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An Improved Deep Learning Method-based Detection of Transmission Line Insulator Defects

An Improved Deep Learning Method-based Detection of Transmis...

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2023 International Conference on Computer, Artificial Intelligence, and control Engineering, CAICE 2023

作者： Gao, Pengpei Song, Chunhe Yu, Shimao Wu, Tingting State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781510665040

The insulator is a vital component of power equipment that aids in power transmission. Aiming at transmission line faults caused by missing insulator pieces, this paper proposed to divide insulator detection and defect location into two steps. Additionally, we add Squeeze-and-Excitation (SE) module into the Faster R-CNN model at the insulator detection stage, which could enhance insulator detection accuracy under similar model complexity and also improve the detection efficiency of the YOLOv3 model on insulator defect location. © 2023 SPIE.

关键词： Object detection

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AGV semantic attack detection based on extended stochastic Petri net

AGV semantic attack detection based on extended stochastic P...

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作者： Zhang, Sichao Liang, Wei Zhang, Yinlong Wang, Kai State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

Semantic attack, as a covert and highly destructive network attack, seriously threatens the security of automatic guided vehicle (AGV) system. To make it worse, the dynamic change of wireless channel makes semantic attack detection more non-intractable. In order to solve the issues on the current semantic attack detection in wireless network environment, this paper proposes an extended stochastic Petri net (ESPN)-based detection method. It builds a stochastic Petri net (SPN) model for the normal operation process of AGV system, and detects the semantic attack by comparing the characteristics of relevance matrix, average dwell time and token density. Additionally, we innovatively add the wireless channel evaluation parameter in the SPN model, to estimate the current wireless channel state and eliminate the impact of wireless interference on attack detection. Eventually, an AGV attack detection system experimental platform has been developed to verify the effectiveness of the proposed method. The experimental results show that the proposed algorithm can effectively eliminate the impact of wireless channel interference on semantic attack detection, and has impressive detection performance. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Stochastic systems

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Air-Ground Robots’ Cooperation-Based Mountain Glaciers Thickness Continuous Detection: systems And Applications 16th

Air-Ground Robots’ Cooperation-Based Mountain Glaciers Thic...

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16th International Conference on Intelligent robotics and Applications, ICIRA 2023

作者： Zhong, Jikang Li, Peng Liu, Xu Zhao, Pinhui Jiang, Han Yang, Liying Li, Decai Bu, Chunguang He, Yuqing State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9789819964826

In this article, we present a multi-robot continuous ice thickness measurement system that can operate in plateau glacial environments with natural slopes up to 30°, large crevasses of glacier, slippery snow/ice ground surface, and inevitable terrain undulations. The ground station operator can operate the unmanned ground vehicle (UGV) with improved safety driving ability by following the optimal driving advice provided by the driving skill learning model. The air-ground robot collaboration algorithm coordinated the operation of the UGV with the unmanned aerial vehicle (UAV) to improve the mobility of the UGV in complex glacier environments. We obtain multi-scale and multi-aspect environmental information through air-ground collaboration and use a lightweight environmental modeling method to obtain a three-dimensional model of the glacier surface and subglacial terrain. The system was applied in the Korchung Gangri Glacier in the Tibet Plateau in June 2022. The experimental results show that the system can operate in the extreme environment of plateau glaciers and collect high-precision continuous ice thickness distribution data. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Thickness measurement

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A Framework for Automatic Identification of Neural Network Structural Redundancy Based on Reinforcement Learning 5

A Framework for Automatic Identification of Neural Network S...

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5th International Conference on Computer Information Science and Artificial Intelligence, CISAI 2022

作者： Wu, Tingting Song, Chunhe Zeng, Peng State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781510662520

The increasing structure of neural networks makes it difficult to deploy on edge devices with limited computing resources. Network pruning has become one of the most successful model compression methods in recent years. Existing works usually compress models by removing unimportant filters based on the importance. However, the importance-based algorithms tend to ignore the parameters that extract edge features with small criterion values. And recent studies have shown that the existing criteria rely on norm and lead to similar model compression structures. Aiming at the problems of ignoring edge features and manually specifying the pruning rate in current importance-based model pruning algorithms, this paper proposes an automatic recognition framework for neural network structure redundancy based on reinforcement learning. First, we perform cluster analysis on the filters of each layer, and map the filters into a multidimensional space to generate similar sets with different functions. We then propose a criterion for identifying redundant filters within similar sets. Finally, we use reinforcement learning to automatically optimize the cluster dimension, and then determine the optimal pruning rate for each layer to reduce the performance loss caused by pruning. Extensive experiments on various benchmark network architectures and datasets demonstrate the effectiveness of our proposed framework. © 2023 SPIE.

