Cognitive Robotics最新文献

{"title":"RDSM: Underwater multi-AUV relay deployment and selection mechanism in 3D space","authors":"Yafei Liu ,&nbsp;Na Liu ,&nbsp;Hao Li ,&nbsp;Yi Jiang ,&nbsp;Junwu zhu","doi":"10.1016/j.cogr.2024.11.001","DOIUrl":"10.1016/j.cogr.2024.11.001","url":null,"abstract":"<div><div>Underwater Wireless Sensor Networks (UWSNs) are widely used in naval military field and marine resource exploration. However, challenges such as resource inefficiency and unbalanced energy consumption severely hinder their practical applications. In this paper, we establish a model of underwater multi-hop wireless sensor network with multiple AUVs as relay nodes, which describes the data transmission process within the network. Based on this, an underwater multi-AUV Relay Deployment and Selection Mechanism in 3D space (RDSM) is proposed to achieve efficient underwater networking. Specifically, the RDSM includes the following key components. Firstly, an optimized relay node deployment strategy (RNDS) is used to deploy AUV nodes to effectively ensure network connectivity. Compared with traditional methods, this strategy has unique advantages in considering underwater space characteristics and can better adapt to the complex underwater environment. Secondly, a new utility function is constructed by integrating factors such as throughput, energy consumption, and load. The relay selection strategy based on utility maximization (RSS-UM) is used to select the next-hop relay node. This strategy is innovative in improving relay selection efficiency and optimizing network performance. Finally, in response to the problem of rapid energy consumption of relay nodes close to the base station, a power adjustment scheme is introduced to achieve a balance in node energy consumption, which is of great significance for prolonging network lifetime and improving overall stability. Experimental results show that compared with existing methods, the proposed mechanism achieves high utility and throughput, while maintaining balanced node energy consumption.</div></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"4 ","pages":"Pages 204-216"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704754","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}

{"title":"YOLOT: Multi-scale and diverse tire sidewall text region detection based on You-Only-Look-Once(YOLOv5)","authors":"Dehua Liu ,&nbsp;Yongqin Tian ,&nbsp;Yibo Xu ,&nbsp;Wenyi Zhao ,&nbsp;Xipeng Pan ,&nbsp;Xu Ji ,&nbsp;Mu Yang ,&nbsp;Huihua Yang","doi":"10.1016/j.cogr.2024.03.001","DOIUrl":"10.1016/j.cogr.2024.03.001","url":null,"abstract":"<div><p>Driving safety is significant to building a people-oriented and harmonious society, Tires are one of the key components of a vehicle and the character information on the tire sidewall is critical to their storage and usage. However, due to the diverse and differentiated features of typographic fonts, simultaneously extracting comprehensive characteristics is an extremely challenging task. To effectively break through these performance degradation issues, a multi-scale tire sidewall text region detection algorithm based on YOLOv5 is introduced, called YOLOT, which fuses comprehensive feature information in both width and depth directions. In this study, we firstly propose the Width and Depth Awareness (WDA) module in the text region detection field and successfully integrated it with the FPN structure to form the WDA-FPN. The purpose of WDA-FPN is to empower the network to capture multi-scale and multi-shape features in images, thereby augmenting the algorithm’s abstraction and representation of image features and concurrently boosting its robustness and generalization performance. Experimental findings indicate that, compared to the primary algorithm, YOLOT achieves significant improvement in accuracy, providing a higher detection reliability. The dataset and code for the paper are available at: https://github.com/Cloude-dehua/YOLOT.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"4 ","pages":"Pages 74-87"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266724132400003X/pdfft?md5=19ce0153cf7a9ea3214d8e7517f90940&pid=1-s2.0-S266724132400003X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140277459","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}

{"title":"Scalable and cohesive swarm control based on reinforcement learning","authors":"Marc-Andrė Blais,&nbsp;Moulay A. Akhloufi","doi":"10.1016/j.cogr.2024.05.003","DOIUrl":"https://doi.org/10.1016/j.cogr.2024.05.003","url":null,"abstract":"<div><p>Unmanned vehicles have seen a significant increase in a wide variety of fields such as for logistics, agriculture and other commercial applications. Controlling swarms of unmanned vehicles is a challenging task that requires complex autonomous control systems. Reinforcement learning has been proposed as a solution to this challenge. We propose an approach based on agent masking to enable a simple Deep Q-Network algorithm to scale on large swarms while training on relatively smaller swarms. We train our approach using multiple swarm sizes and learning rates and compare our results using metrics such as the number of collisions. We also compare the ability of our approach to scale on swarms ranging from five to 25 agents using metrics and visual analysis. Our proposed solution was able to guide a swarm of up to 100 agents to a target while keeping a good swarm cohesion and avoiding collision.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"4 ","pages":"Pages 88-103"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667241324000053/pdfft?md5=3db6df465ddd69e88b21f962232e9c5e&pid=1-s2.0-S2667241324000053-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141292146","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}

