Cognitive Robotics最新文献_第2页

LiPE: Lightweight human pose estimator for mobile applications towards automated pose analysis

Cognitive Robotics Pub Date : 2025-01-01 DOI: 10.1016/j.cogr.2024.11.005

Chengxiu Li , Ni Duan

引用次数: 0

Mobile robot path planning using deep deterministic policy gradient with differential gaming (DDPG-DG) exploration 利用深度确定性策略梯度与微分博弈（DDPG-DG）探索移动机器人路径规划

Cognitive Robotics Pub Date : 2024-01-01 DOI: 10.1016/j.cogr.2024.08.002

Shripad V. Deshpande , Harikrishnan R , Babul Salam KSM Kader Ibrahim , Mahesh Datta Sai Ponnuru

{"title":"Mobile robot path planning using deep deterministic policy gradient with differential gaming (DDPG-DG) exploration","authors":"Shripad V. Deshpande , Harikrishnan R , Babul Salam KSM Kader Ibrahim , Mahesh Datta Sai Ponnuru","doi":"10.1016/j.cogr.2024.08.002","DOIUrl":"10.1016/j.cogr.2024.08.002","url":null,"abstract":"<div><p>Mobile robot path planning involves decision-making in uncertain, dynamic conditions, where Reinforcement Learning (RL) algorithms excel in generating safe and optimal paths. The Deep Deterministic Policy Gradient (DDPG) is an RL technique focused on mobile robot navigation. RL algorithms must balance exploitation and exploration to enable effective learning. The balance between these actions directly impacts learning efficiency.</p><p>This research proposes a method combining the DDPG strategy for exploitation with the Differential Gaming (DG) strategy for exploration. The DG algorithm ensures the mobile robot always reaches its target without collisions, thereby adding positive learning episodes to the memory buffer. An epsilon-greedy strategy determines whether to explore or exploit. When exploration is chosen, the DG algorithm is employed. The combination of DG strategy with DDPG facilitates faster learning by increasing the number of successful episodes and reducing the number of failure episodes in the experience buffer. The DDPG algorithm supports continuous state and action spaces, resulting in smoother, non-jerky movements and improved control over the turns when navigating obstacles. Reward shaping considers finer details, ensuring even small advantages in each iteration contribute to learning.</p><p>Through diverse test scenarios, it is demonstrated that DG exploration, compared to random exploration, results in an average increase of 389% in successful target reaches and a 39% decrease in collisions. Additionally, DG exploration shows a 69% improvement in the number of episodes where convergence is achieved within a maximum of 2000 steps.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"4 ","pages":"Pages 156-173"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667241324000119/pdfft?md5=8c083de5d6ac1af9d3cedcb0733a30fa&pid=1-s2.0-S2667241324000119-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142271646","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

Emerging trends in human upper extremity rehabilitation robot 人体上肢康复机器人的新趋势

Cognitive Robotics Pub Date : 2024-01-01 DOI: 10.1016/j.cogr.2024.09.001

Sk. Khairul Hasan, Subodh B. Bhujel, Gabrielle Sara Niemiec

{"title":"Emerging trends in human upper extremity rehabilitation robot","authors":"Sk. Khairul Hasan, Subodh B. Bhujel, Gabrielle Sara Niemiec","doi":"10.1016/j.cogr.2024.09.001","DOIUrl":"10.1016/j.cogr.2024.09.001","url":null,"abstract":"<div><p>Stroke is a leading cause of neurological disorders that result in physical disability, particularly among the elderly. Neurorehabilitation plays a crucial role in helping stroke patients recover from physical impairments and regain mobility. Physical therapy is one of the most effective forms of neurorehabilitation, but the growing number of patients requires a large workforce of trained therapists, which is currently insufficient. Robotic rehabilitation offers a promising alternative, capable of supplementing or even replacing human-assisted physical therapy through the use of rehabilitation robots. To design effective robotic devices for rehabilitation, a solid foundation of knowledge is essential. This article provides a comprehensive overview of the key elements needed to develop human upper extremity rehabilitation robots. It covers critical aspects such as upper extremity anatomy, joint range of motion, anthropometric parameters, disability assessment techniques, and robot-assisted training methods. Additionally, it reviews recent advancements in rehabilitation robots, including exoskeletons, end-effector-based robots, and planar robots. The article also evaluates existing upper extremity rehabilitation robots based on their mechanical design and functionality, identifies their limitations, and suggests future research directions for further improvement.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"4 ","pages":"Pages 174-190"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667241324000120/pdfft?md5=a51e80d94f3f2f6ca53c667c4682ef83&pid=1-s2.0-S2667241324000120-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142271647","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

Fourier Hilbert: The input transformation to enhance CNN models for speech emotion recognition 傅里叶·希尔伯特：输入变换增强CNN模型的语音情感识别

Cognitive Robotics Pub Date : 2024-01-01 DOI: 10.1016/j.cogr.2024.11.002

Bao Long Ly

{"title":"Fourier Hilbert: The input transformation to enhance CNN models for speech emotion recognition","authors":"Bao Long Ly","doi":"10.1016/j.cogr.2024.11.002","DOIUrl":"10.1016/j.cogr.2024.11.002","url":null,"abstract":"<div><div>Signal processing in general, and speech emotion recognition in particular, have long been familiar Artificial Intelligence (AI) tasks. With the explosion of deep learning, CNN models are used more frequently, accompanied by the emergence of many signal transformations. However, these methods often require significant hardware and runtime. In an effort to address these issues, we analyze and learn from existing transformations, leading us to propose a new method: Fourier Hilbert Transformation (FHT). In general, this method applies the Hilbert curve to Fourier images. The resulting images are small and dense, which is a shape well-suited to the CNN architecture. Additionally, the better distribution of information on the image allows the filters to fully utilize their power. These points support the argument that FHT provides an optimal input for CNN. Experiments conducted on popular datasets yielded promising results. FHT saves a large amount of hardware usage and runtime while maintaining high performance, even offers greater stability compared to existing methods. This opens up opportunities for deploying signal processing tasks on real-time systems with limited hardware.</div></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"4 ","pages":"Pages 228-236"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748300","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

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