Proceedings of the 7th International Conference on Robotics and Artificial Intelligence最新文献_第2页

Automatic Defect Recognition Method of Aluminium Profile Surface Defects 铝型材表面缺陷的自动识别方法

Proceedings of the 7th International Conference on Robotics and Artificial Intelligence Pub Date : 2021-11-19 DOI: 10.1145/3505688.3505692

Lei Yang, Ge Gao, Manman Wu, Jianyong Li

{"title":"Automatic Defect Recognition Method of Aluminium Profile Surface Defects","authors":"Lei Yang, Ge Gao, Manman Wu, Jianyong Li","doi":"10.1145/3505688.3505692","DOIUrl":"https://doi.org/10.1145/3505688.3505692","url":null,"abstract":"Automatic defect detection has important implications to intelligent manufacturing which could be used for the precise quality control of different products. However, the diverse aluminium profile surface defects present the characteristics of micro defects and different sizes. Conventional handcrafted-based methods and machine learning-based methods have limited feature expression ability which cause relatively poor detection performance. Recently, with the stronger feature extraction ability, deep learning has got wide applications on defect detection and recognition. Due to the loss information caused by pooling operations, it still exists a certain drawbacks on multi-scale object detection. To address this issue, with the residual neural network (ResNet), a new deep defect recognition network is proposed in this paper for aluminium profile surface defects to construct an end-to-end defect detection scheme. An attention fusion model is proposed to improve the detection precision on multi-scale defects. Experiments show that the proposed defect detection method shows a better detection performance compared with other advanced detection models.","PeriodicalId":375528,"journal":{"name":"Proceedings of the 7th International Conference on Robotics and Artificial Intelligence","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128311949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Design and Obstacle-surmounting Analysis of a Novel 6× 6 Wheel-tracked Unmanned Ground Platform 新型6× 6轮带无人地面平台设计与越障分析

Proceedings of the 7th International Conference on Robotics and Artificial Intelligence Pub Date : 2021-11-19 DOI: 10.1145/3505688.3505696

Jian Zhang, Xiaodi He

{"title":"Design and Obstacle-surmounting Analysis of a Novel 6× 6 Wheel-tracked Unmanned Ground Platform","authors":"Jian Zhang, Xiaodi He","doi":"10.1145/3505688.3505696","DOIUrl":"https://doi.org/10.1145/3505688.3505696","url":null,"abstract":"In view of the complex topographic structure in the off-road and the insufficiency in trafficability of traditional multi-wheeled vehicles and tracked vehicles, a novel 6×6 wheel-tracked unmanned ground platform (6×6 WTUG platform) was purposed and designed in this paper, its posture can be rotated and adjusted through six independently driven wheel-track mechanisms to adapt to the complex off-road environment. Obstacle-surmounting performance is the fundamental factor in evaluation of platform trafficability. In order to evaluate the obstacle-surmounting performance of 6×6 WTUG platform in typical vertical obstacles, a posture planning model and a kinematic model of the obstacle-surmounting process were established. In order to verify the theoretical analysis, simulation of obstacle-surmounting was carried out in RecurDyn. Studies have shown that 6×6 WTUG platform can overcome obstacles higher than its own height, and the stability and feasibility of 6×6 WTUG platform posture planning were verified by analyzing the change in pitch angle during the obstacle-surmounting process. The studies on 6×6 WTUG platform has potential applications for off-road transportation, disaster relief and the military.","PeriodicalId":375528,"journal":{"name":"Proceedings of the 7th International Conference on Robotics and Artificial Intelligence","volume":"483 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116323222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

An Intelligent Fault Location Algorithm of High Voltage Lines Using Cascading Deep Network 基于级联深度网络的高压线路故障智能定位算法

Proceedings of the 7th International Conference on Robotics and Artificial Intelligence Pub Date : 2021-11-19 DOI: 10.1145/3505688.3505705

