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Multi-Objective Trapping of UAVs Swarm Based on Gene Regulatory Networks

Multi-Objective Trapping of UAVs Swarm Based on Gene Regulat...

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

作者： Peng Xu Ze Shi Zhun Fan International Cooperation Base of Evolution Intelligence and Robotics of Guangdong Province College of Engineering Shantou University Shantou China

Encirclement mode formation in swarm robotic systems is a long-term hot research topic. Most of the encirclement control models designed by traditional methods are based on global information. this paper proposes a distributed unmanned swarm control method. the approach is based on gene regulatory network models and does not rely on any direct communication. the gene regulatory network model will generate a concentration field based on the input target and obstacle information, which will be used by the unmanned swarm to autonomously assign targets and guide the unmanned swarm to move towards the target. In this method, the UAV only needs to obtain the local information within the sensing range through the sensors carried by itself, and can complete the assignment and encirclement tasks of multiple targets by itself, getting rid of the unmanned swarm's dependence on global information. Finally, the validity and reliability of the proposed model are verified by simulating in an obstacle environment.

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A Hybrid Dynamic Modeling Method for External Torque Estimation of Robot Manipulators

A Hybrid Dynamic Modeling Method for External Torque Estimat...

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robotics, Intelligent Control and Artificial Intelligence (RICAI), international conference on

作者： Hanqing Yuan Shanrong Ren Jun Yao Jianliang Mao College of Automation Engineering Shanghai University of Electric Power Shanghai China School of Automation Southeast University Nanjing China

this paper introduces a hybrid modeling approach designed to estimate external forces in robot manipulators, with a focus on achieving high-precision force estimation while minimizing reliance on force/torque sensors. Traditional methods used for robot dynamics modeling and identification often grapple with unmodeled dynamics, system measurement noise, filtering delays, and equipment vibrations, all of which contribute to significant errors in the resulting dynamic models. To address these challenges, this paper leverages neural networks to mitigate the discrepancies in the physical model. the process commences with the application of the Newton-Euler method to perform dynamic modeling of the robot manipulators. Subsequent parameter identification is conducted to establish the physical model governing the robot manipulators' dynamics. Building on this physical model, a backpropagation (BP) neural network is employed to create a data-driven model of the model errors, thereby further enhancing model accuracy. To validate the effectiveness of this approach, external torque estimation experiments were carried out on a six-axis robot manipulators platform utilizing a Beckhoff controller. the experimental results affirm the method's capability to significantly improve the accuracy of external torque estimation in scenarios where force/torque sensors are unavailable.

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RBF Neural Networks Model-Free Feedforward Control of Piezoelectric Actuator

RBF Neural Networks Model-Free Feedforward Control of Piezoe...

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automation, Control and robotics Engineering (CACRE), international conference on

作者： Tao He Yinbing Wang Zhicheng Lin Kai Guo Duo Li Tao Huang College of Mechanical and Vehicle Engineering Chongqing University Chongqing China Department of Mechanical Engineering Shandong University Jinan China School of Mechanical Engineering Shanghai Jiaotong University Shanghai China

ISBN: (数字)9798350350302

ISBN: (纸本)9798350350319

Piezoelectric actuator is widely utilized in ultra-precision position fields. However, it is challenged by difficulty modeling dynamic properties, such as hysteresis, creep, vibration, etc. therefore, this paper presents a model-free feedforward control strategy of Radial Basis Functions Neural Network (RBFNN) for piezoelectric actuator. RBFNN is used to estimate the inverse dynamic model of the piezoelectric actuator, and avoiding the difficult modeling and impossible to invert problems. this feedforward controller has model-free advantage that it does not depend on the specific physics-based model and is only data-driven by references and control signals. the parameters of RBFNN are training by offline methods and online learning strategy especially. the offline learning method can compensate the trained trajectory with high precision, but for the untrained trajectory, the compensation accuracy is insufficient. For unknown target trajectories, the online learning strategy is proposed, which utilizes current data for parameter iteration and updating to improve control accuracy. Finally, simulation verify that the proposed model-free feedforward control strategy is effective. Especially the online learning strategy has the adaptive ability and generalization ability for unknown trajectory targets.

关键词： Vibrations Training Adaptation models Accuracy Creep Piezoelectric actuators Trajectory

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PRBJE: A Novel Pre-Relation Bidirectional Joint Extraction Model

PRBJE: A Novel Pre-Relation Bidirectional Joint Extraction M...

