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World Congress on Intelligent Control and Automation (WCICA)

作者： Hao Li S.X. Yang Fangju Wang Advanced Robotics and Intelligent Systems (ARIS) Laboratory School of Engineering University of Guelph Guelph Canada Computing & Information Science Department University of Guelph Guelph Canada

Membranes hold promise as a new water treatment method of the future. In this study, a device is designed to test the efficiency of membranes. The device is implemented to be controlled remotely. An Internet based remote control system is implemented on the membrane test device to make the users access to it easier. When using the system, a remote operator only needs a general purpose computer with Internet connection to conduct a test. The engineering objective is to perform robust control over the Internet connection. A control architecture that combines computer and the membrane testing hardware is built. This system has two primary parts, the server part and the client part. A server is used to provide the application to the operator to control the hardware. The client part is executed on the remote operator's computer. The client uses a TCP/IP protocol to connect to the server through the Internet. Communication coordination between the client and the server is developed using Java and Common Object Request Broker Architecture (CORBA).

关键词： Control systems Java Biomembranes Internet Web server System testing Computer architecture Hardware Robust control Application software

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Classification of grasps by multifingered robot hands

Classification of grasps by multifingered robot hands

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Proceedings of IEEE/RSJ International Conference on Intelligent Robots and systems. IROS '96

作者： Y. Zhang W.A. Gruver Beijing Aeronautics and Astronautics University Beijing China Intelligent Robotics and Manufacturing Systems Laboratory School of Engineering Science Simon Fraser University Burnaby BC Canada

This research characterizes grasping by multifingered robot hands through an investigation of the space of contact forces between fingers and the grasped object. By decomposing the space of contact forces into four subspaces, we develop a method to determine the dimensions of the subspaces with respect to the connectivity of the object. The relationship we obtain reveals the differences between three types of grasps classified in the paper and indicates how the contact force can be decomposed corresponding to each type of grasp. Therefore, it provides a guideline for formulating the distribution of contact forces as a basis for optimization. The subspaces and the determination of their dimensions are illustrated by examples.

关键词： Fingers Grasping Humans Force control Orbital robotics Intelligent robots Grippers Aerodynamics Manufacturing systems Laboratories

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Fractional-order fast terminal sliding mode control for nanopositioning

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IFAC-PapersOnLine 2023年第2期56卷 4313-4318页

作者： Geng Wang Sumeet S. Aphale Key Laboratory of Testing Technology for Manufacturing Process of Ministry of Education Southwest University of Science and Technology Mianyang China Artificial intelligence Robotics and Mechatronic Systems Group (ARMS) School of Engineering University of Aberdeen UK

A novel fast terminal sliding mode controller is proposed in this work for high-performance trajectory tracking at the nanometer scales. It combines a recursive integer-order non-singular high-order sliding manifold and a fractional-order fast fixed-time reaching law to ensure globally fast convergence, and adopts a time-delay-estimation based disturbance estimator deeming the designed controller robust to parameter uncertainty. The stability of the designed controller is verified through the Lyapunov framework, where the full analyses of convergence region and settling time are also presented. The tracking performance is experimentally verified on a piezo-stack-driven nano-positioning platform.

关键词： high-order sliding mode fractional calculus fast fixed-time convergence time delay estimation nanopositioning

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Transformers of extreme environments and their integration in a solar power infrastructure

Transformers of extreme environments and their integration i...

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AIAA Space and Astronautics Forum and Exposition, SPACE 2016

作者： Stoica, Adrian Quadrelli, Marco B. Ingham, Michel Tamppari, Leslie Mitchell, Karl Gutt, Gary Robotics and Mobility Systems Section Autonomous Systems Division Jet Propulsion Laboratory California Institute of Technology Mail Stop 198-219 4800 Oak Grove Drive PasadenaCA91109-8099 United States Autonomous Systems Division Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109-8099 United States Systems Engineering and Formulation Division Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109-8099 United States Science Division Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109-8099 United States Optical Section Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109-8099 United States

ISBN: (纸本)9781624104275

TransFormers (TF) are adaptive, shape-changing robotic systems that transform a region of an extreme environment, by projecting energy to it, making it milder at the targeted locale, with less environmental constraints on robots or humans operating there. This paper focuses on TF that project solar energy into dark and cold areas, such as permanently shaded craters, to provide illumination and heat and enable long-duration low-cost missions. Thus, TF offer an attractive solution for operations in ice-harboring craters on polar regions of the Moon or Mercury, and caves on Mars and lava tubes on Moon, of high interest not only for great scientific value, but also as shielded habitats for human bases. We performed high-level analysis of four mission scenarios. For a mission scenario involving operations up to 10km deep into the Moon’s Shackleton crater at below 80K temperatures we performed optical and thermal analyses to provide 300W of power to the solar panels of an MSL-class rover. The area of TFs reflecting sunlight from the rim must be more than ~1000m2. A network of TFs would form the backbone of an integrated Solar Power Infrastructure, in which the combined illumination provided to regions of interest is continuous, even though none of the individual TransFormers is continuously illuminated year-round. A Lunar South Pole Solar Power Infrastructure around Shackleton Crater is briefly introduced. © 2016 California Institute of Technology.

