo Vlacic Andrey V. Savkin Department of Aeronautical and Aviation Engineering the Hong Kong Polytechnic University Hong Kong Department of Industrial and Systems Engineering the Hong Kong Polytechnic University Hong Kong State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Engineering Macau University of Science and Technology Macau China School of Electrical and Information Engineering University of Sydney Sydney Australia Department of Mechanical Engineering University of Birmingham Birmingham UK Institute of Intelligent & Integrated Systems and the School of Engineering and Built Environment Griffith University Brisbane Australia School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

In this paper, we propose the innovative use of Intelligent Vehicle Carriers (IVCs) as a key solution to address the energy constraints of small-scale unmanned Intelligent Vehicles (IVs). IVCs function as both transporters and charging stations, significantly boosting the operational range and efficiency of IVs. Our research delves into the IV-IVC collaborative framework, highlighting the existing challenges, exploring potential solutions, and examining a range of applications. This study offers a visionary approach to revolutionizing intelligent transportation systems by leveraging the synergistic relationship between IVs and IVCs.

关键词： Intelligent vehicles Collaboration Charging stations Boosting

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Incremental Joint Learning of Depth, Pose and Implicit Scene Representation on Monocular Camera in Large-scale Scenes

arXiv

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arXiv 2024年

作者： Deng, Tianchen Wang, Nailin Wang, Chongdi Yuan, Shenghai Wang, Jingchuan Wang, Danwei Chen, Weidong The Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The School of Electrical and Electronic Engineering Nanyang Technological University Singapore

Dense scene reconstruction for photo-realistic view synthesis has various applications, such as VR/AR, autonomous vehicles. However, most existing methods have difficulties in large-scale scenes due to three core challenges: (a) inaccurate depth input. Accurate depth input is impossible to get in real-world large-scale scenes. (b) inaccurate pose estimation. Existing methods are not robust enough with the growth of cumulative errors in large scenes and long sequence. (c) insufficient scene representation capability. A single global radiance field lacks the capacity to effectively scale to large-scale scenes. To this end, we propose an incremental joint learning framework, which can achieve accurate depth, pose estimation, and large-scale scene reconstruction. A vision transformer-based network is adopted as the backbone to enhance performance in scale information estimation. For pose estimation, a feature-metric bundle adjustment (FBA) method is designed for accurate and robust camera tracking in large-scale scenes. In terms of implicit scene representation, we propose an incremental scene representation method to construct the entire large-scale scene as multiple local radiance fields to enhance the scalability of 3D scene representation. Extended experiments have been conducted to demonstrate the effectiveness and accuracy of our method in depth estimation, pose estimation, and large-scale scene reconstruction. The code will be open-sourced upon paper acceptance. © 2024, CC BY-NC-SA.

关键词： Depth perception

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Development of a System Based on Image Analysis and Piezoresistive Sensors for the Measurement of Step Length and Width in Walks of Hundreds of Meters

SSRN

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SSRN 2024年

作者： Fernández, Daniel Alberto Rengifo, Carlos Felipe Rodriguez, Pablo Eduardo Caicedo Guevara, Nelson Eduardo Sierra, Wilson Alexander Rodríguez, Luis Eduardo Research Group of Automation Universidad del Cauca Colombia Department of Electronic Intrumentation and Control Universidad del Cauca Colombia Faculty of Engineering and Natural Sciences Corporación Universitaria Autónoma del Cauca Colombia Faculty of Biomedical Engineering Escuela Colombiana de Ingeniería Julio Garavito Colombia

This paper presents a system for estimating step length and step width during human gait. Our approach uses the analysis of depth images captured in synchronisation with piezo-resistive sensors located in two shoe insoles. The system consists of an Intel RealSense D435i RGB-D camera, two insoles equipped with piezoresistive sensors in the heel area, two non-reflective high-visibility markers and a four-wheeled mobile platform. Operation and synchronisation are managed by modules including ESP32 devices and a laptop computer. The proposed system was validated using a comercial motion capture system consisting of 8 cameras. Running tests were performed both on a treadmill and in a natural environment, within the active coverage area of the motion capture system. The results obtained showed that there is a highly significant relationship between the measurements of the proposed system and the measurements of the motion capture system. In the treadmill tests, it was observed that the estimated coefficient of determination R2 was 87.42%±4.55% for step lengths and 83.84%±1.13% for step widths. Likewise, the tests for natural gait had an R2 of 76.83%±3.00% for the step lengths and 81.25% ± 1.63% for the step widths. © 2024, The Authors. All rights reserved.

