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Photoelectric intelligent Detection Platform Based on Multifunctional Edge computing Equipment 43

Photoelectric Intelligent Detection Platform Based on Multif...

引用

43rd Chinese control conference, CCC 2024

作者： Xu, Qianjing Chen, Wenjie Xue, Zhijun State Key Lab of Intelligent Control and Decision of Complex Systems Beijing10081 China Beijing Institute of Technology School of Automation Beijing100081 China

ISBN: (纸本)9789887581581

In order to study to design a photoelectric intelligent platform based on multifunctional edge computing equipment by using photoelectric detection equipment TC505C, RK-Series Development Kits computing platform and Nvidia Jetson Series. The photoelectric intelligent platform can be used to run multiple target detection algorithms after design and implementation. Completed as follows. First of all, after the first stage of the photoelectric intelligent platform is completed, the host computer can establish communication with the photoelectric detection equipment, carry out automatic or manual servo control on the photoelectric detection equipment, switch its visible and infrared field of view and view it in real time through the monitor, and realize simple tracking function through the image processing box. Secondly, after the second stage of the photoelectric intelligent platform is built, under the premise of ensuring the normal availability of servo function, a variety of target detection algorithms can be debugged on multifunctional edge computing equipment. Overall, this paper uses the modular design concept to design, debug and implement the system in stages. Pay attention to the synchronous debugging of hardware and software functions of each module to ensure the stability of each module. Finally completed the design goals of each stage. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Program debugging

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Model-based Policy Optimization using Symbolic World Model

Model-based Policy Optimization using Symbolic World Model

引用

2024 international conference on intelligent Robots and systems

作者： Gorodetskiy, Andrey Mironov, Konstantin Panov, Aleksandr Moscow Inst Phys & Technol Ctr Cognit Modeling Dolgoprudnyi 141701 Russia Artificial Intelligence Res Inst Moscow 105064 Russia Russian Acad Sci Fed Res Ctr Comp Sci & Control Moscow 117312 Russia

ISBN: (纸本)9798350377712;9798350377705

The application of learning-based control methods in robotics presents significant challenges. One is that model-free reinforcement learning algorithms use observation data with low sample efficiency. To address this challenge, a prevalent approach is model-based reinforcement learning, which involves employing an environment dynamics model. We suggest approximating transition dynamics with symbolic expressions, which are generated via symbolic regression. Approximation of a mechanical system with a symbolic model has fewer parameters than approximation with neural networks, which can potentially lead to higher accuracy and quality of extrapolation. We use a symbolic dynamics model to generate trajectories in model-based policy optimization to improve the sample efficiency of the learning algorithm. We evaluate our approach across various tasks within simulated environments. Our method demonstrates superior sample efficiency in these tasks compared to model-free and model-based baseline methods.

关键词： Adversarial machine learning

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Integrating Fuzzy Logic and Deep Neural Networks for intelligent Construction Quality Management in Industrial Informatics 1

引用

international conference on intelligent and Fuzzy systems (INFUS)

作者： Fan, Ching-Lung Republ China Mil Acad Dept Civil Engn 1 Weiwu Rd Kaohsiung 830 Taiwan

ISBN: (数字)9783031700187

ISBN: (纸本)9783031700170;9783031700187

This research drew upon 987 construction inspection data points spanning from 1993 to 2023, sourced from the Taiwanese Public Construction Management Information System, to unveil the correlation between construction elements and quality outcomes. Initially, fuzzy logic was employed to compute the weights of 499 defects, resulting in the identification of 25 pivotal construction factors based on these weight allocations. Subsequently, a deep neural network was deployed to discern the interrelation between these significant construction factors (input variables) and the resultant construction quality (output variable). The evaluation of the prediction model's performance substantiated the influence of these key construction factors on project outcomes. In line with the contemporary trend of intelligent industrial informatics, this study harnessed the application of machine learning to enable these systems to adapt and learn from historical data. The developed hybrid soft computing approach, amalgamating fuzzy logic and artificial neural networks, demonstrated an impressive accuracy rate of 95.95%. The insights gleaned from this research offer project managers actionable intelligence to enhance project management efficacy and establish robust construction management protocols, thereby elevating the overall construction quality of projects.

关键词： Fuzzy logic Deep neural network Soft computing intelligent construction

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Design and control of a Soft Supernumerary Robotic Limb Based on Fiber-Reinforced Actuator

Design and Control of a Soft Supernumerary Robotic Limb Base...

引用

2024 international conference on intelligent Robots and systems

作者： Zhang, Tianyi Xu, Jiajun Lu, Yonghua Zhao, Mengcheng Huang, Kaizhen Chen, Bai Hou, Xuyan Li, Youfu Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210016 Peoples R China Harbin Inst Technol Sch Mechatron Engn Harbin 150001 Peoples R China City Univ Hong Kong Dept Mech Engn Hong Kong 999077 Peoples R China

ISBN: (纸本)9798350377712;9798350377705

Supernumerary robotic limbs (SRLs) provide additional wearable limbs to enhance the user's physical abilities. Most SRLs employ rigid structures, resulting in uncomfortable wearing experience and insufficient flexible manipulation. As a new type of SRL, soft SRLs offer operational flexibility, lightweight structure, and wearing safety, compensating for the shortcomings of rigid SRLs. However, due to the complex actuation mechanisms, soft SRLs pose challenges in multiple deformations and accurate controlling. In this paper, a soft SRL actuated by fiber-reinforced actuators (FRAs) is proposed. A kinematic model is established to capture the posture of the SRL. A control system is proposed to adjust the SRL posture precisely by configuration of the FRAs. Finally, the accuracy of the proposed control strategy is verified through experiments, and the SRL prototype exhibits flexibility and adaptability to various scenarios, effectively assisting users in accomplishing complex tasks.

