Cognitive Robotics最新文献_第5页

Research on YOLOv3 model compression strategy for UAV deployment 无人机部署中YOLOv3模型压缩策略研究

Cognitive Robotics Pub Date : 2023-11-17 DOI: 10.1016/j.cogr.2023.11.001

Fei Xu , Litao Huang , Xiaoyang Gao , Tingting Yu , Leyi Zhang

{"title":"Research on YOLOv3 model compression strategy for UAV deployment","authors":"Fei Xu , Litao Huang , Xiaoyang Gao , Tingting Yu , Leyi Zhang","doi":"10.1016/j.cogr.2023.11.001","DOIUrl":"https://doi.org/10.1016/j.cogr.2023.11.001","url":null,"abstract":"<div><p>UAVs are often limited by limited resources when performing flight tasks, especially the contradiction between storage resources and computing resources when the huge YOLOv3 model is deployed on the edge UAVs. In this paper, we tend to compress YOLOv3 model in different aspects to achieve load availability at the edge. In this paper, deep separable convolution is introduced to reduce the computation of the model. Then, PR regularization term is used as the regularization term of sparse training to better distinguish scaling factors, and then the hybrid pruning combining channel pruning and layer pruning is carried out on the model according to scaling factors, in order to reduce the number of model parameters and the amount of calculation. Finally, since the training data is a 32-bit floating point number, DoReFa-Net quantization method is used to quantify the model, so as to compress the storage capacity of the model. The experimental results show that the compression scheme proposed in this paper can effectively reduce the number of parameters by 97.5 % and the calculation amount by 82.3 %, and can maintain the original detection efficiency of UAVs.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"4 ","pages":"Pages 8-18"},"PeriodicalIF":0.0,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667241323000381/pdfft?md5=c325aa36bb6e1759d99185c61f3a6b9f&pid=1-s2.0-S2667241323000381-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138439137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SRGAN in underwater vision 水下视觉中的SRGAN

Cognitive Robotics Pub Date : 2023-11-03 DOI: 10.1016/j.cogr.2023.08.002

Dingqian Zhao

{"title":"SRGAN in underwater vision","authors":"Dingqian Zhao","doi":"10.1016/j.cogr.2023.08.002","DOIUrl":"10.1016/j.cogr.2023.08.002","url":null,"abstract":"<div><p>In recent years, the rapid industrialization of the world has led to an increasing importance of energy minerals. However, due to the scarcity of mineral resources, opportunities to rely on alternative energy are escalating. As a result, exploration of ocean resources, which exist abundantly in the sea, is being pursued. However, the manual exploration of ocean resources by diving and visually searching is dangerous and impractical. Therefore, it is pertinent to safely advance underwater exploration by having robots perform the work instead. In underwater environments, robots are commonly used as a mainstream exploration tool due to the various hazardous environmental conditions. However, there are several problems with controlling robots in underwater environments, and one of them is poor visibility underwater. Therefore, to improve visibility underwater, efforts are being made to achieve high resolution using super-resolution technology on underwater images. In this paper we first introduce the general model and architecture in GAN. Then we combine the GAN modal and characteristics of the underwater environment, elaborating how ESRGAN can be suitable for such circumstance. For data from ECCV2018 PIRM-SR, ESRGAN outperforms other traditional model like EnhanceNet <span>[1]</span>, EDSR <span>[2]</span>, RCAN <span>[3]</span>, at least 24 % <span>[4]</span>. Such model can be equipped with robotics that highly depends on the resolution of the image, such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs).</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"4 ","pages":"Pages 1-7"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667241323000289/pdfft?md5=327c4a7880ba070fb45e7c349a11ba1e&pid=1-s2.0-S2667241323000289-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135410456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Review Of The Latest Research Technologies Related To 3D Point Cloud 三维点云最新研究技术综述

Cognitive Robotics Pub Date : 2023-09-01 DOI: 10.1016/j.cogr.2023.09.001

Zhang Xin

引用次数: 0

Unmanned aerial vehicles: A review 无人飞行器:综述

Cognitive Robotics Pub Date : 2023-01-01 DOI: 10.1016/j.cogr.2022.12.004

Asif Ali Laghari , Awais Khan Jumani , Rashid Ali Laghari , Haque Nawaz

引用次数: 8

Artificial intelligence based hybridization for economic power dispatch 基于人工智能的混合动力经济调度

Cognitive Robotics Pub Date : 2023-01-01 DOI: 10.1016/j.cogr.2023.07.002

Kothuri Rama Krishna , Rajesh Kumar Samala

引用次数: 0

Lightweight YOLOv5 model based small target detection in power engineering 基于轻量级YOLOv5模型的电力工程小目标检测