{"title":"Intelligent path planning for cognitive mobile robot based on Dhouib-Matrix-SPP method","authors":"Souhail Dhouib","doi":"10.1016/j.cogr.2024.02.001","DOIUrl":"10.1016/j.cogr.2024.02.001","url":null,"abstract":"<div><p>The Mobile Robot Path Problem looks to find the optimal shortest path from the starting point to the target point with collision-free for a mobile robot. This is a popular issue in robotics and in this paper the environment is considered as static and represented as a bidirectional grid map. Besides, the novel optimal method Dhouib-Matrix-SPP (DM-SPP) is applied to create the optimal shortest path for a mobile robot in a static environment. DM-SPP is a greedy method based on a column row navigation in the distance matrix and characterized by its rapidity to solve sparse graphs. The comparative analysis is conducted by applying DM-SPP on thirteen test cases and comparing its results to the results given by four metaheuristics the Max-Min Ant System, the Ant System with punitive measures, the A* and the Improved Hybrid A*. The outcomes acquired from different scenarios indicate that the proposed DM-SPP method can rapidly outperform the four predefined artificial intelligence methods.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"4 ","pages":"Pages 62-73"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667241324000028/pdfft?md5=753a3935e8733e20519f0d68f97e618f&pid=1-s2.0-S2667241324000028-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139966008","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}

{"title":"Robot assisted knee joint RoM exercise: A PID parallel compensator architecture through impedance estimation","authors":"M. Akhtaruzzaman ,&nbsp;Amir A. Shafie ,&nbsp;Md Raisuddin Khan ,&nbsp;Md Mozasser Rahman","doi":"10.1016/j.cogr.2023.11.003","DOIUrl":"10.1016/j.cogr.2023.11.003","url":null,"abstract":"<div><p>Knee joint rehabilitation exercise refers to a therapeutic procedure of a patient having dysfunctions in certain abilities to move knee joint due to some medical conditions like trauma or paralysis. The exercise is basically a series of repeated assistive physical movements within the range of motion (RoM) of the joint. Reflex action of limbs during RoM exercise causes inappropriate balance of load which may cause secondary injuries, such as damages of muscle or tendon tissues. Establishing correlation between impedance data and limb motions is important to solve this problem. This paper aims to design and modeling of a robotic arm with an original approach in control strategy which is developed based on the correlation in between the joint-impedances and joint-motion characteristics during exercise. The knee joint impedances are estimated based on the internal feedback of the system dynamics, that lead to design the torque compensator to improve the overall control signals in real time. This paper also demonstrates the characteristics of various responses of the system during exercise with human subject. Results have reflected good performances with low position and velocity tracking errors, <span><math><mrow><mo>±</mo><mn>0</mn><mo>.</mo><msup><mn>02</mn><mo>∘</mo></msup></mrow></math></span> and <span><math><mrow><mn>0.04</mn><mi>r</mi><mi>a</mi><mi>d</mi><mo>.</mo><mi>s</mi><mi>e</mi><msup><mi>c</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math></span> during hold phase; and <span><math><mrow><mo>±</mo><mn>0</mn><mo>.</mo><msup><mn>14</mn><mo>∘</mo></msup></mrow></math></span> and <span><math><mrow><mn>0.17</mn><mi>r</mi><mi>a</mi><mi>d</mi><mo>.</mo><mi>s</mi><mi>e</mi><msup><mi>c</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math></span> during motion phse. Though, the limitation of the prototype is its current RoM (limited to <span><math><msup><mn>0</mn><mo>∘</mo></msup></math></span>–<span><math><msup><mn>25</mn><mo>∘</mo></msup></math></span>), the system has potential in the application of RoM exercise for paraplegic or monoplegic patients.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"4 ","pages":"Pages 42-61"},"PeriodicalIF":0.0,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266724132300040X/pdfft?md5=e66adda08f021e960ed5946bd42e69d8&pid=1-s2.0-S266724132300040X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138619946","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}

