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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Cihat Talat Akpinar Özcan Koşar Akif Durdu Computer Engineering Research and Development Elfatek Elektronik Ltd. Co. Konya Turkey Department of Electrical and Electronics Engineering Robotics Automation Control Laboratory (RAC-LAB) Konya Technical University Konya Turkey

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

This study aims to compare and improve the performance of four different machine learning algorithms (Naive Bayes, Multi-Layer Perceptron (MLP), Decision Trees, and Support Vector Machines (SVM)) in classification problems. The analyses emphasize the impact of data preprocessing steps and hyperparameter optimization on model performance. As part of the data preprocessing, missing values were imputed, categorical data were transformed into numerical data, and normalization procedures were applied. It was observed that normalization significantly enhanced the performance of the MLP and SVM algorithms in particular. Furthermore, additional improvements in accuracy rates were achieved through hyperparameter optimization. Naive Bayes and Decision Trees were found to exhibit stable performance regardless of data scaling. This study demonstrates that proper data preprocessing and model selection can significantly enhance algorithm performance in classification problems.

关键词： Support vector machines Accuracy Machine learning algorithms Data preprocessing Hyperparameter optimization Data models Bayes methods Classification algorithms Numerical models Decision trees

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Gait Optimization for Legged Systems Through Mixed Distribution Cross-Entropy Optimization

Gait Optimization for Legged Systems Through Mixed Distribut...

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IEEE-RAS International Conference on Humanoid Robots

作者： Ioannis Tsikelis Konstantinos Chatzilygeroudis Computer Engineering and Informatics Department University of Patras Patras Greece Department of Electrical & Computer Engineering Laboratory of Automation and Robotics (LAR) University of Patras Patras Greece Department of Mathematics Computational Intelligence Laboratory (CILab) University of Patras Patras Greece

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

Legged robotic systems can play an important role in real-world applications due to their superior load-bearing capabilities, enhanced autonomy, and effective navigation on uneven terrain. They offer an optimal trade-off between mobility and payload capacity, excelling in diverse environments while maintaining efficiency in transporting heavy loads. However, planning and optimizing gaits and gait sequences for these robots presents significant challenges due to the complexity of their dynamic motion and the numerous optimization variables involved. Traditional trajectory optimization methods address these challenges by formulating the problem as an optimization task, aiming to minimize cost functions, and to automatically discover contact sequences. Despite their structured approach, optimization-based methods face substantial difficulties, particularly because such formulations result in highly nonlinear and difficult to solve problems. To address these limitations, we propose CrEGOpt, a bi-level optimization method that combines traditional trajectory optimization with a black-box optimization scheme. CrEGOpt at the higher level employs the Mixed Distribution Cross-Entropy Method to optimize both the gait sequence and the phase durations, thus simplifying the lower level trajectory optimization problem. This approach allows for fast solutions of complex gait optimization problems. Extensive evaluation in simulated environments demonstrates that CrEGOpt can find solutions for biped, quadruped, and hexapod robots in under 10 seconds. This novel bi-level optimization scheme offers a promising direction for future research in automatic contact scheduling.

关键词： Pipelines Closed box Humanoid robots Graphics processing units Real-time systems Planning Quadrupedal robots Optimization Trajectory optimization Convergence

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Refined Motion Compensation with Soft Laser Manipulators using Data-Driven Surrogate Models

arXiv

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arXiv 2024年

作者： Yan, Yongjun Ding, Qingpeng Li, Mingwu Yan, Junyan Cheng, Shing Shin Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen518055 China Multi-Scale Medical Robotics Center Hong Kong