关键词： Rigid structures

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Low-Cost Air Hockey Robot Using a Five-Bar Linkage Mechanism Driven by Position-control Servomotors

Low-Cost Air Hockey Robot Using a Five-Bar Linkage Mechanism...

引用

2024 international conference on intelligent Robots and systems

作者： Shinjo, Mirai Beltran-Hernandez, Cristian C. Hamaya, Masashi Tanaka, Kazutoshi OMRON SINIC X Corp Hongo 5-24-5Bunkyo Ku Tokyo Japan

ISBN: (纸本)9798350377712;9798350377705

In human-robot interaction (HRI) research, ball games pose significant challenges that demand robotic solutions that are both cost-effective and user-friendly for non-experts. Air hockey, characterized by safe, non-direct-contact play and a simplified state-action space, emerges as an ideal platform for such research. Despite the availability of various air hockey robots, their high cost and complexity have limited widespread use among researchers requiring robotics expertise. Addressing this gap, we introduce a low-cost, accessible air hockey robot designed to facilitate HRI studies. Featuring a lightweight five-bar linkage mechanism powered by low-cost servomotors for position control, this robot combines efficiency with ease of use. The complete robot's cost is estimated at $346.8, with the arm weighing a mere 19 grams. The robot precisely returns the puck by intermittently adjusting its target joint positions, achieving a play with an average return error of 42.6 mm. These characteristics affirm the robot's potential as a valuable tool for advancing HRI research.

关键词： Servomotors

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Low Level Adaptive control of Vehicle Longitudinal Dynamics: a Driver Inspired Architecture. 28

Low Level Adaptive Control of Vehicle Longitudinal Dynamics:...

引用

28th international conference on System Theory control and computing

作者： Penet, Maxime Le-Gall, Gaetan Valeo Brain Creteil France

ISBN: (纸本)9798350364309;9798350364293

This paper deals with the design of a low level controller for acceleration tracking using the vehicle's pedals. Its architecture aims at mimicking the various elements which are involved when a human is driving. The focus is on providing an efficient and easy to deploy solution. This is achieved by using data collected from non tailored driving sessions and requiring basic vehicle's knowledge. This topic takes all its relevance in the context of autonomous driving where the focus is usually on features which provide high level requests such as acceleration. Performances are claimed under the tacit requirements that requests will be accurately tracked by a (real) vehicle. This is however a strong assumption with respects to chassis control challenges, wide deployment concerns and calibration by non experts. From a calibration perspective, the focus is on how to process data collected in a relatively uncontrolled way to automatically generate appropriate parameters. From a feedback design perspective, the challenge is to retain a performing yet easy to interpret controller structure. To illustrate the controller's performances, tests results obtained from real life experiments are presented.

关键词： Automotive control systems Autonomous systems control Applications

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Robot Swarm control Based on Smoothed Particle Hydrodynamics for Obstacle-Unaware Navigation

Robot Swarm Control Based on Smoothed Particle Hydrodynamics...

引用

2024 international conference on intelligent Robots and systems

作者： Eguchi, Michikuni Nishimura, Mai Yoshida, Shigeo Hiraki, Takefumi Cluster Metaverse Lab 8-9-5 Nishigotanda Shinagawa Tokyo 1400031 Japan Univ Tsukuba Grad Sch Comprehens Human Sci 1-2 Kasuga Tsukuba Ibaraki 3058550 Japan OMRON SINIC X Corp 5-24-5 Hongo Tokyo 1130033 Japan

ISBN: (纸本)9798350377712;9798350377705

Robot swarms hold immense potential for performing complex tasks far beyond the capabilities of individual robots. However, the challenge in unleashing this potential is the robots' limited sensory capabilities, which hinder their ability to detect and adapt to unknown obstacles in real-time. To overcome this limitation, we introduce a novel robot swarm control method with an indirect obstacle detector using a smoothed particle hydrodynamics (SPH) model. The indirect obstacle detector can predict the collision with an obstacle and its collision point solely from the robot's velocity information. This approach enables the swarm to effectively and accurately navigate environments without the need for explicit obstacle detection, significantly enhancing their operational robustness and efficiency. Our method's superiority is quantitatively validated through a comparative analysis, showcasing its significant navigation and pattern formation improvements under obstacle-unaware conditions.

关键词： Obstacle detectors

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intelligent computing Making Access control More Secure: From Cipher to Trust 20th

Intelligent Computing Making Access Control More Secure: Fro...

