Cognitive Robotics最新文献_第2页

POMDP-based probabilistic decision making for path planning in wheeled mobile robot 基于 POMDP 的轮式移动机器人路径规划概率决策

Cognitive Robotics Pub Date : 2024-01-01 DOI: 10.1016/j.cogr.2024.06.001

Shripad V. Deshpande, Harikrishnan R, Rahee Walambe

{"title":"POMDP-based probabilistic decision making for path planning in wheeled mobile robot","authors":"Shripad V. Deshpande, Harikrishnan R, Rahee Walambe","doi":"10.1016/j.cogr.2024.06.001","DOIUrl":"https://doi.org/10.1016/j.cogr.2024.06.001","url":null,"abstract":"<div><p>Path Planning in a collaborative mobile robot system has been a research topic for many years. Uncertainty in robot states, actions, and environmental conditions makes finding the optimum path for navigation highly challenging for the robot. To achieve robust behavior for mobile robots in the presence of static and dynamic obstacles, it is pertinent that the robot employs a path-finding mechanism that is based on the probabilistic perception of the uncertainty in various parameters governing its movement. Partially Observable Markov Decision Process (POMDP) is being used by many researchers as a proven methodology for handling uncertainty. The POMDP framework requires manually setting up the state transition matrix, the observation matrix, and the reward values. This paper describes an approach for creating the POMDP model and demonstrates its working by simulating it on two mobile robots destined on a collision course. Selective test cases are run on the two robots with three categories – MDP (POMDP with belief state spread of 1), POMDP with distribution spread of belief state over ten observations, and distribution spread across two observations. Uncertainty in the sensor data is simulated with varying levels of up to 10 %. The results are compared and analyzed. It is demonstrated that when the observation probability spread is increased from 2 to 10, collision reduces from 34 % to 22 %, indicating that the system's robustness increases by 12 % with only a marginal increase of 3.4 % in the computational complexity.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"4 ","pages":"Pages 104-115"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667241324000077/pdfft?md5=ccfa806c0ae32c5aba224cbf968b6b8d&pid=1-s2.0-S2667241324000077-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141483724","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

Optimizing Food Sample Handling and Placement Pattern Recognition with YOLO: Advanced Techniques in Robotic Object Detection 利用 YOLO 优化食品样品处理和放置模式识别：机器人物体检测的先进技术

Cognitive Robotics Pub Date : 2024-01-01 DOI: 10.1016/j.cogr.2024.01.001

Shoki Koga, Keisuke Hamamoto, Huimin Lu, Y. Nakatoh

引用次数: 0

Autonomous novel class discovery for vision-based recognition in non-interactive environments 在非交互式环境中自主发现基于视觉识别的新类别

Cognitive Robotics Pub Date : 2024-01-01 DOI: 10.1016/j.cogr.2024.10.002

Xuelin Zhang , Feng Liu , Xuelian Cheng , Siyuan Yan , Zhibin Liao , Zongyuan Ge

引用次数: 0

High-fidelity learning-based motion cueing algorithm by bypassing worst-case scenario-based tuning technique 通过绕过基于最坏情况的调整技术实现基于学习的高保真运动提示算法

Cognitive Robotics Pub Date : 2024-01-01 DOI: 10.1016/j.cogr.2024.07.001

Mohammad Reza Chalak Qazani , Houshyar Asadi , Zoran Najdovski , Shehab Alsanwy , Muhammad Zakarya , Furqan Alam , Hassen M. Ouakad , Chee Peng Lim , Saeid Nahavandi

