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Digital Futures and Transformative Technologies (ICoDT2), International Conference on

作者： Zaid Mahboob Ayesha Zeb Umar S. Khan College of EME National University of Science and Technology Islamabad Pakistan National Center of Robotics and Automation Pakistan

Accurate tobacco plant detection is crucial for effective agricultural management strategies. Due to the challenges posed by high plant variability, adverse lighting conditions, and occlusions, existing machine learning algorithms and convolutional neural networks (CNNs) have struggled to achieve satisfactory detection accuracy under real-world scenarios. This paper employs YOLOv8, a state-of-the-art object detection algorithm, to reliably detect tobacco plants. A comprehensive evaluation and benchmark of 12 state-of-the-art YOLO object detection algorithms including the YOLOv8, is established for tobacco detection. YOLOv8 demonstrates remarkable test accuracy of 96.4compared to YOLOv5 and YOLOv7. Performance comparisons with YOLOv5 and YOLOv7, two ancestor algorithms, using test images further solidify YOLOv8’s superior detection capabilities for tobacco plants. YOLOv8’s real-time object detection ability renders it an ideal solution for mobile and embedded devices, opening new possibilities for on-the-go agricultural management.

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Base position planning of mobile manipulators for assembly tasks in construction environments

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Advances in Manufacturing 2023年第1期11卷 93-110页

作者： Dai-Jun Xie Ling-Dong Zeng Zhen Xu Shuai Guo Guo-Hua Cui Tao Song Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and AutomationShanghai UniversityShanghai200444People’s Republic of China National Demonstration Center for Experimental Engineering Training Education Shanghai UniversityShanghai200444People’s Republic of China School of Mechanical and Automotive Engineering Shanghai University of Engineering ScienceShanghai201620People’s Republic of China Shanghai Collaborative Innovation Center of Intelligent Manufacturing Robot Technology for Large Components Shanghai201620People’s Republic of China

With good mobility and flexibility,mobile manipulators have shown broad applications in construction *** position(BP)planning,which refers to the robot autonomously determining its working station in the environment,is an important technique for mobile manipulators when performing the construction assembly task,especially in a large-scale construction ***,the BP planning process is tedious and time-consuming for a human worker to carry ***,to improve the efficiency of construction assembly tasks,a novel BP planning method is proposed in this paper,which can lead to appropriate BPs and minimize the number of BPs at the same ***,the feasible BP regions are generated based on the grid division and the variable workspace of the mobile ***,the positioning uncertainties of the mobile manipulator are considered in calculating the preferred BP areas using ***,a set coverage optimization model is established to obtain the minimum number of BPs using an optimization algorithm according to the greedy *** simulated experiment based on a 9-degree of free(DoF)mobile manipulator has been *** results illustrated that the time for BP planning was significantly reduced and the number of BPs was reduced by 63.41%compared to existing manual planning,which demonstrated the effectiveness of the proposed method.

关键词： Base position(BP)planning Mobile manipulators Variable workspace Positioning uncertainties Construction environments

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GPU Accelerated Color Correction and Frame Warping for Real-time Video Stitching

arXiv

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arXiv 2023年

作者： Yang, Lu Kong, Zhenglun Li, Ting Bai, Xinyi Lin, Zhiye Cheng, Hong Center for Robotics University of Electronic Science and Technology of China Chengdu China Center for Robotics School of Automation Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China

Traditional image stitching focuses on a single panorama frame without considering the spatial-temporal consistency in videos. The straightforward image stitching approach will cause temporal flcking and color inconstancy when it is applied to the video stitching task. Besides, inaccurate camera parameters will cause artifacts in the image warping. In this paper, we propose a real-time system to stitch multiple video sequences into a panoramic video, which is based on GPU accelerated color correction and frame warping without accurate camera parameters. We extend the traditional 2D-Matrix (2D-M) color correction approach and a present spatio-temporal 3D-Matrix (3D-M) color correction method for the overlap local regions with online color balancing using a piecewise function on global frames. Furthermore, we use pairwise homography matrices given by coarse camera calibration for global warping followed by accurate local warping based on the optical flow. Experimental results show that our system can generate high-quality panorama videos in real time. Copyright © 2023, The Authors. All rights reserved.