Cognitive Robotics Pub Date : 2023-01-01 DOI: 10.1016/j.cogr.2023.03.002

Ping Luo, Xinsheng Zhang, Yongzhong Wan

{"title":"Lightweight YOLOv5 model based small target detection in power engineering","authors":"Ping Luo, Xinsheng Zhang, Yongzhong Wan","doi":"10.1016/j.cogr.2023.03.002","DOIUrl":"https://doi.org/10.1016/j.cogr.2023.03.002","url":null,"abstract":"<div><p>Deep learning architectures have yielded a significant leap in target detection performance. However, the high cost of deep learning impedes real-world applications, especially for UAV and UGV platforms. Moreover, detecting small targets is still of lower accuracy in contrast to the large ones. Aiming to comprehensively handle these two issues, a novel SP-CBAM-YOLOv5 architecture is proposed. The main novelty of our hybrid model lies in the cooperation of the attention mechanism and the typical YOLOv5 architecture, which can largely improve the performance of the small target detection. Moreover, the depth convolution and knowledge distillation are jointly introduced for lightening the model architecture. To evaluate the performance of our proposed SP-CBAM-YOLOv5, we built a novel dataset containing challenging scenes of power engineering. Experimental results on this benchmark demonstrate that our proposed SP-CBAM-YOLOv5 achieves a competitive performance in contrast to the other YOLO architectures. Besides, our lightweight YOLOv5 has more than 70% decrease of parameters. Moreover, the ablation study is conducted to demonstrate the compact architecture of SP-CBAM-YOLOv5.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"3 ","pages":"Pages 45-53"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49732949","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

A computing offloading strategy for UAV based on improved bat algorithm 基于改进蝙蝠算法的无人机计算卸载策略

Cognitive Robotics Pub Date : 2023-01-01 DOI: 10.1016/j.cogr.2023.07.005

Fei Xu , Shun Zi , Jianguo Wang , Jiajun Ma

{"title":"A computing offloading strategy for UAV based on improved bat algorithm","authors":"Fei Xu , Shun Zi , Jianguo Wang , Jiajun Ma","doi":"10.1016/j.cogr.2023.07.005","DOIUrl":"https://doi.org/10.1016/j.cogr.2023.07.005","url":null,"abstract":"<div><p>In the process of multi-UAVs cooperative reconnaissance operations, due to the limited battery capacity and computing resources of the unmanned aerial vehicle (UAV), processing tasks can not only lead to excessive delay, but also increase the energy consumption of the UAV, which reduces the endurance time of the UAV. Therefore, we have proposed a mobile edge computing (MEC) system architecture composed of single unmanned helicopter (UH) and multiple reconnaissance UAVs. Among them, the UH as a MEC server to provide computing services for reconnaissance UAVs. By solving the computing offloading strategy problem of multi-UAVs, the objective is to minimize the weighted sum of energy consumption and delay for the multi-UAVs' task execution. In solving the problem, previous heuristic algorithms such as the Particle Swarm Optimization (PSO) are often used as basic algorithms for research, but they tend to converge early, fall into local optimum easily, and have low solution accuracy, making it difficult to obtain the optimal offloading strategy. Therefore, this paper proposes an improved bat algorithm (IBA) with fast convergence ability and global search ability. Through the simulation experiments and comparative analysis of PSO, BA, IPSO and IBA, it is proved that the IBA is more accurate, stable, and efficient in solving this problem based on the system architecture proposed in this paper, and effectively reduces the weighted sum of energy consumption and delay for the multi-UAVs' task execution.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"3 ","pages":"Pages 265-283"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49732991","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

Fault diagnosis using transfer learning with dynamic multiscale representation 基于动态多尺度表示的迁移学习故障诊断

Cognitive Robotics Pub Date : 2023-01-01 DOI: 10.1016/j.cogr.2023.07.006

Xinjie Sun , Shubiao Wang , Jiangping Jing , Zhangliang Shen , Liudong Zhang

{"title":"Fault diagnosis using transfer learning with dynamic multiscale representation","authors":"Xinjie Sun , Shubiao Wang , Jiangping Jing , Zhangliang Shen , Liudong Zhang","doi":"10.1016/j.cogr.2023.07.006","DOIUrl":"https://doi.org/10.1016/j.cogr.2023.07.006","url":null,"abstract":"<div><p>A critical problem for fault diagnosis is caused by the feature shift under different working conditions, which significantly degenerates the diagnosis accuracy in practice. Aiming to solve this problem, this paper proposes a novel Transfser Learning (TL) framework with Dynamic Multiscale Representation (DMR) for fault diagnosis. This model draws the inspiration from the shared learning and transfer learning, processing information captured and exploited by multiscale signal factors. In particular, a novel multi-path merging network is proposed to generate dynamic weights for fusing multiscale factors. To drive this generation, and to control the extent of the shared fusion, the Multi-gate Mixture-of-Experts (MMoE) is introduced to model the tradeoff between scale-specific representation and inter-scale correlation. A transfer learning backend is also introduced to align cross-domain features, which enables proposed method to diagnose faults across distinct working conditions. Experiments evaluate the fault-diagnosis performance. Our primary, ablation and interpretation evaluations comprehensively indicate the robustness and flexibility of the proposed method to diverse fault diagnosis applications. Especially, the proposed method achieves 4.71% and 3.86% improved to the second best one (MSSLN) on the PHM2009 and MCP datasets, respectively.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"3 ","pages":"Pages 257-264"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49710708","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

