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Causality-based Prediction Method for the Diesel Engine Assembly Line System 18

Causality-based Prediction Method for the Diesel Engine Asse...

引用

IEEE 18th international conference on automation Science and Engineering (IEEE CASE)

作者： Hu, Jin-Hua Sun, Yan-Ning Xu, Hong-Wei Zhang, Zhan-Luo Qin, Wei Li, Xin-Yu Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

ISBN: (数字)9781665490429

ISBN: (纸本)9781665490429

The prediction of diesel engine power is a vital prerequisite for diesel engine quality promotion. A key issue of diesel engine power prediction is the selection of representative features for forecasting. However, current feature selection methods mainly rely on correlation analysis which cannot distinguish between direct correlation and indirect correlation. This paper presents a causal feature selection method for diesel engine power forecasting. Causalities distinguish direct influences from indirect ones. Therefore, this paper proposes a diesel engine power prediction framework based on using Markov Blanket-based feature selection approach and Gradient Boosting Decision Tree (GBDT) forecasting model. The proposed framework first applies Markov Blanket to identify causalities between manufacturing variables and diesel engine power and generates a causal feature set. Then, the quantitative relationship between causal features and the diesel engine power is established through GBDT. Finally, the proposed framework is tested by the experiment on a real diesel engine dataset. And the results show that the proposed framework delivers a satisfactory performance advantage for the validation condition in actual applications, the root mean squared error and the coefficient of variation of the root mean squared error of the GBDT model under the validation condition are 2.94kW and 1.17%, respectively.

关键词： Correlation Computer aided software engineering Diesel engines Markov processes Predictive models Feature extraction Boosting

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Tool or Partner: Mental-Aware Human-Robot Collaboration

Tool or Partner: Mental-Aware Human-Robot Collaboration

引用

automation, robotics and Computer Engineering (ICARCE), international conference on

作者： Jie Ren Yajie Liu Peiyan Li Xianglong Meng Yanzhi Dong Xiaohu Yuan School of Physics and Electronic Information Yantai University Yantai China Columbia University New York America Qiyuan Lab Beijing China

ISBN: (数字)9798331529505

ISBN: (纸本)9798331529512

Human-robot collaboration is a critical area of research, aiming to maximize synergy between humans and robots. However, a fundamental question persists: Should robots be seen as mere tools or genuine partners? Existing experimental environments have limitations in supporting virtual human-robot collaboration studies. In this paper, we propose a novel robot training framework designed for mental-aware human-robot collaboration. Within this framework, we introduce a Trust-Aware Mental Model incorporating a Human Trust component to represent human trust in robots. We create a virtual simulation environment to evaluate this model and conduct human-robot collaboration experiments. Quantitative and qualitative analyses demonstrate that integrating the trust-aware mental model enhances collaboration effectiveness. This finding underscores the necessity of viewing robots as true human partners and provides valuable insights for future research in human-robot collaboration.

关键词： Training Three-dimensional displays Data analysis Computational modeling Human-robot interaction Collaboration Predictive models Cognitive science Robots Next generation networking

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KGGPT: Empowering Robots with OpenAI’s ChatGPT and Knowledge Graph 16th

KGGPT: Empowering Robots with OpenAI’s ChatGPT and Knowle...

引用

16th international conference on Intelligent robotics and Applications, ICIRA 2023

作者： Mu, Zonghao Zhao, Wenyu Yin, Yue Xi, Xiangming Song, Wei Gu, Jianjun Zhu, Shiqiang Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China

ISBN: (纸本)9789819964949

This paper presents a study on using knowledge graph with ChatGP for robotics applications, called KGGPT. Traditional planning methods for robot tasks based on structured data and sequential actions, such as rosplan, have limitations such as limited data range and lack of flexibility to modify behaviors based on user feedback. Recent research has focused on combining AI planning with large language models (LLMs) to overcome these limitations, but generated text may not always be consistent with real-world physics and the robot skills to perform physical actions. To address these challenges, we propose KGGPT, a system that incorporates prior knowledge to enable ChatGPT for a variety of robotic tasks. KGGPT extracts relevant knowledge from the knowledge graph, generates a semantic description of the knowledge, and connects it to ChatGPT. The gap between the knowledge of ChatGPT and actual service environments is addressed by using the knowledge graph to model robot skills, task rules, and environmental constraints. The output is a behavior tree based on robot skills. We evaluate our method in an office setting and show that it outperforms traditional PDDL planning and a separate ChatGPT planning scheme. Additionally, our system reduces programming effort for applications when new task requirements arise. This research has the potential to significantly advance the field of robotics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Knowledge graph

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Single Underwater Image Enhancement via Contrast Stretching and Fusion

Single Underwater Image Enhancement via Contrast Stretching ...