关键词： Cameras

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Image Classification with Small Datasets: Overview and Benchmark

arXiv

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arXiv 2022年

作者： Brigato, Lorenzo Barz, Björn Iocchi, Luca Denzler, Joachim Department of Computer Control and Management Engineering Sapienza University of Rome Italy Department of Mathematics and Computer Science Friedrich Schiller University Jena Germany

Image classification with small datasets has been an active research area in the recent past. However, as research in this scope is still in its infancy, two key ingredients are missing for ensuring reliable and truthful progress: a systematic and extensive overview of the state of the art, and a common benchmark to allow for objective comparisons between published methods. This article addresses both issues. First, we systematically organize and connect past studies to consolidate a community that is currently fragmented and scattered. Second, we propose a common benchmark that allows for an objective comparison of approaches. It consists of five datasets spanning various domains (e.g., natural images, medical imagery, satellite data) and data types (RGB, grayscale, multispectral). We use this benchmark to re-evaluate the standard cross-entropy baseline and ten existing methods published between 2017 and 2021 at renowned venues. Surprisingly, we find that thorough hyper-parameter tuning on held-out validation data results in a highly competitive baseline and highlights a stunted growth of performance over the years. Indeed, only a single specialized method dating back to 2019 clearly wins our benchmark and outperforms the baseline classifier. © 2022, CC BY.

关键词： Image classification

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Human-Aware Navigation in Crowded Environments Using Adaptive Proxemic Area and Group Detection

Human-Aware Navigation in Crowded Environments Using Adaptiv...

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

作者： Carlos Medina-Sánchez Simon Janzon Matteo Zella Jesús Capitán Pedro J. Marrón Faculty of Business Administration and Economics Institute for Computer Science and Business Information Systems (ICB) University of Duisburg-Essen Essen Germany Faculty of Electrical Engineering and Computer Science University of Applied Sciences Niederrhein Krefeld Germany Department of System Engineering and Automation University of Seville Seville Spain

Navigation is an essential task for social robots. However, certain rules must be followed to allow them to move without causing distraction or discomfort to people. Considering that the context surrounding robots and persons affects the expected behavior, this work defines a social area around a person that adapts to the real situation. In addition, the social context of a person is extended to identify groups of people, which the robot should take into account while navigating. With this understanding of the surrounding of the robot together with the ability to predict the trajectory of individuals as well as groups, the proposed solution not only effectively addresses collision avoidance while promoting socially acceptable behavior but also outperforms the majority of recent works in terms of accuracy. Furthermore, a dedicated policy is introduced to react to social navigation conflicts. The evaluation performed in a simulated environment shows that the computation of our proposed solution is at least 8 times faster than the best state-of-the-art approach while preserving comparable social conduct. Also, the results of realistic experiments performed using Gazebo and a real robot are reported.

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Proximal ADMM for Nonconvex and Nonsmooth Optimization

arXiv

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arXiv 2022年

作者： Yang, Yu Jia, Qing-Shan Xu, Zhanbo Guan, Xiaohong Spanos, Costas J. School of Automation Science and Engineering Xi’an Jiaotong University Shaanxi China CFINS Department of Automation BNRist Tsinghua University Beijing China Electrical Engineering and Computer Sciences University of California Berkeley United States

By enabling the nodes or agents to solve small-sized subproblems to achieve coordination, distributed algorithms are favored by many networked systems for efficient and scalable computation. While for convex problems, substantial distributed algorithms are available, the results for the more broad nonconvex counterparts are extremely lacking. This paper develops a distributed algorithm for a class of nonconvex and nonsmooth problems featured by i) a nonconvex objective formed by both separate and composite components regarding the decision variables of interconnected agents, ii) local bounded convex constraints, and iii) coupled linear constraints. This problem is directly originated from smart buildings and is also broad in other domains. To provide a distributed algorithm with convergence guarantee, we revise the powerful alternating direction method of multiplier (ADMM) method and proposed a proximal ADMM. Specifically, noting that the main difficulty to establish the convergence for the nonconvex and nonsmooth optimization with ADMM is to assume the boundness of dual updates, we propose to update the dual variables in a discounted manner. This leads to the establishment of a so-called sufficiently decreasing and lower bounded Lyapunov function, which is critical to establish the convergence. We prove that the method converges to some approximate stationary points. We besides showcase the efficacy and performance of the method by a numerical example and the concrete application to multi-zone heating, ventilation, and air-conditioning (HVAC) control in smart buildings. © 2022, CC BY-SA.

