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robotics and Computer Vision (ICRCV), International Conference on

作者： Fei Song Jinyu Li Fengzhen Tang Yandong Tang Bailu Si State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China School of Systems Science Beijing Normal University Beijing China

The field of traditional mobile robot navigation has undergone a gradual transformation, evolving into a standardized and procedural research domain. Through a fresh cognitive perspective on this navigation process, a bridge emerges between robotic researchers and cognitive neuroscientists, thereby fostering the growth of interdisciplinary exploration. This article meticulously examines three pivotal phases of robot navigation: information acquisition, simultaneous localization and mapping (SLAM), and path planning. These phases are intricately linked to pertinent cognitive research. Furthermore, a comprehensive survey of existing biomimetic and neuromorphic algorithms is presented, highlighting their applications in augmenting robot navigation capabilities. Ultimately, this article charts a visionary course for the future of robot navigation, envisioning its potential in facilitating more intelligent and efficient navigation techniques.

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Continual Forgetting for Pre-trained Vision Models

arXiv

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arXiv 2024年

作者： Zhao, Hongbo Ni, Bolin Fan, Junsong Wang, Haochen Wang, Yuxi Zhu, Fei Chen, Yuntao Meng, Gaofeng Zhang, Zhaoxiang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences China University of Chinese Academy of Sciences China Shanghai Artificial Intelligence Laboratory China

For privacy and security concerns, the need to erase unwanted information from pre-trained vision models is becoming evident nowadays. In real-world scenarios, erasure requests originate at any time from both users and model owners. These requests usually form a sequence. Therefore, under such a setting, selective information is expected to be continuously removed from a pre-trained model while maintaining the rest. We define this problem as continual forgetting and identify two key challenges. (i) For unwanted knowledge, efficient and effective deleting is crucial. (ii) For remaining knowledge, the impact brought by the forgetting procedure should be minimal. To address them, we propose Group Sparse LoRA (GS-LoRA). Specifically, towards (i), we use LoRA modules to fine-tune the FFN layers in Transformer blocks for each forgetting task independently, and towards (ii), a simple group sparse regularization is adopted, enabling automatic selection of specific LoRA groups and zeroing out the others. GS-LoRA is effective, parameter-efficient, data-efficient, and easy to implement. We conduct extensive experiments on face recognition, object detection and image classification and demonstrate that GS-LoRA manages to forget specific classes with minimal impact on other classes. Codes will be released on https://***/bjzhb666/GS-LoRA. Copyright © 2024, The Authors. All rights reserved.

关键词： Face recognition

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Augmented Reality based Training of Surgical Staff to Operate a Laparoscopic Instrument 7

Augmented Reality based Training of Surgical Staff to Operat...

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7th International Conference on Big Data, Knowledge and Control systems Engineering, BdKCSE 2021

作者： Ivanova, Veronika Boneva, Ani Vasilev, Plamen Ivanov, Stoyan Lekova, Svetla Institute of Robotics Bulgarian Academy of Sciences Department of Robotized and Mechatronics Intelligent Systems Sofia Bulgaria Institute of Information and Communication Technologies Bulgarian Academy of Sciences Department of Communication Systems and Services Sofia Bulgaria University of Chemical Technologies and Metallurgy Department of Industrial Automation Sofia Bulgaria

ISBN: (纸本)9781665410427

Simulation-based training is used in various fields. One of these fields is medical education. This study researches the use of Augmented Reality (AR) for laparoscopic surgeries and the settings for surgical training. AR adds an overlay of computer-generated content on a camera live image. AR helps medical staff to improve diagnostics and surgical treatment of patients. This paper presents an approach to teaching medical students to use augmented reality. Two software applications MOSAR ICTPro 1.0 and MOSAR ICTPro 2.0 have been developed, which are designed as upgrades to the multifunctional operating station for laparoscopic manipulations, developed on Tcl/Tk. Applications run under Android®. The experimental results take into account the force interaction of the instrument-obj ect. This work is a continuation of previous research in the field of surgical robotics. © 2021 IEEE.

关键词： Augmented reality

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Recognition of Dynamic Grasping Motion through Feature Extraction for Teleoperation

Recognition of Dynamic Grasping Motion through Feature Extra...

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WRC Symposium on Advanced robotics and automation (WRC SARA)

作者： Yanping Dai Wenyuan Chen Ning Xi Wenxue Wang 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 University of Chinese Academy of Sciences Beijing China Department of Industrial and Manufacturing Systems Engineering Emerging Technologies Institute University of Hong Kong Hong Kong China

Detecting human movement intentions and transmitting human operational capabilities to robots is of great significance for teleoperations. However, the difference in kinematic structure between human hands and robotic manipulators brings difficulties to teleoperation mapping. In this paper, we present an approach to hand grasp recognition and mapping based on feature extraction to drive heterogeneous remote manipulator. The hand grasp data set HUST recorded with a data glove was used for this study. The data were preprocessed and their features were extracted by PCA. A neural network-based classifier was obtained through offline training of human grasping activities. We compared different network models and selected the network with the best performance based on the training time and the task requirements of teleoperation. The experimental results showed that the recognition accuracy of dynamic grasping motion with feature extraction is higher than that without feature extraction and can be up to 97.47%. Finally, we validated the feasibility of this method to tele-operate an Barrett hand through online experiments. Our proposed method provides an effective approach of mapping natural human grasping operations to heterogeneous robotic teleoperation systems.