引用

IEEE international conference on Cyber Technology in automation, Control, and Intelligent Systems

作者： Xu Wen-bo Zheng Xin-hui Tian Qi-yan Zhang Qi-feng State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Key Laboratory of Marine Robotics Shenyang Liaoning Province China University of Chinese Academy of Sciences Beijing China Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) Guangzhou China

In this paper, a novel single underwater image enhancement method is proposed to address color distortion, poor contrast and detail blur in underwater degraded images. First, the two attenuation channels of underwater degraded images are color compensated in the RGB color space. Then, the improved White Patch hypothesis is used to implement color correction. Moreover, contrast stretching and detail preservation of color-corrected images are carried out in the Lab color space. Finally, the two images and the weights defined are fused to obtain the final image at multiple scales. The experiment shows that the proposed method can effectively improve color distortion, enhance contrast and reduce blur in multiple underwater scenes, which is superior to existing methods.

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Optimal Control for Clutched-Elastic Robots: A Contact-Implicit Approach

Optimal Control for Clutched-Elastic Robots: A Contact-Impli...

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IEEE international conference on robotics and automation (ICRA)

作者： Dennis Ossadnik Vasilije Rakčević Mehmet C. Yildirim Edmundo Pozo Fortunić Hugo T. M. Kussaba Abdalla Swikir Sami Haddadin Chair of Robotics and Systems Intelligence and the Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich Germany Department of Electrical and Electronic Engineering Omar Al-Mukhtar University (OMU) Albaida Libya

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Intrinsically elastic robots surpass their rigid counterparts in a range of different characteristics. By temporarily storing potential energy and subsequently converting it to kinetic energy, elastic robots are capable of highly dynamic motions even with limited motor power. However, the time-dependency of this energy storage and release mechanism remains one of the major challenges in controlling elastic robots. A possible remedy is the introduction of locking elements (i.e. clutches and brakes) in the drive train. This gives rise to a new class of robots, so-called clutched-elastic robots (CER), with which it is possible to precisely control the energy-transfer timing. A prevalent challenge in the realm of CERs is the automatic discovery of clutch sequences. Due to complexity, many methods still rely on pre-defined modes. In this paper, we introduce a novel contact-implicit scheme designed to optimize both control input and clutch sequence simultaneously. A penalty in the objective function ensures the prevention of unnecessary clutch transitions. We empirically demonstrate the effectiveness of our proposed method on a double pendulum equipped with two of our newly proposed clutch-based Bi-Stiffness Actuators (BSA).

关键词： Potential energy Prevention and mitigation Optimal control Switches Motors Linear programming Timing

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Efficient Implicit Neural Reconstruction Using LiDAR

Efficient Implicit Neural Reconstruction Using LiDAR

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IEEE international conference on robotics and automation (ICRA)

作者： Dongyu Yan Xiaoyang Lyu Jieqi Shi Yi Lin School of Mechanical Engineering and Automation Harbin Institute of Technology (Shenzhen). Department of Electrical and Electronic Engineering The University of Hong Kong. Department of Electronic and Computer Engineering Hong Kong University of Science and Technology. Dji Co.

Modeling scene geometry using implicit neural representation has revealed its advantages in accuracy, flexibility, and low memory usage. Previous approaches have demonstrated impressive results using color or depth images but still have difficulty handling poor light conditions and large-scale scenes. methods taking global point cloud as input require accurate registration and ground truth coordinate labels, which limits their application scenarios. In this paper, we propose a new method that uses sparse LiDAR point clouds and rough odometry to reconstruct fine-grained implicit occupancy field efficiently within a few minutes. We introduce a new loss function that supervises directly in 3D space without 2D rendering, avoiding information loss. We also manage to refine poses of input frames in an end-to-end manner, creating consistent geometry without global point cloud registration. As far as we know, our method is the first to reconstruct implicit scene representation from LiDAR-only input. Experiments on synthetic and real-world datasets, including indoor and outdoor scenes, prove that our method is effective, efficient, and accurate, obtaining comparable results with existing methods using dense input.

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Securing Mobile Robots Multi-Party Authentication Technique Using Modified Elliptic Curve Cryptography

Securing Mobile Robots Multi-Party Authentication Technique ...

引用

2023 international conference on Advanced Computing and Communication Technologies, ICACCTech 2023

作者： Haldar, Bilas Jha, Prabin Kumar The Neotia University Computer Science & Engineering West Bengal Sarisha743368 India The Neotia University Robotics & Automation West Bengal Sarisha743368 India

ISBN: (纸本)9798350380880

Mobile robots continue to play a pivotal role in various industries, safeguarding their operations against unauthorized access and cyber-attacks becomes increasingly critical. The burgeoning adoption of ubiquitous services across a variety of sectors has led to an exponential rise in mobile robot users. In this scenario, the authentication of mobile robots becomes a crucial requirement for ensuring data security and confidentiality. To protect the authenticity of confidential information it is necessary to provide strong authentication systems as these robots become key participants for providing a range of services. This work proposed an innovative approach to enhance the security of mobile robots by implementing Multi-Part Authentication (MP A) techniques using a modified Elliptic Curve Cryptography (ECC) algorithm. Traditional authentication methods often prove insufficient when it comes to the unique challenges faced by mobile robots, including remote control vulnerabilities, data integrity concerns, and the potential for physical tampering. The present work is a novel, efficient, and robust cryptography technique for generating keys using modified ECC and arithmetic greatest common divisor for multi-party communications. Additionally, the proposed work also presented a novel methodology of encryption and decryption techniques for the distribution of the authentication keys using a modified ECC algorithm. The results of the work show that the proposed technique ensures that only authorized users are able to control mobile robots as well as provide protection against attacks. © 2023 IEEE.

