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Autonomous Non-communicative and vision-based control Strategy for AGV to Track UAV

Autonomous Non-communicative and Vision-based Control Strate...

引用

international conference on control, automation and Systems ( ICCAS)

作者： Ashok Kumar Sivarathri Amit Shukla School of Mechanical and Materials Engineering Indian Institute of Technology Mandi Himachal Pradesh India Centre for Artificial Intelligence and Robotics Indian Institute of Technology Mandi Himachal Pradesh India

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

UAV-AGV (Unmanned Aerial Vehicle-Autonomous Ground Vehicle) is a suitable system for automation of industrial tasks such as inspection and mapping. vision-based UAV-AGV system (camera attached to UAV or AGV or both) is prominent in GPS inaccessible zones. vision-based tracking motion between both agents is an important aspect during execution of outdoor autonomous missions. It can be either UAV tracking AGV or vice-versa. Former one (UAV tracking the motion of AGV) is well solved. Latter one is less focused as per the literature. But it also required for effective collaboration of the two-agents. For UAV leader and AGV follower roles, AGV needs to continuously track UAV for supporting (payload and tethered charging) longer outdoor missions of UAV. However, existing work in the literature is not suitable for the outdoor missions. Hence, this work presents a non-communicative and vision-based tracking strategy for AGV to track the motion of UAV. AGV contains an on-board sky-facing camera to localize UAV. Deep learning-based technique is implemented for robust localization of UAV using on-board camera of AGV. Sliding mode control technique is implemented to develop tracking controller for AGV. UAV is manually operated with random velocity commands and AGV autonomously tracks the projection of center of UAV on the ground plane. Tracking experiments are performed using quadcopter UAV and non-holonomic AGV mobile platforms. Learning based detection of UAV and tracking controller of AGV are showing satisfactory performance from the experimental results. this shows feasibility to execute outdoor missions without requirement of GPS and also communication between the agents.

关键词： automation Tracking Collaboration Autonomous aerial vehicles Cameras Real-time systems Sliding mode control Global Positioning System Drones Quadrotors

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Semantic Segmentation of Vehicle Steering Column Pattern Image Based on K-Net Network

Semantic Segmentation of Vehicle Steering Column Pattern Ima...

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

作者： Tingxuan Wu Jing Li Nanyan Shen Xiaobo Tang Xin Wang School of Mechatronic Engineering and Automation Shanghai University Shanghai China KOSTAL(Shanghai)Mechatronic Co. Ltd Shanghai China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

As an important assembly part of the automobile, the steering column module is mainly used to control the turn signal and the wiper switch. therefore, it is necessary to check the integrity of its printed beacon pattern. However, the image quality of the traditional image processing vision system is easily affected by the field environment, and due to the existence of a large number of reflection areas in the image, the image segmentation is low precision, low adaptability and high error rate. therefore, this paper uses K-Net deep learning network semantic segmentation model to automatically remove image reflection. By using Swin-T backbone network and introducing self-attention mechanism, the segmentation performance of beacon is further improved, and the anti-reflective binary segmentation of beacon is realized. Finally, the superiority of the above model is verified in the production line data set of enterprise steering column products. the experimental results show that: (1) K-Net network has a good segmentation effect, and its mIoU reaches 95.77%; (2) After using the Swin-T backbone network, the mIoU of K-Net model increased by 1.1% compared with the traditional CNN backbone network.

关键词： Deep learning Adaptation models Semantic segmentation Machine vision Semantics Production Switches Data models Reflection Robots

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A Geometric approach for estimating sideslip angle for Powered Two- Wheeled Vehicles

A Geometric approach for estimating sideslip angle for Power...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Obaida Alrazouk Martin Pryde Amine Chellali Lamri Nehaoua Hichem Arioui Laboratoire IBISC University of Paris-Saclay Evry-Courcouronnes France

ISBN: (纸本)9781665476881

this paper proposes a simple, model-independent method to estimate the sideslip angle of Powered Two-Wheeled Vehicles (P2WV) using knowledge about the road model and an Inertial Measurement Unit (IMU). this proposed model is then tested using simulator software (BikeSim) on different scenarios with different tracks, velocities, and sample rates. Our results indicate the validation of the model. With these promising results, we propose some future applications where this work can be utilized.