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robotics, Intelligent Control and Artificial Intelligence (RICAI), international conference on

作者： Qiang Yu Wenhui Zhou Shuai Dong Dongyuan Li School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

Joint extraction of entities and their semantic relationships to form factual triples from unstructured text is a fundamental task in information extraction. However, existing methods either ignore the interactions between relationships and entities or extract the subjects and objects in a fixed sequential order, which makes these approaches too sensitive to the extraction results of subjects. To address the two issues, we propose a pre-relation bidirectional joint extraction model (PRBJE). Specifically, we first detect potential relationships from the sentence to assist in the subsequent extraction of head and tail entities. then, we extract all possible entity pairs maintained in the text using two complementary directions: extracting the tail entity based on the head entity and vice versa. Finally, we assign relationships to all entity pairs by a biaffine model, thereby forming complete relational triples. Extensive experimental results demonstrate that our model outperforms state-of-the-art methods, with improved F1 scores across four public datasets, particularly achieving gains of 0.9 and 0.7 on NYT and WebNLG, respectively.

关键词： Semantics Refining Tail Predictive models Information retrieval Natural language processing Data mining Artificial intelligence Robots Intelligent control

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PCB Defect Detection Based on YOLOv5s

PCB Defect Detection Based on YOLOv5s

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Data Science and Computer Application (ICDSCA), IEEE international conference on

作者： Yuze Sun Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350368239

ISBN: (纸本)9798350368246

With the rapid development of the electronics manufacturing industry, the quality and reliability of printed circuit boards (PCBs) have become key factors in the performance of electronic products. Traditional PCB defect detection methods suffer from low efficiency and inaccurate results. this paper proposes a PCB defect detection method based on the YOLOv5s algorithm, aiming to improve the efficiency and accuracy of detection. YOLOv5s, as a lightweight real-time object detection model, is particularly suitable for automated detection tasks. this paper provides a detailed introduction to the basic structure and network design of the YOLOv5s model, including the Input, Backbone, Neck, and Head, and discusses its application in PCB defect detection. through experimental design, we used an open-source PCB image dataset provided by the Peking University Intelligent robotics Open Lab and trained and evaluated the model. the experimental results show that the model achieves high precision and recall rates in PCB defect detection, with a high mean Average Precision (mAP) value, demonstrating excellent performance. this study not only optimizes the quality control process of PCBs but also provides strong technical support for the automation and intelligent upgrading of the electronics manufacturing industry.

关键词： YOLO Manufacturing industries Adaptation models automation Computational modeling Printed circuits Quality control Production Data models Defect detection

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Model and Method of Resource-Saving Tasks Distribution for the Fog robotics 1

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7th international conference on Interactive Collaborative robotics, ICR 2022

作者： Klimenko, Anna Institute of IT and Security Technologies Kirovogradskaya Str. 25 Moscow117534 Russia

ISBN: (数字)9783031236099

ISBN: (纸本)9783031236082

In the current paper the question of the resource-saving tasks distribution is under consideration. Fog robotics, integrating fog computing and robotics, transfers the issues of the fog computing to the new subject area, as well as modern robots require more and more computing power. Tasks offloading to the fog spotlights the problem of assigning the task to those fog nodes, which failure rate decrease is acceptable. However, the models and methods of such task relocation problem are not presented in the literature, as well as the questions of devices reliability in the fog are not considered. In this paper a new model of task distribution is proposed, which allows to estimate the expediency of the task assigning to the node from the resource-saving point of view. Every node chooses: to process the data or to transmit them to the next node, which is situated farer from the data source. With the transfer of the data processing to the node of the low computational resources, its reliability can be improved by means of workload distribution through the extra-time, which appears due to the data transfer time reduction. Some selected simulation results are presented and discussed as well. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fog

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Embodied Runtime Monitoring of Learning-enabled Robot Perception Components

Embodied Runtime Monitoring of Learning-enabled Robot Percep...

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IEEE international conference on automation Science and Engineering (CASE)

作者： Deebul Nair Sathwik Panchangam Miguel A. Olivares-Mendez Nico Hochgeschwender Institute for Artificial Intelligence and Autonomous Systems Bonn-Rhein-Sieg University of Applied Sciences Sankt Augustin Germany Space Robotics Research Group (SpaceR) Interdisciplinary Centre for Security Reliability and Trust (SnT) University of Luxembourg Luxembourg Department of Mathematics and Computer Science University of Bremen Germany

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Monitoring during runtime, the estimated uncertainty from learning-enabled robot perception components, is a vital tool for assuring and evaluating model correctness. By utilizing different uncertainty estimation methods, monitors exploit the perceived sensor data and predictions generated by the perception model to make informed decisions, such as in safety-critical situations. In robotics, informed decisions should be aligned with embodied information regarding space and time. We argue and show that runtime monitors of learning-enabled components can be made more robust by including the embodiment information of the robot. However, it remains challenging to build uncertainty-based runtime monitors with embodiment information using only real-world datasets, which are tedious to collect. To this end, we propose a solution that leverages simulation-based datasets to characterize expected uncertainty estimates with robot embodiment. By constructing a nominal runtime monitor model based on simulated datasets, we establish a reference for the expected uncertainty along with the embodiment information that can be subsequently used to monitor real-world deployed perception components. We demonstrate the zero-shot sim-to-real transfer of our approach on a Kinova Gen3 manipulator picking task, for which the runtime monitor reliability model was trained using simulation-based data containing embodiment information of distance from the object being selected.