Lei Yang, Yuge Gu, Manman Wu, Yanhong Liu

{"title":"An Intelligent Fault Location Algorithm of High Voltage Lines Using Cascading Deep Network","authors":"Lei Yang, Yuge Gu, Manman Wu, Yanhong Liu","doi":"10.1145/3505688.3505705","DOIUrl":"https://doi.org/10.1145/3505688.3505705","url":null,"abstract":"Due to ever-increasing power equipments and the distances of power transmission lines, insulator inspection presents a valuable but challenging issue. As a common insulator defect, missing-cap defects affect the structural strength of power insulators and cause irreparable harm to power supply security. Therefore, insulator defect detection is a basic and critical task for power line inspection. Most detection methods are mainly based on machine learning algorithms. Shallow learning methods rely on handcrafted image features and are always aimed at specific scenarios or prior knowledge. The unbalanced data sets of insulators affect the detection performance of deep learning algorithms. To address the above problems regarding insulator defect detection, a novel detection algorithm based on a cascading deep architecture is proposed for unmanned aerial vehicle (UAV) inspection. Combined with the strong detection performance of deep architecture, an insulator location algorithm based on the improved YOLOV3 model is proposed to remove complex backgrounds such as ”region of interest (ROI) extraction”. On this basis, a novel semantic segmentation algorithm is proposed to realize defect segmentation for small missing-cap defects. Experiments show that the proposed algorithm can satisfactorily meet the precision and robustness requirements of power line inspection compared with other related detection models.","PeriodicalId":375528,"journal":{"name":"Proceedings of the 7th International Conference on Robotics and Artificial Intelligence","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134350353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Event-Triggered Intelligent Critic Design for Constrained Nonaffine Nonzero-Sum Games 约束非仿射非零和博弈的事件触发智能批评设计

Proceedings of the 7th International Conference on Robotics and Artificial Intelligence Pub Date : 2021-11-19 DOI: 10.1145/3505688.3505701

Lingzhi Hu, Ding Wang, Ning Gao, Mingming Zhao

引用次数: 0

Fuzzy Controlled Adaptive Follow the Gap Obstacle Avoidance Algorithm 模糊控制自适应跟随间隙避障算法

Proceedings of the 7th International Conference on Robotics and Artificial Intelligence Pub Date : 2021-11-19 DOI: 10.1145/3505688.3505704

Bilal Yucel, Abdurrahman Yilmaz, Osman Ervan, H. Temeltas

引用次数: 0

A Deep Learning Approach for Multi-copter Detection using mm-Wave Radar Sensors: Application of Deep Learning for Multi-copter detection using radar micro-Doppler signatures 毫米波雷达传感器多机探测的深度学习方法:深度学习在雷达微多普勒特征多机探测中的应用

Proceedings of the 7th International Conference on Robotics and Artificial Intelligence Pub Date : 2021-11-19 DOI: 10.1145/3505688.3505706

George Samuell Aiad Saleip Nasr Alla, Paulina Maurer, A. Hassan, Michael Frangenberg, W. Granig

{"title":"A Deep Learning Approach for Multi-copter Detection using mm-Wave Radar Sensors: Application of Deep Learning for Multi-copter detection using radar micro-Doppler signatures","authors":"George Samuell Aiad Saleip Nasr Alla, Paulina Maurer, A. Hassan, Michael Frangenberg, W. Granig","doi":"10.1145/3505688.3505706","DOIUrl":"https://doi.org/10.1145/3505688.3505706","url":null,"abstract":"The increasing number of affordable drones and their misuse calls for the need of new multi-copter detection technologies. Such new technologies shall enable the detection of drones flying in restricted regions, for instance in military zones. Therefore, multiple detection techniques have been developed, mainly using camera sensors and digital imaging. Since cameras however suffer the problems of inapplicability in bad weather and low light conditions, radar systems have been recently developed for multi-copter detection using various conventional detection algorithms. Radar systems enable collecting rotor-specific data. Due to the fact that all rotor-objects show similar characteristics in the Radar Doppler spectrogram, i.e. the so called Micro-Doppler signatures, Machine Learning classification techniques on radar collected data enable reaching better and more reliable detection results when compared to the conventional algorithms. This paper introduces a Deep-Learning-based technique that can be used to detect multi-copters. The main idea is making use of the micro-Doppler properties of multi-copter and applying Deep Learning approaches for the automation of the classification. Due to their rotating components, rotor-wing aircrafts induce distinct Radar micro-Doppler signatures. In this work, experimental measurements of radar micro-Doppler signatures for both cases: rotating wing-copters and various other non-rotating objects are detected using continuous wave (CW) Radar. Radar micro-Doppler signature images are collected, and then further processed and used for detecting the various multi-rotor objects. Detection is performed using a trained convolutional neural network (CNN).","PeriodicalId":375528,"journal":{"name":"Proceedings of the 7th International Conference on Robotics and Artificial Intelligence","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126043145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Kernel-Based Autoencoders for Large-Scale Representation Learning 基于核的大规模表示学习自编码器