关键词： Reinforcement learning

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Robust Tracking for Electromagnetic-Actuated Microrobot Via Sliding Mode control 49

Robust Tracking for Electromagnetic-Actuated Microrobot Via ...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Wu, Yihang Qu, Ziyao Chang, Xiaocong Hu, Zhijian Yang, Rongni Ma, Renjie Harbin Institute of Technology State Key Laboratory of Robotics and Systems Harbin China School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Control Science and Engineering Shandong University Jinan China

ISBN: (纸本)9798350331820

Microrobots can work in small, enclosed spaces and complete complex tasks. It has potential applications in the field of biomedical and life sciences. This paper focuses on investigating robust dynamic tracking for electromagnetic-actuated microrobot via sliding mode control. First, we generalize the planar motion to the case of spatial trajectory tracking and then establish the three-dimensional tracking error model of microrobot. Then, we deploy a novel sliding control strategy and ensure the stability of sliding mode dynamics, once the robotic state reaches the sliding surface and sustains there in the sequel. Finally, the numerical simulation is conducted, compared to the traditional proportion-integration-differentiation control strategy, to verify the effectiveness and advantages of our proposed control scheme. © 2023 IEEE.

关键词： Sliding mode control

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Robust Robot control Policy Learning Based on Gaussian Mixture Variational Autoencoders

Robust Robot Control Policy Learning Based on Gaussian Mixtu...

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2024 China Automation Congress, CAC 2024

作者： Dong, Qingwei Zeng, Peng Zang, Chuanzhi Wan, Guangxi Dong, Xiaoting Cui, Shijie State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Shenyang University of Technology Shenyang110870 China

ISBN: (纸本)9798350368604

Reinforcement learning methods have demonstrated advanced capabilities in training neural network controllers for specific tasks. In the industrial domain, where model discrepancies and state disturbances occur, robot control strategies must exhibit adaptability. Existing studies, such as Generative Motor Reflexes (GMR), employ Variational Autoencoders (VAE) to approximate the latent variable space distribution to a standard normal distribution. However, this method, while straightforward, offers limited capability in representing data. The Gaussian Mixture Variational Autoencoder (GMVAE) algorithm leverages a Gaussian Mixture Model (GMM) as the prior distribution for data generation, enhancing the representation of the original state distribution. To increase the robustness of the trained neural network controllers against unknown states, we utilize GMVAE to represent the original state space, subsequently learning the mapping from latent variables to actions. We further accelerate the policy search process by integrating state representation learning with Guided Policy Search (GPS) methods. Our method was evaluated on robotic reaching tasks. Experimental results validate the robustness of our method against state perturbations. © 2024 IEEE.

关键词： Normal distribution

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Online Estimation Of Risk in AUV Course Keeping Problem

Online Estimation Of Risk in AUV Course Keeping Problem

引用

Ocean Studies (ICOS), International Conference on

作者： Ivan Lipko Robotics and Intelligent Control Systems Laboratory Sevastopol State University Sevastopol Russian Federation

The article is devoted to the problem of assessing the risk of the presence of an autonomous underwater vehicle in a position limited by a given course angle at every moment of the system's operation. These risky situations arise in the tasks of navigation, docking, tracking objects, receiving and transmitting data in the underwater environment. To perform risk assessments, the most probable trajectories of movement to the threshold values of phase states are used. The most probable trajectories are calculated by solving the minimization problems of the so-called quasipotentials with differential coupling in the form of linear differential equations describing an object and external disturbances acting on it. As a result, we have information about the trajectories of movement, time and probability of events when the object moves along the trajectory. Comparing the current state of the object with known trajectories at each moment of the system operation, an assessment is made of how close the object is to the threshold values. The evaluation results are presented in the form of numerical simulation, which are then aggregated and shown in the form of quality metrics of the forecast of the time remaining until the threshold is crossed. The forecast cannot be carried out for a sufficiently longer amount of time, since the nature of the risk situation already determines the functioning of the object in the danger zone. The estimation of the boundary of this prediction is also shown by simulation using the Monte Carlo method.