Non-contact laser ablation, a precise thermal technique, simultaneously cuts and coagulates tissue without the insertion errors associated with rigid needles. Human organ motions, such as those in the liver, exhibit rhythmic components influenced by respiratory and cardiac cycles, making effective laser energy delivery to target lesions while compensating for tumor motion crucial. This research introduces a data-driven method to derive surrogate models of a soft manipulator. These low-dimensional models offer computational efficiency when integrated into the Model Predictive Control (MPC) framework, while still capturing the manipulator’s dynamics with and without control input. Spectral Submanifolds (SSM) theory models the manipulator’s autonomous dynamics, acknowledging its tendency to reach equilibrium when external forces are removed. Preliminary results show that the MPC controller using the surrogate model outperforms two other models within the same MPC framework. The data-driven MPC controller also supports a design-agnostic feature, allowing the interchangeability of different soft manipulators within the laser ablation surgery robot system. © 2024, CC BY.

关键词： Laser ablation

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Spiking neural networks: Towards bio-inspired multimodal perception in robotics

arXiv

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arXiv 2024年

作者： Oikonomou, Katerina Maria Balaska, Vasiliki Tsintotas, Konstantinos A. Mavridis, Christos N. Kansizoglou, Ioannis Gasteratos, Antonios The Department of Production and Management Engineering Laboratory of Robotics and Automation Democritus University of Thrace Xanthi Greece The Department of Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Spiking neural networks (SNNs) have captured apparent interest over the recent years, stemming from neuroscience and reaching the field of artificial intelligence. However, due to their nature SNNs remain far behind in achieving the exceptional performance of deep neural networks (DNNs). As a result, many scholars are exploring ways to enhance SNNs by using learning techniques from DNNs. While this approach has been proven to achieve considerable improvements in SNN performance, we propose another perspective: enhancing the biological plausibility of the models to leverage the advantages of SNNs fully. Our approach aims to propose a brain-like combination of audio-visual signal processing for recognition tasks, intended to succeed in more bio-plausible human-robot interaction applications. © 2024, CC BY-NC-SA.

关键词： Adversarial machine learning

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Virtual Reality Solutions for Troubleshooting Autonomous Industrial Mobile Robots

Virtual Reality Solutions for Troubleshooting Autonomous Ind...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

This study aims to develop a virtual reality (VR) application to improve the fault detection and troubleshooting processes of autonomous industrial mobile robots. The complex systems and operating environments of autonomous robots make fault detection challenging for users, requiring effective and user-friendly solutions to overcome these difficulties. In this context, the Robot Operating System (ROS) was utilized to detect errors in robots and record these errors in a database. Thanks to the modular structure and robust communication infrastructure offered by ROS, faults in the robots were monitored and analyzed through ROS *** study demonstrates the effective use of VR technology in the maintenance and repair processes of autonomous industrial mobile robots. By integrating ROS-based data collection and fault detection into a VR environment for user training, robot operators are enabled to troubleshoot faults more quickly and effectively. In this way, it is aimed to achieve time and cost savings in industrial processes.

关键词： Training Service robots Databases Fault detection Operating systems Virtual reality Maintenance engineering Maintenance Mobile robots Monitoring

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Truth in the Age of Misinformation: A Dual-Approach Model for Improved Fake News Detection

Truth in the Age of Misinformation: A Dual-Approach Model fo...

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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Ishan Malik Sanjay Kumar Singh Pranshu Saxena University School of Automation and Robotics Guru Gobind Singh Indraprastha University East Delhi Campus Delhi India Department of Information Technology ABES Engineering College Ghaziabad India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