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A Novel Hybrid Method for Fault Diagnosis of Industrial Equipment Based on Vibration Signals 14

A Novel Hybrid Method for Fault Diagnosis of Industrial Equi...

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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Tao, Sen Wang, Kai Zeng, Peng Yu, Tianda Wu, Hao Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110169 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110169 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Nuclear Power Institute of China Chengdu610213 China State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing400044 China

ISBN: (纸本)9798350301359

Industrial equipment reliability is a critical issue that smart manufacturing systems must address. Implementation of online fault diagnosis for industrial equipment is a promising technology that can improve equipment reliability. Vibration analysis is a popular technique for equipment condition monitoring. For the non-linear and non-stationary features of industrial vibration signals, a hybrid model based on wavelet transform (WT), support vector machine (SVM), and grey wolf optimization algorithm (GWO) is proposed. The time-frequency domain features of fault signals are extracted by wavelet transform to solve the non-linearity of signals, and the obtained feature vectors are input into SVM for classification. Aiming at the problem of setting penalty coefficient and kernel function parameters, GWO is introduced to seek hyperparameters by using the hunting principle of wolves. In the experimental part, firstly, the comparison of the GWO-WT-SVM, GWO-SVM and SVM models was carried out. Secondly, Comparison with classical classification algorithms is made. Apparently, the GWO-WT-SVM model achieves higher accuracy than other models in bearing fault diagnosis. © 2023 IEEE.

关键词： component Fault diagnosis GWO PHM SVM

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Modeling of a System for Studying of Biological Tissues with the Use of Augmented Reality

Modeling of a System for Studying of Biological Tissues with...

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2021 International Conference Automatics and Informatics, ICAI 2021

作者： Vasilev, Plamen Ivanova, Veronika Andreev, Rumen Boneva, Ani University of Chemical Technologies and Metallurgy Department of Industrial Automation Sofia Bulgaria Institute of Robotics Bulgarian Academy of Sciences Department of Robotized and Mechatronics Intelligent Systems Sofia Bulgaria Institute of Information and Communication Technologies - Bulgarian Academy of Sciences Department of Communication Systems and Services Sofia Bulgaria

ISBN: (纸本)9781665426619

Trends in medical technologies are novel techniques and methods for study of biological tissue and and identify the existing of a tissue anomaly. An attempt in this direction is based on augmented reality surgical systems and devices that allow doctors to visualize data from diagnostic and surgical procedures, and improve work efficiency. The primary aim of this work is to study biological tissue using Augmented Reality surgical systems and to model the behavior of an instrument-organ in an environment close to its real one. To solve these problems the following is done: i) an original tactile model of an instrument for robot-assisted surgery is designed and produced ii) Unified Modeling Language (UML) design of augmented reality surgical system, and iii) an extension program MOSAR ICTPro 2.0 with an augmented reality upgrade is design. This work is a continuation of previous research in the field of surgical robotics. © 2021 IEEE.

关键词： Diagnosis

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Multiple Jumping and Perching of a Miniature Mobile Robot

Multiple Jumping and Perching of a Miniature Mobile Robot

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IEEE International Conference on robotics and Biomimetics

作者： Longlong Hu Lingqi Tang Hao Chen Yao Li Bing Li Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen China Key University Laboratory of Mechanism Machine Theory and Intelligent Unmanned Systems of Guangdong Shenzhen China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Many insects and miniature robots can traverse through narrow spaces via jumping motion; however, successful jumping and landing remains challenging in small-scale. No jumping robot within 90 g have yet demonstrated multiple jumping and perching successfully. Here, we demonstrate continuous jumping and perching of a miniature robot. The origami-inspired structure was manufactured by smart composite microstructure technology. A novel jumping mechanism was designed to load adjustable jump energy actuated by only one motor. A perching mechanism with two adhesive pads was designed to passively couple with the jumping mechanism, achieving perching on the ground and automatically detaching before the next jump. The jumping mechanism achieved adjustable jumping energy, corresponding to jumping height from 0.7 cm to 36.4 cm. The perching mechanism provided successful landing within a wide range (when velocity angle according to vertical direction $=18^{\circ}$ , the velocity magnitude $\leq 3.4\mathrm{m}/\mathrm{s}$ , the body angle $\leq 30^{\circ}$ ). $\text{Unlike}$ typical jumping robots performing uncontrollable landing and free-rolling, our robot kept its body up-righted during the whole movement and avoided free-rolling after landing. The self-contained prototype had 18 g of mass, 6.5 cm of body height, and 36.4 cm of maximum jumping height. Finally, an eccentric motor was integrated to perform multiple omnidirectional jumping and perching movements; this represents significant improvement of motion ability for small-scale mobile robots.