关键词： Runtime Uncertainty Computational modeling Estimation Predictive models Robot sensing systems Manipulators Reliability engineering Data models Monitoring

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Learning Peg-in-Hole Assembly Skills from Demonstration Based on Geometric Constraints 11

Learning Peg-in-Hole Assembly Skills from Demonstration Base...

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11th IEEE Annual international conference on CYBER Technology in automation, Control, and Intelligent Systems, CYBER 2021

作者： Tian, Zhonglai Cheng, Hongtai Zhao, Liang Cao, Shuo School of Mechanical Engineering and Automation Northeastern University Shenyang110819 China

ISBN: (纸本)9781665425278

Existing Learning from Demonstration (LfD) methods that can learn the peg-in-hole assembly skills mainly adopts the impedance control strategy, and generally do not pay attention to the assembly skill segmentation problem. this scheme is susceptible to environmental noise and is difficult to learn stable and effective assembly skills. In this paper, a geometric constraints-based LfD method is proposed and applied to the peg-in-hole assembly task. By identifying geometric constraints from the motion trajectory and considering the contact force information, the demonstration dataset is first segmented into a series of different sub-skills. Hybrid Force/Position Control (HFPC) schema is adopted to model those skills. the selection matrix is identified according to the geometric constraints. the Gaussian Mixture Model (GMM)/Gaussian Mixture Regression(GMR) and quasiperiodic motion models are used to representing the reference trajectories. A switching mechanism is also learned to coordinate those HFPCs with different configurations to accommodate different sub-skills. the experimental results show that the proposed method can accomplish the learning and reproduction of assembly tasks stably and effectively. © 2021 IEEE.

关键词： Demonstrations

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Synchronous Control of Bilateral Teleoperation under Hybrid Attacks

Synchronous Control of Bilateral Teleoperation under Hybrid ...

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robotics, Intelligent Control and Artificial Intelligence (RICAI), international conference on

作者： Xiuzhu Lu Yijun Zhang Jingyuan Li School of Automation Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

the wide prospect of bilateral teleoperation system in various practical applications has attracted many researchers to study its control methods. this scholarly work addresses the control challenges inherent to bilateral teleoperation systems subjected to hybrid cyber-attacks. A model is developed to describe the working state of a bilateral remote operating system, taking into account the impact of denial of service (DoS) attacks and false data injection (FDI) attacks that exist with probability. An adaptive controller is designed to mitigate issues arising from data loss due to DoS attacks and packet tampering resulting from FDI attacks. When an adversary launches an attack, there will be DoS or FDI attacks in the channel of a communication network therefore, this paper uses Bernoulli component to model the attack situation of the system. By utilizing the Lyapunov functional methodology, a criterion for global asymptotic stability is established, thereby substantiating that the master-slave system is capable of attaining global asymptotic synchronization. Finally, by selecting a manipulator system for numerical simulation, the effectiveness of the control strategy is verified.

关键词： Asymptotic stability Adaptation models Operating systems Stability criteria Robustness Synchronization Communication networks Adaptive control Numerical stability Cyberattack

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Voxel Mamba: group-free state space models for point cloud based 3D object detection 24

Voxel Mamba: group-free state space models for point cloud b...

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Proceedings of the 38th international conference on Neural Information Processing Systems

作者： Guowen Zhang Lue Fan Chenhang He Zhen Lei Zhaoxiang Zhang Lei Zhang The Hong Kong Polytechnic University and Centre for Artificial Intelligence and Robotics HKISI CAS Institute of Automation Chinese Academy of Sciences The Hong Kong Polytechnic University Centre for Artificial Intelligence and Robotics HKISI CAS and Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences

ISBN: (纸本)9798331314385

Serialization-based methods, which serialize the 3D voxels and group them into multiple sequences before inputting to Transformers, have demonstrated their effectiveness in 3D object detection. However, serializing 3D voxels into 1D sequences will inevitably sacrifice the voxel spatial proximity. Such an issue is hard to be addressed by enlarging the group size with existing serialization-based methods due to the quadratic complexity of Transformers with feature sizes. Inspired by the recent advances of state space models (SSMs), we present a Voxel SSM, termed as Voxel Mamba, which employs a group-free strategy to serialize the whole space of voxels into a single sequence. the linear complexity of SSMs encourages our group-free design, alleviating the loss of spatial proximity of voxels. To further enhance the spatial proximity, we propose a Dual-scale SSM Block to establish a hierarchical structure, enabling a larger receptive field in the 1D serialization curve, as well as more complete local regions in 3D space. Moreover, we implicitly apply window partition under the group-free framework by positional encoding, which further enhances spatial proximity by encoding voxel positional information. Our experiments on Waymo Open Dataset and nuScenes dataset show that Voxel Mamba not only achieves higher accuracy than state-of-the-art methods, but also demonstrates significant advantages in computational efficiency. the source code is available at https://***/gwenzhang/Voxel-Mamba.

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