关键词： Measurement units automation Roads Inertial navigation Software Safety Vehicle dynamics

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Distributed Estimation of Sensor Networks Based on LSTM-Kalman Filter*

Distributed Estimation of Sensor Networks Based on LSTM-Kalm...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Si-peng Kuang Ya Zhang Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing P.R. China

ISBN: (纸本)9781665476881

this paper studies the distributed estimation problem of sensor networks where the noise parameters are unknown and some sensors cannot obtain the measurements. the expectation maximization (EM) algorithm integrating Kalman filtering algorithm, which is called the EM-KF algorithm, is adopted to sensor networks. Simulation examples are given to illustrate the effectiveness of the algorithm. By using LSTM network, and LSTM-KF algorithm is further proposed to improve the accuracy of EM-KF algorithm.

关键词： automation Estimation Process control Filtering algorithms Robot sensing systems Noise measurement Kalman filters

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

2021 IEEE 11th Annual International Conference on CYBER Tech...

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

ISBN: (纸本)9781665425278

the proceedings contain 164 papers. the topics discussed include: the cubic B-spline trajectories with the boundary conditions of null velocities and accelerations;vision-based autonomous perceiving and planning system of a 7DOF robotic manipulator;ultra-short term power prediction of wind farm based on EEMDSE-GGRU;effect of high speed grinding on surface integrity of cycloid gear;prescribed performance-based chattering-free tracking control for pneumatic muscle actuators;visual detection of cells in brain tissue slice for patch clamp system;and a fast and energy-saving neural network inference method for fault diagnosis of industrial equipment based on edge-end collaboration.

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6D Pose Estimation for vision-guided Robot Grasping Based on Monocular Camera

6D Pose Estimation for Vision-guided Robot Grasping Based on...

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robotics, control and automation Engineering (RCAE), international conference on

作者： Shiman Wang Jianxin Liu Qiang Lu Zhen Liu Yongjie Zeng Dechang Zhang BO Chen Xihua University Chengdu China Nagasaki Institute of Applied Science Nagasaki Japan

Enhancing the intelligence of grasping operations through visual empowerment is currently a hot research topic in the field of robotics, where pose estimation is an important component. However, existing methods that solely rely on RGB images mostly employ direct methods or indirect methods using two-stage/multistage network predictions. Estimating the translation and rotation components of pose solely through a single network model does not yield optimal solutions. In this paper, different networks were used to predict the translation and rotation components, and convolutional block attention module (CBAM) and Pyramid Pooling Module (PPM) were introduced to address the issues that features decay in the backbone residual network after multiple layers of convolution and the insufficient utilization of feature information. Furthermore, in the rotation prediction branch, the problem of inaccurate recognition caused by weakened details after multiple layers of convolution was addressed by incorporating pyramid pooling into the network to capture richer detail and contextual information. Experimental tests were conducted on the publicly available Linemod dataset and in real environments. the results demonstrate that the proposed network exhibits a significant improvement of 2.76% in the most common ADD metric compared to a coordinate-based disentangled pose network (CDPN). It also achieves a 1 % improvement in the 5°, 5cm metrics and comparable performance in the Proj.2D metric with a 0.2% increase.

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Forward Kinematics and Natural Frequency Analysis of the Upgraded Feed Cabin in FAST 8

Forward Kinematics and Natural Frequency Analysis of the Upg...

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8th IEEE international conference on Advanced robotics and Mechatronics, ICARM 2023

作者： Gao, Xinyu Zhang, Bin Li, Zhaoyang Wang, Xiaoyan Zhou, Yu Zhang, Fei Ma, Yifan Li, Hui Yao, Rui Yin, Jia-Ning Yang, Qingge Li, Qingwei Shang, Weiwei University of Science and Technology of China Department of Automation Hefei230027 China CAS Key Laboratory of FAST National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China