关键词： Solar energy

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Design of a piezoelectric meso-scale mobile robot: a compliant amplification approach

Design of a piezoelectric meso-scale mobile robot: a complia...

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IEEE International Conference on robotics and Automation (ICRA)

作者： V.K. Varma W.E. Dixon Nuclear Science & Technology Division-Remote Systems Oak Ridge National Laboratory Oak Ridge TN USA Engineering Science & Technology Division-Robotics Oak Ridge National Laboratory Oak Ridge TN USA

In this paper, the development of a novel piezoelectric-based locomotion design for a meso-scale mobile robot is described. The design exploits a compliant mechanical structure that enables piezoelectric forces to be amplified and transmitted to "legs" that propel the robot through a lift and pull scheme. The lift and pull design contrasts with typical slip/stick approaches that assume the inertia effects of the robot will allow the robot to slip on a smooth surface. By eliminating the slipping assumption, the potential surfaces that the proposed robot can traverse is significantly extended. Discussions are provided regarding the proposed locomotion method, kinematic modeling and system constraints, mechanical optimization, and control of the meso-scale robot.

关键词： Mobile robots Brushes Laboratories Friction Lubricants US Department of Energy Design engineering systems engineering and theory Leg Propulsion

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Trajectory Tracking Error Optimization based on Iterative Learning 4

Trajectory Tracking Error Optimization based on Iterative Le...

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4th IEEE International Conference on Power, Intelligent Computing and systems, ICPICS 2022

作者： Huang, Haoran Zhang, Tao Jia, Danping Zhang, Hualiang Yan, Hongyu Pei, Quanshen Ding, Fan Shenyang University of Technology School of Information Science and Engineering Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China School of Artificial Intelligence Shenyang University of Technology Shenyang China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

The X-Y precision plane motion platform is widely used in various precision motion control fields. In this paper, to improve the tracking accuracy of the X-Y precision plane motion table, a high-level iterative learning control with forgetting factor is proposed, and it is combined with a kind of PD-type iterative learning control with low-pass filter, and the method is applied to the Simulink model of the linear motor of the X-Y precision plane platform, thereby improving the trajectory accuracy of the X-Y precision plane motion platform. The traditional ILC has the disadvantage that the more number of iterations, the more divergent tracking error is. The new ILC proposed in this paper can not only effectively reduce the noise, reduce the tracking error, but also eliminate the problem of error divergence caused by too many iterations. At last, the experimental results of high-order PD iterative learning with forgetting factor and low-pass filter, PD iterative learning with low-pass filter, ordinary PD iterative learning are compared. The results show that the high-order PD iterative learning with forgetting factor and low-pass filter has better tracking effect than the first-order iterative learning and other several algorithms. It can make the movement trajectory of the X-Y precision plane motion platform more stable and improve the accuracy. © 2022 IEEE.

关键词： Low pass filters

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Revisiting population structure and particle swarm performance 10th

Revisiting population structure and particle swarm performan...

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10th International Joint Conference on Computational Intelligence, IJCCI 2018

作者： Fernandes, Carlos M. Fachada, Nuno Laredo, Juan L.J. Merelo, Juan Julian Castillo, Pedro A. Rosa, Agostinho LARSyS: Laboratory for Robotics and Systems in Engineering and Science University of Lisbon Lisbon Portugal HEI-LAB - Digital Human-Environment and Interactions Labs Universidade Lusófona Lisbon Portugal LITIS University of Le Havre Le Havre France Departamento de Arquitectura y Tecnología de Computadores University of Granada Granada Spain

ISBN: (纸本)9789897583278

Population structure strongly affects the dynamic behavior and performance of the particle swarm optimization (PSO) algorithm. Most of PSOs use one of two simple sociometric principles for defining the structure. One connects all the members of the swarm to one another. This strategy is often called gbest and results in a connectivity degree = , where is the population size. The other connects the population in a ring with = 3. Between these upper and lower bounds there are a vast number of strategies that can be explored for enhancing the performance and adaptability of the algorithm. This paper investigates the convergence speed, accuracy, robustness and scalability of PSOs structured by regular and random graphs with 3 ≤ ≤ . The main conclusion is that regular and random graphs with the same averaged connectivity may result in significantly different performance, namely when is low. Copyright © 2018 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved.

关键词： Particle swarm optimization (PSO)

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UAV Path Planning Based on Simultaneous Optimization of Monitoring Frequency and Security

UAV Path Planning Based on Simultaneous Optimization of Moni...