Proceedings of the 7th International Conference on Robotics and Artificial Intelligence Pub Date : 2021-11-19 DOI: 10.1145/3505688.3505707

Jinzhou Bao, Bo Zhao, Ping Guo

引用次数: 0

An Improved Detection Algorithm of Moving Targets Based on Envelope Alignment in Sea Environments 海洋环境下基于包络线对准的运动目标检测改进算法

Proceedings of the 7th International Conference on Robotics and Artificial Intelligence Pub Date : 2021-11-19 DOI: 10.1145/3505688.3505695

Zhefeng Wu, P. Sun, Yanchao Lin, Laitian Cao

引用次数: 0

Prototype of Portable Robusta Coffee Harvesting Machine 便携式罗布斯塔咖啡采收机的原型

Proceedings of the 7th International Conference on Robotics and Artificial Intelligence Pub Date : 2021-11-19 DOI: 10.1145/3505688.3505709

Nuttapong Ruttanadech, T. Chungcharoen, S. Sansiribhan, Ronnachart Munsin, R. Thuwapanichayanan, Arkom Palamanit, W. Limmun

{"title":"Prototype of Portable Robusta Coffee Harvesting Machine","authors":"Nuttapong Ruttanadech, T. Chungcharoen, S. Sansiribhan, Ronnachart Munsin, R. Thuwapanichayanan, Arkom Palamanit, W. Limmun","doi":"10.1145/3505688.3505709","DOIUrl":"https://doi.org/10.1145/3505688.3505709","url":null,"abstract":"The limitations of machine size and cost are main problems for coffee harvesting using machine picking. Therefore, the prototype of Robusta coffee harvesting machine with portable and low cost was designed and created in this research. Moreover, the effects of bristle hardness, bristle size and brush rotation speed on the coffee harvesting efficiency were investigated. The experimental results showed that the prototype of portable Robusta coffee harvesting machine was effective to harvest the Robusta coffee. It provided the highest coffee harvesting efficiency of 75.24% by using the bristle hardness of 75 shore A, bristle size of 8 mm and brush rotation speed of 700 rpm. The bristle hardness, bristle size and brush rotation speed were significantly affected the coffee harvesting efficiency. The coffee harvesting efficiency was increased when increasing the bristle hardness from 60 shore A to 75 shore A and the brush rotation speed from 600 rpm to 700 rpm. On the other hand, the increase of bristle size from 8 mm to 10 mm provided the lower coffee harvesting efficiency.","PeriodicalId":375528,"journal":{"name":"Proceedings of the 7th International Conference on Robotics and Artificial Intelligence","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124046595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Analysis of a Coupled Aquaculture Ship and Cage Mooring System in Deep Sea 深海养殖船-笼系泊耦合系统动力学分析

Proceedings of the 7th International Conference on Robotics and Artificial Intelligence Pub Date : 2021-11-19 DOI: 10.1145/3505688.3505710

Pengchao Wang, Rui Tan, Yan He

引用次数: 0