{"title":"Optimizing Speech Emotion Recognition with Hilbert Curve and convolutional neural network","authors":"Zijun Yang ,&nbsp;Shi Zhou ,&nbsp;Lifeng Zhang ,&nbsp;Seiichi Serikawa","doi":"10.1016/j.cogr.2023.12.001","DOIUrl":"10.1016/j.cogr.2023.12.001","url":null,"abstract":"<div><p>In the realm of speech emotion recognition, researchers strive to refine representation methods for improved emotional information capture. Traditional one-dimensional time series classification falls short in expressing intricate emotional patterns present in speech signals, posing challenges in accuracy and robustness. This study introduces an innovative algorithm leveraging Hilbert curves to transform one-dimensional speech data into two-dimensional form, enhancing feature extraction accuracy. A tiling module based on Hilbert curve maximizes Hilbert curve arrangements for improved emotional information capture. Results reveal spatial efficiency gains up to 23,195 times pixel units, enhancing data storage. With an exceptional 98.73% accuracy, the proposed approach traditional methods, affirming its superior emotion classification performance on the same dataset. These empirical findings underscore the effectiveness of our proposed method in advancing speech emotion recognition.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"4 ","pages":"Pages 30-41"},"PeriodicalIF":0.0,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667241323000411/pdfft?md5=bfed8ff77493b33cdfb6f93a3ba0a2c9&pid=1-s2.0-S2667241323000411-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138609217","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}

{"title":"An improved single short detection method for smart vision-based water garbage cleaning robot","authors":"Anandakumar Haldorai,&nbsp;Babitha Lincy R,&nbsp;Suriya M,&nbsp;Minu Balakrishnan","doi":"10.1016/j.cogr.2023.11.002","DOIUrl":"https://doi.org/10.1016/j.cogr.2023.11.002","url":null,"abstract":"<div><p>These days, plastic trash is exponentially overwhelming our waterways. The catastrophe has attracted global attention at this point. As a result, protecting the environment on the water's surface has received increasing focus. Currently, manpower can be used to clean up contaminated water bodies like ponds, rivers, and oceans. Using the current cleaning approach results in low efficiency and hazard. The detection, collection, sorting, and removal of plastic trash from such water surfaces has been the subject of relatively little robotic research, despite the dire circumstances. From private sources, there are very few individual efforts to be found. In order to attain great efficiency without human assistance or operation, a fully autonomous water surface cleaning robot is proposed in this study. The robot was created to adapt to any type of water body found in the real world. An efficient object identification machine learning technique can be suggested for the creation of autonomous cleaning robots. This study improved the Single Short Detection (SSD) method to recognise objects accurately. Because of the enhanced detection techniques, the robot is able to collect trash on its own. With a mean average precision (mAP) of 94.099 % and a detection speed of up to 64.67 frames per second, experimental findings show that the enhanced SSD has exceptional detection speed and accuracy.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"4 ","pages":"Pages 19-29"},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667241323000393/pdfft?md5=a8305dcc49d8d37defb2594ad2b10d51&pid=1-s2.0-S2667241323000393-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138738971","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}

{"title":"Research on YOLOv3 model compression strategy for UAV deployment","authors":"Fei Xu ,&nbsp;Litao Huang ,&nbsp;Xiaoyang Gao ,&nbsp;Tingting Yu ,&nbsp;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}

{"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}

{"title":"A Review Of The Latest Research Technologies Related To 3D Point Cloud","authors":"Zhang Xin","doi":"10.1016/j.cogr.2023.09.001","DOIUrl":"https://doi.org/10.1016/j.cogr.2023.09.001","url":null,"abstract":"In recent years, point clouds have been widely used in fields such as computer vision, medical image processing, virtual and augmented reality, autonomous driving, and robotics. Despite the remarkable achievements of deep learning methods in processing 2D data, they still face some unique challenges when processing 3D point cloud data [1]. The unstructured and irregular nature of point clouds makes it difficult to directly apply traditional deep learning methods, so point cloud deep learning is still in its infancy. However, some progress has been made in the field of deep learning for point clouds. Researchers have proposed many innovative methods and network architectures for solving tasks such as classification, segmentation, generation, and detection of point cloud data. These methods include the network structure of PointNet [2], PointRCNN [9] and so on as well as various data enhancement and optimization strategies. These research results laid the foundation for the development of point cloud deep learning, and provided important reference and inspiration for future research.","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135638057","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}