关键词： Real time systems

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Mechanical Properties Inside Origami-Inspired Structures: An Overview

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Applied Mechanics Reviews 2025年第1期77卷 011001页

作者： Yan, Peng Huang, Hailin Meloni, Marco Li, Bing Cai, Jianguo Department of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518052 China Harbin Institute of Technology Shenzhen518052 China Key Laboratory of C & PC Structures of Ministry of Education National Prestress Engineering Research Center Southeast University Nanjing211189 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Harbin Institute of Technology Shenzhen518052 China Key University Laboratory of Mechanism Machine Theory and Intelligent Unmanned Systems of Guangdong Habin Institute of Technology Shenzhen518052 China

In recent decades, origami has transitioned from a traditional art form into a systematic field of scientific inquiry, characterized by attributes such as high foldability, lightweight frameworks, diverse deformation modes, and limited degrees-of-freedom. Despite the abundant literature on smart materials, actuation methods, design principles, and manufacturing techniques, comprehensive reviews focusing on the mechanical properties of origami-inspired structures are relatively rare and unsystematic. This review aims to fill this void by analyzing and summarizing the significant studies conducted on the mechanical properties of origami-inspired structures from 2013 to 2023. We begin with an overview that includes essential definitions of origami, classical origami patterns, and their associated tessellated or stacked structures. Following this, we delve into the principal dynamic modeling method for origami and conduct an in-depth analysis of the key mechanical properties of origami-inspired structures. These properties include tunable stiffness, bistability and multistability, metamechanical properties demonstrated by origami-based metamaterials, and bio-inspired mechanical properties. Finally, we conclude with a comprehensive summary that discusses the current challenges and future directions in the field of origami-inspired structures. Our review provides a thorough synthesis of both the mechanical properties and practical applications of origami-inspired structures, aiming to serve as a reference and stimulate further research. © 2025 American Society of Mechanical Engineers (ASME). All rights reserved.

关键词： Degrees of freedom (mechanics)

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Tendon-Driven Continuum Robot Stiffness with Pretension Effect

Tendon-Driven Continuum Robot Stiffness with Pretension Effe...

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Annual Conference of Industrial Electronics Society

作者： Zhenting Du Weibang Bai ShanghaiTech Automation and Robotics (STAR) Center School of Information Science and Technology ShanghaiTech University Shanghai China

The stiffness analysis of tendon-driven continuum robots is highly demanded in human-robot interaction applications. Although pretension is a common approach to enhance the stiffness of these robots with tendon-displacement-controlled strategies, investigating and analyzing the relationship between stiffness and pretension is challenging. To address this, we proposed a novel Cosserat rod-based model for tendon-driven continuum robots, which takes into account both tendon displacement and tendon elasticity constraints. Through simulation tests, we established the relationship between stiffness analysis and the pretension effect based on the proposed methods.

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Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

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Computers, Materials & Continua 2022年第5期71卷 2285-2302页

作者： Hajira Saleem Faisal Riaz Asadullah Shaikh Khairan Rajab Adel Rajab Muhammad Akram Mana Saleh Al Reshan Control Automotive and Robotics Lab Affiliated lab of National Center of Robotics and Automation(NCRA HEC Pakistan)and with the Department of Computer Science and Information TechnologyMirpur University of Science and Technology(MUST)Mirpur Azad Kashmir10250Pakistan College of Computer Science and Information System Najran UniversityNajran61441Saudi Arabia College of Computer Science and Engineering University of South FloridaTampa33620United States

Deep learning techniques,particularly convolutional neural networks(CNNs),have exhibited remarkable performance in solving visionrelated problems,especially in unpredictable,dynamic,and challenging *** autonomous vehicles,imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of *** this regard,globally,researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best *** has proven the superiority of metaheuristic algorithms over the manual-tuning of ***,to the best of our knowledge,these techniques are yet to be applied to address the problem of imitationlearning-based steering angle ***,in this study,we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters,which are employed to solve the steering angle prediction *** validate the performance of each hyperparameters’set and architectural parameters’set,we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set:optimizer,Adagrad;learning rate,0.0052;and nonlinear activation function,exponential linear *** per our findings,we determined that the deep learning models show better results but require more training epochs and time as compared to shallower *** show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning *** testing was also performed using the model trained with the optimal architecture,which we developed using our approach.

关键词： Bat algorithm convolutional neural network hyperparameters metaheuristic optimization algorithm steering angle prediction

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Backstepping-Based Adaptive Cooperative Control For Multiple Euler-Lagrange Systems Under Unknown Harmonic Disturbances

Backstepping-Based Adaptive Cooperative Control For Multiple...

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2023 China automation Congress, CAC 2023

作者： Chen, Ying Zhao, Zi-Yi Sun, Peng Guo, Xiang-Gui Wang, Jian-Liang Coutinho, Daniel School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing Engineering Research Center of Industrial Spectrum Imaging Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Hangzhou Innovation Institute of Beihang University Collaborative Flying Robotics HangZhou China Universidade Federal de Santa Catarina Department of Automation and Systems Florianópolis Brazil

ISBN: (纸本)9798350303759

This paper studies cooperative control problem for multiple Euler-Lagrange systems with unknown harmonic disturbances, where the disturbances are the superposition of sinusoidal components with unknown amplitude, phase, and frequency. A novel adaptive disturbance observer without requiring the disturbance frequency information is designed to estimate the disturbances. Over the directed network topology, a cooperative control scheme is proposed based on backstepping techniques, so that the tracking error is ultimately uniformly bounded. The scheme developed can be used to reject the harmonic disturbances in a uniform way, which means the characteristics of the disturbances can be described as a fixed parameter vector. Finally, an application is presented to demonstrate the effectiveness of the proposed control strategy in the context of a two-link manipulator system. © 2023 IEEE.