关键词： Lyapunov functions

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Thick 3D-Printed Hierarchical Li4Ti5O12@MOF anode and Grid-Lined micropores for High-Performance Lithium-Ion batteries

引用

Journal of Colloid and Interface Science 2025年 696卷 137883页

作者： Mwizerwa, Jean Pierre Li, Jie Nsengiyumva, Walter Li, Chen Xu, Kun Rasaki, Sefiu Abolaji Liu, Changyong School of Materials Science and Engineering Fujian University of Technology Fuzhou350118 China Additive Manufacturing Institute College of Mechatronics & Control Engineering Shenzhen University Shenzhen518060 China Fujian Provincial Key Laboratory of Terahertz Functional Devices and Intelligent Sensing School of Mechanical Engineering and Automation Fuzhou University Fuzhou China The Institute of Precision Instrument and Intelligent Measurement & Control Fuzhou University Fuzhou China Institute: Department of Chemical and Petroleum Engineering University of Calgary AlbertaT2N 1N4 Canada

Li4Ti5O12 (LTO) anode is a promising candidate for high-energy–density lithium-ion batteries (LIBs), but achieving high mass loading, a porous structure, and efficient ion transport remains a challenge. Herein, we present a high-mass-loading, hierarchically porous LTO anode with a conductive network and grid-lined micropores, embedded with a metal–organic framework (MOF) as both an electrode additive and surface protective layer. This novel structure is fabricated using extrusion-based three-dimensional (3D) printing technology. The self-standing framework provides mechanical stability and high conductivity, enabling a thick (316 μm) 3D-printed LTO@UiO-66-MOF (3D-LTO@U) anode with a mass loading of 11 mg/cm2. It delivers a high-rate capability of 161 mAh/g at 5C, an areal specific capacity of 5.37 mAh/cm2, and 78.9 % areal capacity retention after 150 cycles. Additionally, it achieves a high specific energy density of 382.35 Wh/kg. The UiO-66 MOF provides strong binding affinity, suppressing side reactions and enhancing Li-ion/electron transport within the 3D-printed interconnected channels. This improves active material utilization during charge and discharge. Furthermore, a 3D-printed full cell integrating a grid-lined 3D-LTO@U anode and a 3D-printed LiFePO4 cathode exhibits enhanced electrochemical performance. This work demonstrates an effective strategy for designing thick, high-mass-loading, and porous conductive network anodes for advanced LIBs. © 2025 Elsevier Inc.

关键词： Lithium-ion batteries

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NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and Denoising

arXiv

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arXiv 2024年

作者： Deng, Tianchen Wang, Yanbo Xie, Hongle Wang, Hesheng Wang, Jingchuan Wang, Danwei Chen, Weidong Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

In recent years, there have been significant advancements in 3D reconstruction and dense RGB-D SLAM systems. One notable development is the application of Neural Radiance Fields (NeRF) in these systems, which utilizes implicit neural representation to encode 3D scenes. This extension of NeRF to SLAM has shown promising results. However, the depth images obtained from consumer-grade RGB-D sensors are often sparse and noisy, which poses significant challenges for 3D reconstruction and affects the accuracy of the representation of the scene geometry. Moreover, the original hierarchical feature grid with occupancy value is inaccurate for scene geometry representation. Furthermore, the existing methods select random pixels for camera tracking, which leads to inaccurate localization and is not robust in real-world indoor environments. To this end, we present NeSLAM, an advanced framework that achieves accurate and dense depth estimation, robust camera tracking, and realistic synthesis of novel views. First, a depth completion and denoising network is designed to provide dense geometry prior and guide the neural implicit representation optimization. Second, the occupancy scene representation is replaced with Signed Distance Field (SDF) hierarchical scene representation for high-quality reconstruction and view synthesis. Furthermore, we also propose a NeRF-based self-supervised feature tracking algorithm for robust real-time tracking. Experiments on various indoor datasets demonstrate the effectiveness and accuracy of the system in reconstruction, tracking quality, and novel view synthesis. © 2024, CC BY-NC-SA.

关键词： Geometry

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