Mental simulation of actions for learning optimal poses 学习最佳姿势的心理模拟动作

Cognitive Robotics Pub Date : 2023-01-01 DOI: 10.1016/j.cogr.2023.07.003

Pietro Morasso

{"title":"Mental simulation of actions for learning optimal poses","authors":"Pietro Morasso","doi":"10.1016/j.cogr.2023.07.003","DOIUrl":"https://doi.org/10.1016/j.cogr.2023.07.003","url":null,"abstract":"<div><p>Mental simulation of actions is a powerful tool for allowing cognitive agents to develop <em>Prospection Capabilities</em> that are crucial for learning and memorizing key aspects in challenging actions. In particular, this study focuses on the initial or final posture of actions and provides a computational tool that allows an agent to evaluate their feasibility and appropriateness. Such tool is a kinematic network, equivalent to an internal body schema, that allows a cognitive agent to generate simulation-states that reach the goal with a comfortable final posture, by exploiting the redundancy of the kinematic network. This is obtained by activating and integrating in the network dynamics three types of virtual force fields: 1) Focal force field applied to the end-effector, related to the goal of the action; 2) Range of Motion force fields, applied separately and independently to each degree of freedom in order to preserve the natural joint limits; 3) Postural force field, applied to the pelvis area, for maintaining the projection of the center of mass of the body model inside the support base. The efficacy of this approach is demonstrated in relation to a simple task: reaching a heavy load in order to lift it and then shifting it forward before dropping it on a table. The mental simulation model attempts to provide a kinematic template compatible with the overall plan and the postural/articular constraints, as a function of the initial position of the body relative to the load. The simulation may fail and this indicates that the chosen initial posture is inappropriate for the task. Successful simulations can also be evaluated in terms of precision and effort by monitoring the peak torque required of each joint actuator. Optimal or at least sub-optimal solutions can be memorized in episodic memory, thus accruing the know-how of the agent.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"3 ","pages":"Pages 185-200"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49761359","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

Artificial intelligence, machine learning and deep learning in advanced robotics, a review 先进机器人中的人工智能、机器学习和深度学习综述

Cognitive Robotics Pub Date : 2023-01-01 DOI: 10.1016/j.cogr.2023.04.001

Mohsen Soori , Behrooz Arezoo , Roza Dastres

{"title":"Artificial intelligence, machine learning and deep learning in advanced robotics, a review","authors":"Mohsen Soori , Behrooz Arezoo , Roza Dastres","doi":"10.1016/j.cogr.2023.04.001","DOIUrl":"https://doi.org/10.1016/j.cogr.2023.04.001","url":null,"abstract":"<div><p>Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have revolutionized the field of advanced robotics in recent years. AI, ML, and DL are transforming the field of advanced robotics, making robots more intelligent, efficient, and adaptable to complex tasks and environments. Some of the applications of AI, ML, and DL in advanced robotics include autonomous navigation, object recognition and manipulation, natural language processing, and predictive maintenance. These technologies are also being used in the development of collaborative robots (cobots) that can work alongside humans and adapt to changing environments and tasks. The AI, ML, and DL can be used in advanced transportation systems in order to provide safety, efficiency, and convenience to the passengers and transportation companies . Also, the AI, ML, and DL are playing a critical role in the advancement of manufacturing assembly robots, enabling them to work more efficiently, safely, and intelligently. Furthermore, they have a wide range of applications in aviation management, helping airlines to improve efficiency, reduce costs, and improve customer satisfaction. Moreover, the AI, ML, and DL can help taxi companies in order to provide better, more efficient, and safer services to customers. The research presents an overview of current developments in AI, ML, and DL in advanced robotics systems and discusses various applications of the systems in robot modification. Further research works regarding the applications of AI, ML, and DL in advanced robotics systems are also suggested in order to fill the gaps between the existing studies and published papers. By reviewing the applications of AI, ML, and DL in advanced robotics systems, it is possible to investigate and modify the performances of advanced robots in various applications in order to enhance productivity in advanced robotic industries.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"3 ","pages":"Pages 54-70"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49732989","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: 46