关键词： Authentication

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Automatic 3D Object Recognition and Localization for Robotic Grasping 18

Automatic 3D Object Recognition and Localization for Robotic...

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18th international conference on Informatics in Control, automation and robotics (ICINCO)

作者： Santo, Bruno Antao, Liliana Goncalves, Gil Univ Porto Fac Engn Res Ctr Syst & Technol SYSTEC Rua Dr Roberto Frias P-4200465 Porto Portugal

ISBN: (纸本)9789897585227

With the emergence of Industry 4.0 and its highly re-configurable manufacturing context, the typical fixedposition grasping systems are no longer usable. This reality underlined the necessity for fully automatic and adaptable robotic grasping systems. With that in mind, the primary purpose of this paper is to join Machine Learning models for detection and pose estimation into an automatic system to be used in a grasping environment. The developed system uses Mask-RCNN and Densefusion models for the recognition and pose estimation of objects, respectively. The grasping is executed, taking into consideration both the pose and the object's ID, as well as allowing for user and application adaptability through an initial configuration. The system was tested both on a validation dataset and in a real-world environment. The main results show that the system has more difficulty with complex objects;however, it shows promising results for simpler objects, even with training on a reduced dataset. It is also able to generalize to objects slightly different than the ones seen in training. There is an 80% success rate in the best cases for simple grasping attempts.

关键词： robotics Grasping Object Pose Estimation Object Recognition

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Deep Learning-based Feature Fusion for Action Recognition Using Skeleton Information

Deep Learning-based Feature Fusion for Action Recognition Us...

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international conference on robotics and Artificial Intelligence (ICRAI)

作者： Fahad Ul Hassan Asif Mattoo Umar Shahbaz Khan Tahir Nawaz Nasir Rashid Department of Mechatronics Engineering College of Electrical and Mechanical Engineering National University of Sciences and Technology Islamabad Pakistan

Various action recognition systems have been proposed, but most of them are not feasible to be used in real-time applications. Skeleton-based action recognition has a low computational cost and is not affected by background changes. As the pose estimation models are becoming faster (almost real-time), a model was created with only 1.8M parameters named DD-net, which uses the skeleton information to predict the action. Recently an improved version of the model came out and was named TD-net. The model is very rich with geometric-based features but lacks motion-based features. To overcome this we added two motion features in the model named acceleration and velocity. These features were created using second order Taylor's approximation, in a window around the current frame. The model accuracy was compared with DD-net, TD-net, and state-of-the-art algorithms using three different datasets. An increase in accuracy is observed for all three datasets (i.e 1.1% for SHERC, 1.7% for FPHAB and 2% for JHMDB) when compared with TD-net.

关键词： Industries Computational modeling Pose estimation Predictive models Streaming media Skeleton Real-time systems

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A Graph Attention Spatio-temporal Convolutional Network for 3D Human Pose Estimation in Video

A Graph Attention Spatio-temporal Convolutional Network for ...

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IEEE international conference on robotics and automation (ICRA)

作者： Liu, Junfa Rojas, Juan Li, Yihui Liang, Zhijun Guan, Yisheng Xi, Ning Zhu, Haifei Guangdong Univ Technol Sch Electromech Engn Biomimet & Intelligent Robot Lab BIRL Guangzhou 510006 Peoples R China Chinese Univ Hong Kong Dept Mech & Automat Engn Hong Kong Peoples R China Univ Hong Kong Dept Indus Trial & Mfg Syst Engn Hong Kong Peoples R China

ISBN: (纸本)9781728190778

Spatio-temporal information is key to resolve occlusion and depth ambiguity in 3D human pose estimation. Previous methods have focused on either temporal contexts or local-to-global architectures that embed fixed-length spatiotemporal information. To date, there have not been effective proposals to simultaneously and flexibly capture varying spatiotemporal sequences and effectively achieves real-time 3D human pose estimation. In this work, we improve the learning of kinematic constraints in the human skeleton: posture, local kinematic connections, and symmetry by modeling local and global spatial information via attention mechanisms. To adapt to single- and multi-frame estimation. the dilated temporal model is employed to process varying skeleton sequences. Also, importantly, we carefully design the interleaving of spatial semantics with temporal dependencies to achieve a synergistic effect. To this end, we propose a simple yet effective graph attention spatio-temporal convolutional network (GAST-Net) that comprises of interleaved temporal convolutional and graph attention blocks. Experiments on two challenging benchmark datasets (Human3.6M and HumanEva-I) and YouTuhe videos demonstrate that our approach effectively mitigates depth ambiguity and self-occlusion, generalizes to half upper body estimation, and achieves competitive performance on 2D-to-3D video pose estimation.

关键词： Convolutional codes Adaptation models Three-dimensional displays Pose estimation Semantics Kinematics Skeleton

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