The inspiration that drives this study is the increase in the prevalence of misinformation, and the need to solve the big challenge of detecting fake news. With the possibility of uncontrolled misinformation disastrously impacting our society, the objective of our study is to make contributions towards the body of knowledge which aims to improve the efficiency and accuracy of current methodologies to detect false information. Although there has been an improvement in the fake news detection systems, their adaptation to real-time applications remains sub-optimal due to the subtle and complex processing involved with handling misinformation. The purpose of this study is to bridge this gap and come up with a better detection model using the Recurrent neural network (RNN) models like Long Short-Term Memory (LSTM) and Bi-directional LSTM (BiLSTM) that will enhance the performance of the existing models, especially in terms of detection accuracy, as the existing systems are weak in the “disagree” category and mostly misclassify it. We use a mix of machine learning algorithms and natural language processing techniques. Integrating LSTM with BiLSTM will analyze textual content at a granular level to find patterns representing fake news. The preliminary results have indicated that our model is performing significantly better than existing ones, with an overall improvement of up to 86.25% in accuracy [15]. However, in the “disagree” category, there are a few challenges faced by the system, reflecting the scope for many improvements in the future. Our research has made a valuable contribution to the knowledge and tools in the area of digital information credibility by providing a strong model that improves detection rates of more subtle cues of misinformation thereby, combating the spread of fake news.

关键词： Training Adaptation models Accuracy Bidirectional long short term memory Real-time systems Natural language processing Reliability Fake news Standards Overfitting

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A Cooperative Security Monitoring Mechanism Aided by Optimal Multiple Slave Cluster Heads for UASNs

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China Communications 2023年第5期20卷 148-169页

作者： Yougan Chen Wei Wang Xiang Sun Yi Tao Zhenwen Liu Xiaomei Xu Key Laboratory of Underwater Acoustic Communication and Marine Information Technology Xiamen UniversityXiamen 361005China Shenzhen Research Institute of Xiamen University Shenzhen 518000China Dongshan Swire Marine Station College of Ocean and Earth SciencesXiamen UniversityXiamen 361102China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Department of Electrical and Computer Engineering University of New MexicoAlbuquerqueNM 87131USA College of Harbour and Coastal Engineering Jimei UniversityXiamen 361021China

As each cluster head(CH)sensor node is used to aggregate,fuse,and forward data from different sensor nodes in an underwater acoustic sensor network(UASN),guaranteeing the data security in a CH is very *** this paper,a cooperative security monitoring mechanism aided by multiple slave cluster heads(SCHs)is proposed to keep track of the data security of a *** designing a low complexity“equilateral triangle algorithm(ETA)”,the optimal SCHs(named as ETA-based multiple SCHs)are selected from the candidate SCHs so as to improve the dispersion and coverage of SCHs and achieve largescale data security *** addition,by analyzing the entire monitoring process,the close form expression of the probability of the failure attack identification for the SCHs with respect to the probability of attack launched by ordinary nodes is *** simulation results show that the proposed optimal ETA-based multiple SCH cooperation scheme has lower probability of the failure attack identification than that of the existing *** addition,the numerical simulation results are consistent with the theoretical analysis results,thus verifying the effectiveness of the proposed scheme.

关键词： underwater acoustic sensor networks data security cluster head nodes optimal location distribution of multiple slave cluster head nodes

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Pose-Shape Unified Planning of Cable-driven Serial Manipulators based on Multiple Arm-Angle Construction

Pose-Shape Unified Planning of Cable-driven Serial Manipulat...

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IEEE International Conference on robotics and Biomimetics

作者： Wenshuo Li Bin Liang Boyang Lin Junxiang Li Wenfu Xu School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Department of Automation Tsinghua University Beijing China

Cable-driven serial manipulator (CDSM) has slender body and high dexterity. Hence it has large potentials in confined space application. However, in terms of complicated motion, achieving overall shape planning and control for the manipulator as a whole has been rarely accomplished, greatly limiting the utilization of redundant degrees of freedom. Therefore, this paper proposes a pose-shape unified planning method based on multiple arm-angle construction and optimization. Firstly, we derive the multi-space mapping relationships, in particularly introducing the concept of arm shape for joint space and deriving the corresponding analytical expressions. Subsequently, we derive the Jacobian matrix of Multiple arm-angel, which theoretically guarantees the efficiency of planning and the ability to achieve acceptable goals in a globally feasible and stable manner. Simulations demonstrate that our framework enables rapid and efficient arm-angles planning while ensuring high pose accuracy. Finally, experiments are conducted using the CDSM to verify the arm's motion capabilities, including pose adjustment and maintaining arm shape during movement. Compared to other planning and closed-loop control methods, the results confirm the consistency and reliability of the arm shape and end-effector pose.