ISBN: (纸本)9798350300178

the five-hundred-meter aperture spherical radio telescope (FAST) is one of the most successful cable-driven parallel robots. the current structure of the feed cabin is very complicated and its total weight is more than 30t, which makes the high-precision pose control over a large span challenging. What's worse, such heavy structure greatly limits the scope of FAST and makes the galactic center unobservable. Aiming at reducing the total weight, we propose an upgraded feed cabin, where a much lighter 6-cable parallel mechanism is used to replace the current heavy components. then, a preliminary analysis of the forward kinematics and natural frequencies of the newly-designed 6-cable mechanism is conducted in this paper. First, a novel forward kinematic algorithm is proposed based on the increment model, which can reduce the complexity of the kinematic model and realize an accurate and real-time solution. Second, by deliberating the special structure of the 6-cable mechanism, the approximate vibration equation is established and the natural frequencies are then analyzed across the whole workspace. the results indicate that the new design has a high enough natural frequency to resist low-frequency disturbances and will not resonate with the outer cable system in FAST. All the analyses verify that the lighter-weight upgraded feed cabin can feasibly replace the current heavy one, which makes it possible for FAST to realize a larger observation range, including the galactic center, in the future. © 2023 IEEE.

关键词： Natural frequencies

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Adaptive control for a class of switched nonlinear systems via event-triggered output feedback

Adaptive control for a class of switched nonlinear systems v...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Manman Yuan Junyong Zhai School of Automation Southeast University Nanjing Jiangsu China

ISBN: (纸本)9781665476881

this paper studies the problem of dynamic event-triggered (ET) output feedback (OF) control for switched nonlinear systems (SNSs) with uncertain output function. the considered system allows more general growth restriction. Firstly, to deal with the system uncertainties, a homogeneous observer embedded with a dynamic gain is put forward. then, an adaptive control scheme under arbitrary switching is presented. It is worth emphasizing that the threshold parameters of the used ET mechanism can be dynamically adjusted. Besides, it is proven that all signals of the closed-loop system are bounded. the effectiveness of the presented method is verified by an illustrative example.

关键词： Uncertainty automation Switches Observers Nonlinear dynamical systems Closed loop systems Adaptive control

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Lidar-only 3D SLAM System Comparative Study

Lidar-only 3D SLAM System Comparative Study

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international conference on control, automation, robotics and vision (ICARCV)

作者： Wenhu Ren Xueyuan Li Mengkai Li Qi Liu Zirui Li School of Mechanical Engineering Beijing Institute of Technology Beijing China

ISBN: (纸本)9781665476881

Simultaneous localization and mapping (SLAM) is an attractive and hot research topic in computer vision, robotics, and artificial intelligence. Autonomous vehicles driving in unknown environments try to perceive and map the surrounding environment while recognizing their location and trajectory. In this paper, five state-of-the-art open-source 3D lidar-only SLAM algorithms are reviewed: LOAM, LeGO-LOAM, F-LOAM, BALM, and MULLS. We briefly introduce the characteristics of these algorithms. Finally, the experimental comparison is carried out to compare the absolute pose error (APE), efficiency, and operation memory occupation of each algorithm.

关键词： Simultaneous localization and mapping three-dimensional displays Measurement units Laser radar Robot vision systems Memory management Inertial navigation

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A fault detection method for AUV based on multi-scale spatiotemporal feature fusion 5

A fault detection method for AUV based on multi-scale spatio...

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5th international conference on Computer Information Science and Artificial Intelligence, CISAI 2022

作者： Xia, Shaoxuan Zhou, Xiaofeng Shi, Haibo Li, Shuai Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation 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

Autonomous underwater vehicles (AUVs) are important equipment for ocean development and exploration. To ensure the task implementation of AUV, faults shall be detected in time. We propose a fault detection method based on multi-scale spatiotemporal feature fusion (MSF) for the time-varying characteristics and multiple correlation characteristics of AUV monitoring data. First, we apply a variety of sampling and data processing methods to generate monitoring windows with different scales along the time axis. then, a composite feature extraction method is proposed to obtain temporal and spatial features simultaneously, and a feature pyramid of temporal and spatial information is formed. We use bidirectional long short-term memory (BiLSTM) to obtain the time-series characteristics of a single monitoring variable, and convolutional neural networks (CNN) to obtain the implicit spatial relationship characteristics among multiple monitoring variables. Next, we use an adaptive feature fusion method to solve the inconsistency in different feature scales, which can adaptively suppress the possible conflict information of different scale features. Finally, we use a fully connected network to detect the fault of the fused features. the fault detection experiment of Haizhe AUV shows the effectiveness and superiority of the proposed method. © 2023 SPIE.

关键词： Autonomous underwater vehicles

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