关键词： Insects Biomimetics Prototypes Motors Mobile robots Microstructure

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A Model Integrating Wavelet Convolution and Linear Attention Mechanism for Inventory Management in Power Warehouses

A Model Integrating Wavelet Convolution and Linear Attention...

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Information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Tao Li Zhengli Zhang Ligang Li Guozheng Du State Grid Shandong Electric Power Company Material Company Jinan China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

Accurate inventory management in power warehouses is crucial for maintaining consistency between records and actual stock levels. However, the complex storage layout and the diversity of stored materials often lead to human errors during manual inventory checks, resulting in inefficiencies. To address these challenges, we collected 2,000 images from power warehouse environments and created a binary classification dataset containing images of correctly and incorrectly placed items. We propose a novel network, CWAMNet, specifically designed for visual inventory management in complex power warehouse environments. CWAMNet integrates wavelet convolution and a linear attention mechanism to overcome the limitations of traditional convolutional neural networks (CNNs) and standard attention mechanisms, achieving a balance between model capacity and computational efficiency.

关键词： Solid modeling Visualization Attention mechanisms Accuracy Convolution Computational modeling Inventory management Computational efficiency Convolutional neural networks Standards

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Automated Disassembly Sequence Prediction for Industry 4.0 Using Enhanced Genetic Algorithm

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Computers, Materials & Continua 2021年第11期69卷 2531-2548页

作者： Anil Kumar Gulivindala M.V.A.Raju Bahubalendruni R.Chandrasekar Ejaz Ahmed Mustufa Haider Abidi Abdulrahman Al-Ahmari Department of Mechanical Engineering Industrial Robotics and Manufacturing Automation LaboratoryNational Institute of TechnologyKariakal609609India Department of Computer Science Engineering National Institute of TechnologyKariakal609609India Raytheon Chair for Systems Engineering Advanced Manufacturing InstituteKing Saud UniversityRiyadh11421Saudi Arabia Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The evolution of Industry 4.0 made it essential to adopt the Internet of Things(IoT)and Cloud Computing(CC)technologies to perform activities in the new age of *** technologies enable collecting,storing,and retrieving essential information from the manufacturing *** collected at sites are shared with others where execution automatedly *** obtained information must be validated at manufacturing to avoid undesirable data losses during the de-manufacturing ***,information sharing from the assembly level at the manufacturing stage to disassembly at the product end-of-life state is a major *** current research validates the information optimally to offer a minimum set of activities to complete the disassembly *** optimal disassembly sequence plan(DSP)can possess valid information to organize the necessary actions in ***,finding an optimal DSP is complex because of its combinatorial *** genetic algorithm(GA)is a widely preferred artificial intelligence(AI)algorithm to obtain a near-optimal solution for the DSP *** converging nature at local optima is a limitation in the traditional *** study improvised the GA workability by integrating with the proposed priori crossover *** optimality function is defined to reduce disassembly effort by considering directional changes as *** enhanced GA method is tested on a real-time product to evaluate the *** obtained results reveal that diversity control depends on the operators employed in the disassembly *** proposed method’s solution can be stored in the cloud and shared through IoT devices for effective resource allocation and disassembly for maximum recovery of the *** effectiveness of the proposed enhanced GA method is determined by making a comparative assessment with traditional GA and other AI methods at different population sizes.

关键词： automation internet of things disassembly sequence planning priori cross over operator enhanced GA disassembly predicates

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Learning to Turn: Deformation Control of a Novel Flexible Fishtail Actuated by Artificial Muscles

Learning to Turn: Deformation Control of a Novel Flexible Fi...

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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Junwen Gu Yukai Feng Zhengxing Wu Jian Wang Junzhi Yu Key Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Advanced Manufacturing and Robotics State Key Laboratory for Turbulence and Complex Systems College of Engineering Peking University Beijing China

ISBN: (数字)9798350388039

ISBN: (纸本)9798350388046

Achieving agile, fish-like turning remains a critical challenge in the development of robotic fish. The flexible deformation of a fish’s body significantly contributes to its turning ability. Consequently, researchers have devoted substantial efforts to enhancing the compliance and agility of robotic fish. This paper presents a novel flexible fishtail, inspired by the musculoskeletal structure of real fish, which can control the deformation of its flexible components during turns, improving the turning performance of robotic fish. The fishtail primarily consists of a carbon fiber plate with attached artificial muscles. These muscles can contract and stretch, causing the carbon fiber tail to bend and deform. To enable precise deformation control, a dynamic model of the fishtail is established based on Hamilton’s principle. Subsequently, deep reinforcement learning methods such as deep deterministic policy gradient and soft actor-critic are employed to develop a deformation controller under joint deflection. Cross-validation ensures the controller’s accuracy and robustness. The experimental results demonstrate that this controller facilitates the deformation control of the flexible tail across various turning amplitudes and speeds, allowing the fishtail to exhibit adjustable compliance or resistance to fluids during turning motion. Consequently, this enhances the agility of the robotic fish.

关键词： Artificial muscles Deformation Tail Learning (artificial intelligence) Muscles Turning Fish Robustness Carbon Robots

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