{"title":"High-fidelity learning-based motion cueing algorithm by bypassing worst-case scenario-based tuning technique","authors":"Mohammad Reza Chalak Qazani , Houshyar Asadi , Zoran Najdovski , Shehab Alsanwy , Muhammad Zakarya , Furqan Alam , Hassen M. Ouakad , Chee Peng Lim , Saeid Nahavandi","doi":"10.1016/j.cogr.2024.07.001","DOIUrl":"10.1016/j.cogr.2024.07.001","url":null,"abstract":"<div><p>The motion cueing algorithm (MCA) enhances the realism of simulator driving experiences by generating vehicle motions within platform limitations. Existing MCAs are typically tuned for worst-case scenarios, limiting their efficiency for medium or slow driving motions. This study proposes a comprehensive MCA unit using learning-based models to overcome this problem and efficiently utilise the simulator workspace for all driving scenarios. Data samples are regenerated to cover various motion signal levels, and three classical washout filters are tuned to extract optimal motion signals. A multilayer perceptron (MLP) is trained with these extracted datasets, forming an AI-based MCA that provides high-fidelity driving motions for any scenario while optimising the platform workspace. Simulink/MATLAB is used for modelling and evaluation. Results demonstrate the proposed model's superior performance, with lower motion sensation errors, a higher correlation between sensed motion signals, and more efficient platform workspace usage.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"4 ","pages":"Pages 116-127"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667241324000089/pdfft?md5=58f8e8d108ff26e8f330464bd10afbcf&pid=1-s2.0-S2667241324000089-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141852185","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 new paradigm to study social and physical affordances as model-based reinforcement learning 研究社会和物理负担能力的新范式--基于模型的强化学习

Cognitive Robotics Pub Date : 2024-01-01 DOI: 10.1016/j.cogr.2024.08.001

Augustin Chartouny, Keivan Amini, Mehdi Khamassi, Benoît Girard

引用次数: 0

Unmanned aerial vehicles advances in object detection and communication security review 无人驾驶飞行器在物体探测和通信安全方面的进展回顾

Cognitive Robotics Pub Date : 2024-01-01 DOI: 10.1016/j.cogr.2024.07.002

Asif Ali Laghari , Awais Khan Jumani , Rashid Ali Laghari , Hang Li , Shahid Karim , Abudllah Ayub Khan

{"title":"Unmanned aerial vehicles advances in object detection and communication security review","authors":"Asif Ali Laghari , Awais Khan Jumani , Rashid Ali Laghari , Hang Li , Shahid Karim , Abudllah Ayub Khan","doi":"10.1016/j.cogr.2024.07.002","DOIUrl":"10.1016/j.cogr.2024.07.002","url":null,"abstract":"<div><p>Unmanned Aerial Vehicles (UAVs) have become increasingly popular in recent years, with a wide range of applications in areas such as surveying, delivery, and security. UAV technology plays an important role in human life. Integrating Artificial Intelligence (AI) techniques into UAVs can significantly enhance their capabilities and performance. After the integration of AI in UAVs, their efficiency can be improved. It can automatically detect any object and highlight those objects with detailed information using AI. In most of the security surveillance places, UAV technology is beneficial. In this paper, we comprehensively reviewed the most widely used UAV communication protocols, including Wi-Fi, Zigbee, and Long-Range Wi-Fi (LoRaWAN). The review further explores valuable insights into the strengths and weaknesses of these protocols and how cognitive abilities such as perceptions and decision-making can be incorporated into UAV systems for autonomy. This paper provides a comprehensive overview of the state-of-the-art UAV object detection in remote sensing environments, as well as its types and use cases in different applications. It highlights the potential applications of these techniques in various domains, such as wildlife monitoring, search and rescue operations, and surveillance. The challenges and limitations of these methods and open research issues are given for future research.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"4 ","pages":"Pages 128-141"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667241324000090/pdfft?md5=ae431a84d10fa53e1e7e0f199787e6ef&pid=1-s2.0-S2667241324000090-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141978061","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

Improving log anomaly detection via spatial pooling: Combining SPClassifier with ensemble method 通过空间池改进日志异常检测：将 SPClassifier 与集合方法相结合