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第34届中国控制与决策会议

作者： Yifei Shu Yang Chen Mian Hu Huaiyu Wu Xingang Zhao Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences

UAVs have great potential when applied to persistent monitoring,but there are still problems such as difficulty in ensuring the monitoring frequency and easy leakage of monitoring path ***,it is necessary to increase the UAV monitoring frequency of targets and the randomness of monitoring paths as much as possible on the premise of covering all monitoring *** response to the above problems,this paper studies the UAV path planning problem of simultaneous optimization of monitoring frequency and path *** the evaluation of monitoring frequency and path security based on monitoring overdue time and path entropy,a mathematical model for UAV path planning is *** improved ant colony algorithm based on the monitoring overdue time(Overdue-aware Ant Colony Optimization,OACO) is designed,and finally the UAV flight trajectory with high monitoring frequency and the high monitoring path security is *** simulation results show that the method proposed in this paper can effectively improve the monitoring frequency of each monitoring node to be accessed,improve the security of the UAV monitoring path,which is of great significance for enhancing monitoring security and preventing intrusion.

关键词： UAV persistent monitoring path planning monitoring frequency overdue time security

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Online Learning in Blockchain-based Energy Trading systems 24

Online Learning in Blockchain-based Energy Trading Systems

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2024 International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, MobiHoc 2024

作者： Liu, Yunshu Cheung, Man Hon Huang, Jianwei Pillar of Engineering Systems and Design Singapore University of Technology and Design Singapore Singapore Department of Computer Science City University of Hong Kong Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network CSIJRI Joint Research Centre on Smart Energy Storage The Chinese University of HongKong Guangdong Shenzhen518172 China

ISBN: (纸本)9798400705212

In this paper, we consider a blockchain-based energy trading (BBET) system with the proof-of-stake (PoS) protocol. The system designer aims to minimize system cost by considering the prosumers’ strategic token allocation between blockchain staking and energy purchase for their applications. This is challenging as the system designer does not know prosumers’ private information of impatience levels towards different applications. To this end, we propose an online learning mechanism (OLM), which includes incentive mechanisms to guide both prosumers’ private information reporting and staking decisions in two phases. In the exploration phase, we design a randomized staking reward to encourage prosumers’ truthful reporting of their private information for the learning of impatience level distributions. Based on the threshold structure of the prosumers’ equilibrium staking strategies, in the exploitation phase, we propose a learning-error-based reward to minimize the system cost considering the finite-sample bias. By characterizing the optimal exploration duration, we prove that OLM achieves an asymptotic zero-regret against the complete information benchmark, with the regret bounded by O(3√lnT/T) when operating for T time slots. We implement the corresponding smart contract in Ethereum to demonstrate the feasibility of our approach. Experiment results show that our mechanism reduces regret by an average of 74% compared to the state-of-art mechanism. © 2024 Copyright held by the owner/author(s).

关键词： Blockchain

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Data engineering and AI-Powered Skin Cancer Identification for Healthcare Applications 28th

Data Engineering and AI-Powered Skin Cancer Identification f...

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28th International Conference on Knowledge Based and Intelligent information and engineering systems, KES 2024

作者： Ahmed, Imran Ahmad, Misbah Chehri, Abdellah Jeon, Gwanggil School of Computing and Information Science Anglia Ruskin University Cambridge United Kingdom Animal and Agriculture Department Hartpury University Gloucester United Kingdom Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol United Kingdom Canada Department of Embedded Systems Engineering Incheon National University Incheon Korea Republic of

Skin cancer diagnosis, a critical task in the medical domain, can be revolutionized through the application of advanced deep-learning techniques. This work investigates the efficacy of Convolutional Neural Networks (CNNs) in the automated classification of skin cancer. The process begins with a comprehensive explanation of key CNN layers: Conv2D, MaxPool2D, Dropout, and Dense. The Conv2D layers employ learnable filters that transform localized image segments, while MaxPool2D contributes to downsampling, effectively reducing computational cost and overfitting risk. Integrating these layers enables the network to capture local and global characteristics, which is crucial for accurate classification. Adding Dropout layers enhances generalization and mitigates overfitting by introducing randomness during training. ReLU activation functions infuse non-linearity, and the Flatten layer facilitates the transition to fully connected layers. The proposed CNN architecture is meticulously designed considering filter counts, kernel sizes, and pooling dimensions. The trained model demonstrates promising performance by utilizing the HAM10000 dataset, encompassing diverse skin lesion images across seven classes. The CNN model's parameters and architecture are systematically presented, offering insights into its design rationale. The model undergoes optimization with the Adam optimizer and annealing techniques to facilitate convergence. The model's effectiveness is evaluated on validation and test datasets, demonstrating an accuracy of 78.55% and 76.49%, respectively, for skin cancer classification. Data augmentation strategies are introduced to enhance model generalization further. The results underscore CNN's potential as a robust tool for automating skin cancer diagnosis, aligning with the broader trend of leveraging deep learning for medical image analysis © 2024 The Authors.

关键词： Convolutional neural networks

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