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PID Based Path Following Algorithm for the MiddleAUV

PID Based Path Following Algorithm for the MiddleAUV

引用

International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： Ivan Lipko Robotics and Intelligent Control Systems Laboratory Sevastopol State University Sevastopol Russian Federation

The baseline path following algorithm based on PID controller for horizontal movement of the MiddleAUV is constructed. The MiddleAUV is small autonomous underwater vehicle designed to study the control algorithms. This article discusses the one case of PID controllers design and results of numerical simulations. The path is constructed using waypoints. The movement of the AUV between waypoints is accomplished by controlling the distance and yaw between the current position of the AUV and the waypoint. When the AUV approaches a waypoint, a special unit generates a new waypoint and the AUV continues moving. The main difficulty was configuring the controllers for the non-linear version of the AUV model. To solve this problem, we tuned the PID controller for the linear model and chose the best one for the nonlinear model. Numerical experiments were performed on the nonlinear mathematical model, which showed that the resulting PID controllers could be used as the basis for the robot's motion along the waypoints.

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KNTU-RoboGlove: Design of a Pneumatic Soft Robotic Glove 11

KNTU-RoboGlove: Design of a Pneumatic Soft Robotic Glove

引用

11th RSI International Conference on robotics and Mechatronics, ICRoM 2023

作者： Boka, Tanin Nikkhah, M. Navid Moosavian, S. Ali A. Toosi University of Technology Tehran Center of Excellence in Robotics and Control Advanced Robotics and Automated Systems Laboratory Dept of Mechanical Engineering K. N. Tehran Iran K. N. Toosi University of Technology Tehran Dept of Mechanical Engineering Tehran Iran

ISBN: (纸本)9798350308105

This paper introduces an innovative advancement in the field of wearable assistive technology by presenting a revolutionary portable soft robotic glove designed to assist individuals with hand impairments during rehabilitation exercises and daily tasks. The glove offers both active finger flexion and passive extension to aid in hand support and rehabilitative training. It achieves this by utilizing integrated soft actuators, including a variable stiffness layer and a pneumatic-driven layer, which guide specific bending, twisting, and extending movements when air pressure is applied. Notably, the variable stiffness layer takes inspiration from pangolins, whose scales are pliable during daily activities but become rigid when facing threats. To provide increased power with enhanced stiffness, a toothed pneumatic actuator is specially designed. The three-finger soft glove is manufactured by molding super-elastic material, creating the actuator mold using a 3D printer. The glove's grasping abilities have been thoroughly tested, and experimental results demonstrate that, when compared to previously developed elastomeric-based actuators, these actuators can generate substantial force and torque to facilitate finger flexion even at lower air pressure levels. This capability allows the glove to grasp a wide range of objects, assisting individuals in their rehabilitative training. Furthermore, a control system for grasping has been developed, which includes motion processing unit sensors for collecting feedback signals aligned with the actuators, along with a closed-loop controller for regulating the pressure. In contrast to existing devices, this glove, thanks to its portable design, has the potential to significantly enhance user autonomy and independence. © 2023 IEEE.

关键词： Pneumatic actuators

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Development of a Method for Approaching the Autonomous Vehicle to a Moving Object

Development of a Method for Approaching the Autonomous Vehic...

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2024 International Russian Automation Conference, RusAutoCon 2024

作者： Naidanova, Irina Gubankov, Anton Maritime State University Named after Admiral G.I. Nevelskoy Laboratory of Marine Unmanned Aviation and Marine Aviation Systems Vladivostok Russia Institute of Automation and Control Processes Maritime State University Named after Admiral G.I. Nevelskoy Robotics Laboratory Vladivostok Russia

The paper considers the development of a method for approaching an autonomous vehicle to a moving object. Approaching to objects may be necessary in various fields, for example, when landing an unmanned aircraft on a moving platform, animals and marine wildlife photo and videorecording, following the leader in group control, etc. It is assumed that the coordinates of the object are obtained without delay, however, at some points in time, position data is not received, that is, the trajectory of the object is represented by several separate segments of the trajectory. The paper proposes to use a combination of two classical guidance methods to approach the object. This eliminates the disadvantages of both methods and increases the overall efficiency of movement to the object. The pursuit method is used at short distances between the controlled vehicle and the object. The proportional approach method is used at long distances. A comparison of the simulation results on the generated data of the developed algorithm with the results obtained using only classical methods confirms the efficiency of simultaneous use of two guidance methods. © 2024 IEEE.

关键词： Extended Kalman filters

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