Cognitive Robotics Pub Date : 2024-01-01 DOI: 10.1016/j.cogr.2024.10.001

Hironori Uchida , Keitaro Tominaga , Hideki Itai , Yujie Li , Yoshihisa Nakatoh

{"title":"Improving log anomaly detection via spatial pooling: Combining SPClassifier with ensemble method","authors":"Hironori Uchida , Keitaro Tominaga , Hideki Itai , Yujie Li , Yoshihisa Nakatoh","doi":"10.1016/j.cogr.2024.10.001","DOIUrl":"10.1016/j.cogr.2024.10.001","url":null,"abstract":"<div><div>In the ever-updating field of software development, new bugs emerge daily, requiring significant time for analysis. As a result, research is being conducted on automating bug resolution using techniques such as anomaly detection through deep learning applied to text logs. This study focuses on anomaly detection using text logs and aims to address current challenges. Specifically, we aim to improve the accuracy of SPClassifier, a robust and lightweight AI model capable of handling dynamic log datasets through ad-hoc learning. We employ three ensemble learning methods to enhance the accuracy of SPClassifier. The method that achieved the greatest improvement was Improved Bagging, which combines the non-overlapping sampling of Pasting with the overlapping sampling of Bagging, resulting in a maximum F1-score improvement of 155 %. Additionally, on certain datasets, the F1-score surpassed that of well-known DNN methods by 130 %. Furthermore, the proposed method demonstrated lower variance compared to DNN methods, indicating its advantage, particularly in environments where datasets frequently fluctuate, such as development fields. These results highlight the clear superiority of the proposed method, which is lightweight in terms of computational resources and supports ad-hoc learning.</div></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"4 ","pages":"Pages 217-227"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704755","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

Big Data Course Multidimensional Evaluation Model based on Knowledge Graph enhanced Transformer

Cognitive Robotics Pub Date : 2024-01-01 DOI: 10.1016/j.cogr.2024.11.003

Ning Liu, Yeyangyi Xiang, Fei Wang, Shuyu Cao

{"title":"Big Data Course Multidimensional Evaluation Model based on Knowledge Graph enhanced Transformer","authors":"Ning Liu, Yeyangyi Xiang, Fei Wang, Shuyu Cao","doi":"10.1016/j.cogr.2024.11.003","DOIUrl":"10.1016/j.cogr.2024.11.003","url":null,"abstract":"<div><div>Based on the positioning of training application-oriented and innovative talents in the field of big data, this article aims to address the current situation where the theoretical system of big data course is not complete, the experimental system is unreasonable, and the assessment indicators are not perfect. A Transformer based “1 + 1 + <em>N</em>” big data course unified system and multidimensional evaluation model is constructed, reforms and practices are carried out in terms of improving the course theoretical system, increasing unit experiments and comprehensive experiment cases, and improving process assessment. The Transformer based multi-dimensional evaluation model of the big data course is proposed to solve the current problems of heavy theory and light practice, heavy standardization assessment and light innovation ability training in the course. The proposed course unified system and multidimensional evaluation model had achieved remarkable results, effectively increasing students’ construction of the big data professional knowledge system, enhancing students’ subjective initiative in learning the course, and significantly improving students’ innovative ability and ability to comprehensively solve practical problems.</div></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"4 ","pages":"Pages 237-244"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129865","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

RDSM: Underwater multi-AUV relay deployment and selection mechanism in 3D space RDSM：三维空间中的水下多AUV中继部署和选择机制

Cognitive Robotics Pub Date : 2024-01-01 DOI: 10.1016/j.cogr.2024.11.001

Yafei Liu , Na Liu , Hao Li , Yi Jiang , Junwu zhu

{"title":"RDSM: Underwater multi-AUV relay deployment and selection mechanism in 3D space","authors":"Yafei Liu , Na Liu , Hao Li , Yi Jiang , 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}

引用次数: 0

YOLOT: Multi-scale and diverse tire sidewall text region detection based on You-Only-Look-Once(YOLOv5) YOLOT：基于 "只看一次"（YOLOv5）的多尺度、多样化轮胎侧壁文字区域检测

Cognitive Robotics Pub Date : 2024-01-01 DOI: 10.1016/j.cogr.2024.03.001

Dehua Liu , Yongqin Tian , Yibo Xu , Wenyi Zhao , Xipeng Pan , Xu Ji , Mu Yang , Huihua Yang

{"title":"YOLOT: Multi-scale and diverse tire sidewall text region detection based on You-Only-Look-Once(YOLOv5)","authors":"Dehua Liu , Yongqin Tian , Yibo Xu , Wenyi Zhao , Xipeng Pan , Xu Ji , Mu Yang , 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}

引用次数: 0