关键词：

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Design and Implementation of Neuro-Fuzzy Control Approach for Robot's Trajectory Tracking 7

Design and Implementation of Neuro-Fuzzy Control Approach fo...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Kiruba Sandou, D. Sunad Kumara, A.N. Choudhary, Binod Kumar Gurpur, Shashikala Sarishma Natrayan, L. Sivaramkumar, M. Manakula Vinayagar Institute of Technology Department of Robotics and Automation Pondicherry605107 India BGS Institute of Technology Department of Computer Science and Engineering Adichunchanagiri University Karnataka Mandya571448 India ARKA Jain University Department of Electrical and Electronics Engineering Jharkhand Jamshedpur832108 India Maharashtra Pune411014 India Graphic Era Deemed to Be University Department of Management Studies Uttarakhand Dehradun India Saveetha School of Engineering SIMATS Department of Mechanical Engineering Tamilnadu Chennai602105 India Karpagam Academy of Higher Education Department of EEE Tamiinadu Coimbatore India

ISBN: (纸本)9798350340600

Achieving precise trajectory tracking while minimizing errors, reducing settling time, and curbing overshoot presents a significant challenge in the domain of robot-manipulator control. In response, this paper proposes a novel controller design for robot manipulators, incorporating two distinct approaches: a conventional fuzzy controller and an advanced Neuro-Fuzzy controller. These controllers are tailored to elevate trajectory tracking performance by simultaneously optimizing time characteristics in robot manipulator responses. To assess their efficacy, a range of test scenarios is devised, encompassing different trajectory types, including linear and nonlinear paths. Upon comparative analysis, the results highlight an intriguing contrast: while the fuzzy controller exhibits higher maximum overshoot and longer settling times in the robot's response, the Neuro-Fuzzy controller consistently demonstrates superior performance. The comprehensive error analysis such as Integral Square Error (ISE) and Integral Absolute Error (IAE) further underscores the capability of the Neuro-Fuzzy-based robot manipulator in delivering trajectory tracking precision. This study contributes to the advancement of trajectory control techniques for robot manipulators, paving the way for enhanced automation and performance across various applications. © 2023 IEEE.

关键词： Controllers

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Riding the Rollercoaster: Improving UAV Piloting Skills with Augmented Visualization and Collaborative Planning

Riding the Rollercoaster: Improving UAV Piloting Skills with...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Riccardo Franceschini Javier Rodriguez Marquez Matteo Fumagalli Julian Cayero Becerra Eurecat Centre Tecnològic de Catalunya Robotics and Automation Unit Cerdanyola del Vallès Barcelona Spain Department of Electrical Engineering Automation and Control group Danish Technical University Elektrovej Kgs. Lyngby Denmark

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

Operating unmanned aerial vehicles (UAVs) in complex environments can be challenging, particularly for inexperienced operators. This paper introduces a method aimed at enhancing the piloting experience by incorporating an intermediary processing layer between the remote controller and the drone. The presented approach empowers operators to control both the speed and direction of the UAV along a secure path, which is continuously computed and overlaid onto the operator's camera stream. The UAV autonomously plans and executes this path, adapting to the operator's commands and en-vironmental changes. The primary aim of this proposed solution is to enhance the operator's situational awareness, perception, as well as the safety and efficiency of UAV navigation. The paper outlines the system and methodology employed, showing its ability to operate at a high enough frequency to enable seamless user interactions. Furthermore, to validate the effectiveness of this approach, a real-world test and a user-based experimental study conducted in a simulation environment with an audience comprising varying levels of pilot expertise have been carried out.

关键词： Human-robot interaction Process control Collaboration Autonomous aerial vehicles Safety Planning Haptic interfaces

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