引用

20th international conference on intelligent computing (ICIC)

作者： Wang, Tian Fang, Weidong Ni, Mufan Yang, Xiaoliang Chen, Wei Chen, Wentao Zhang, Wuxiong Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Sci & Technol Microsyst Lab Shanghai 201899 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Shanghai Res & Dev Ctr Micronano Elect Shanghai 201210 Peoples R China Minist Water Resources HaiHe River Water Conservancy Commiss HaiHe River Lower Reaches Adm Bur Sluice Management Dept Duliujian River Tianjin 300380 Peoples R China China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China State Grid Xinjiang Co Ltd Elect Power Res Inst Urumqi 830011 Peoples R China Southeast Univ Chien Shiung Wu Coll Nanjing 211102 Peoples R China

ISBN: (纸本)9789819756025;9789819756032

Access control is an important component of information security system. It protects resources from unauthorized users or misuses by authorized users. Yet, the security of cipher-based access control may be compromised due to its fixed and predefined features. As an intelligent computing technology that simulates human behavior, trust evaluation can be an effective method for capturing users' unpredictable actions. Trust-based access control is a better method for tracking dynamic user behavior. Researchers are becoming increasingly enthusiastic in integrating intelligent computing concepts with access control. However, there is no article that covers all aspects of trust-based access control. Therefore, a comprehensive survey in this area of study is desperately needed. In this paper, we will investigate a novel approach to access control with trust. Access control is thus becoming more secure by incorporating trust evaluation. Furthermore, we will demonstrate how trust can be used to enhance access control secure by combining it with other intelligent computing technology like game theory.

关键词： Access control Trust Information Security intelligent computing Game Theory

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A Soft Robotic System Automatically Learns Precise Agile Motions Without Model Information

A Soft Robotic System Automatically Learns Precise Agile Mot...

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2024 international conference on intelligent Robots and systems

作者： Bachhuber, Simon Pawluchin, Alexander Pal, Arka Boblan, Ivo Seel, Thomas FAU Erlangen Nurnberg Dept Artificial Intelligence Biomed Engn D-91052 Erlangen Germany Univ Appl Sci & Technol CoRoLab Berlin D-13353 Berlin Germany Indian Inst Technol Bhubaneswar Sch Mech Sci Bhubaneswar 752050 Odisha India Leibniz Univ Hannover Inst Mechatron Syst D-30167 Hannover Germany

ISBN: (纸本)9798350377712;9798350377705

Many application domains, e.g., in medicine and manufacturing, can greatly benefit from pneumatic Soft Robots (SRs). However, the accurate control of SRs has remained a significant challenge to date, mainly due to their nonlinear dynamics and viscoelastic material properties. Conventional control design methods often rely on either complex system modeling or time-intensive manual tuning, both of which require significant amounts of human expertise and thus limit their practicality. In recent works, the data-driven method, Automatic Neural ODE control (ANODEC) has been successfully used to - fully automatically and utilizing only input-output data - design controllers for various nonlinear systems in silico, and without requiring prior model knowledge or extensive manual tuning. In this work, we successfully apply ANODEC to automatically learn to perform agile, non-repetitive reference tracking motion tasks in a real-world SR and within a finite time horizon. To the best of the authors' knowledge, ANODEC achieves, for the first time, performant control of a SR with hysteresis effects from only 30 s of input-output data and without any prior model knowledge. We show that for multiple, qualitatively different and even out-of-training-distribution reference signals, a single feedback controller designed by ANODEC outperforms a manually tuned PID baseline consistently. Overall, this contribution not only further strengthens the validity of ANODEC, but it marks an important step towards more practical, easy-to-use SRs that can automatically learn to perform agile motions from minimal experimental interaction time.

关键词： Feedback control

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Advancing Scalability and Efficiency in Distributed Network computing Through Innovative Resource Allocation and Load Balancing Strategies

Advancing Scalability and Efficiency in Distributed Network ...

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international conference on intelligent and Fuzzy systems (INFUS)

作者： Singh, Manisha Bhardwaj, Purvee Bhardwaj, Ramakant Narayan, Satyendra CKEM Coll Dept Comp Sci Satna MP India Rabindranath Tagore Univ Dept Phys Sci Bhopal India Amity Univ Dept Math Kolkata WB India Sheridan Inst Technol Dept Appl Comp Oakville ON Canada

ISBN: (纸本)9783031671944;9783031671951

This research aims to explore and develop original approaches for improving the scalability and efficiency of distributed network computing systems. The escalating demand for high-performance computing and the widespread integration of interconnected devices present a critical challenge in optimizing resource allocation and load balancing within distributed networks. The study will investigate cutting-edge algorithms, employ machine learning techniques, and devise adaptive strategies to dynamically distribute computing tasks across network nodes. The primary objective is to enhance system scalability, minimize response times, and maximize resource utilization, contributing significantly to the progression of network technologies in distributed computing environments. The research findings are expected to have substantial implications for various applications, including cloud computing, edge computing, and Internet of Things (IoT) ecosystems.

关键词： Edge computing Internet of Things (IoT) Cloud computing Digital Ecosystems Challenges and Solutions Responsible Technology Evolution

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