关键词： Backstepping

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Accurate Detection of Facial Landmark Points Using Local Intensity Information for Emotion Detection 4th

Accurate Detection of Facial Landmark Points Using Local Int...

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4th International Conference on Smart Trends for Computing and Communications, SmartCom 2020

作者： Bhattacherjee, Payal Ramya, M.M. Center for Automation and Robotics Hindustan Institute of Technology and Science Padur ChennaiTamil Nadu India

ISBN: (纸本)9789811552236

Face is the gateway to mind. Understanding facial expressions gives the affective state of human mind. Accurate detection of facial expressions is one of the open challenges in machine vision. The fulcrum of facial expression detection and classification depends on extraction of salient points on the face. The accurate detection of these points is a mandatory step for emotion detection as these facial points undergo tremendous change as emotion expressed by human changes. This paper proposes an accurate adaptive algorithm for extraction of the distinct facial points which will make identification of various expressions a feasible task. Viola–Jones face detection algorithm was used for detection of face. The extracted face region was decimated into various regions of interest (ROIs) based on prior knowledge of human face anatomy. Eye centers were located using horizontal and vertical projections of histogram of eye ROI. The ROI for eyebrows was located using the location of eye centers. The eyebrows were detected by taking advantage of the sign of double edges produced by second-order differential used for edge detection. Lip corners were extracted from the bottom half of the face by obtaining the highest contrast points from the contrast image. The algorithm was tested on the UNBC Pain Archive and achieved 100%, 100%, and 99.87% accuracy for eye centers, lip corners, and eyebrow corners, respectively. The algorithm is learning free, computational inexpensive, and can be modeled into embedded platforms. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Emotion Recognition

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Research on Particle Swarm Optimization algorithm incorporating Entropy Weight Method for UAV gas plume tracking subtask

Research on Particle Swarm Optimization algorithm incorporat...

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2022 International Conference on Artificial Intelligence and Computer Information Technology, AICIT 2022

作者： Zeng, Qing-Xue Cheng, Lei Zhong, Jia-Liang Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Eng. Res. Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan China

ISBN: (数字)9781665450874

ISBN: (纸本)9781665450874

Unlike ground robot that performs Gas Source Localization task (GSL) in a two-dimensional plane, UAV can be applied to various three-dimensional space scenarios due to their flexibility. However, due to the interference of wind speed and wind direction, the gas diffusion distribution is uneven, resulting in different confusion degrees in the gas distribution. Based on this, this paper proposes a Particle Swarm optimization algorithm (PSO) incorporating Entropy Weight method (EWM) for UAV gas plume tracking strategy, which referred to as EWM-PSO algorithm below, and the Gaussian gas plume model in three-dimensional space is used to simulate the interference between wind speed and wind direction. As for this algorithm, EWM is used to calculate the entropy value and comprehensive score at each position, PSO uses the comprehensive score as its fitness value, which will guide the UAV move towards the position with high concentration value, i.e., the position of the gas plume source. After setting up two groups of comparative experiments, the results verify that this algorithm has high universality and accuracy. © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Research on Rapid Gas Classification and Incremental Learning Based on Sensor Array

Research on Rapid Gas Classification and Incremental Learnin...

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2022 International Conference on Artificial Intelligence and Computer Information Technology, AICIT 2022

作者： Zhong, Jia-Liang Cheng, Lei Zeng, Qing-Xue Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Eng. Res. Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan China

ISBN: (数字)9781665450874

ISBN: (纸本)9781665450874

In the field of machine olfaction, odor recognition whose focus is to accurately classify odor information and improve efficiency, is one of the important research directions. In recent years, deep learning for gas identification has been used in some researches, which significantly improves the identification accuracy. As a variant of deep learning, incremental learning is widely used in the field of image classification and can effectively solve the problem of catastrophic forgetting in image classification. Incremental learning has not been practically used in gas identification research, this paper will try to use an incremental learning method to identify and classify multiple types of gases. The main objectives of the research are as follows: 1) The experimental data set was divided into two equal parts, a data preprocessing method was used to convert the sensor array data into gas images which would be the input of incremental learning. And the pre-part was trained and classified by the incremental learning network. 2) The latter part of the dataset was taked as a newly emerging gas dataset, which would be incrementally learned and classified by incremental learning model. This study used an open source gas dataset, applied machine learning and deep learning architecture to analyze and compare with the algorithm. The proposed supervised contrastive replay (SCR) was used for data training and classification in this study, achieving a 94.63& classification accuracy, and the training time is only 69.7s. © 2022 IEEE.

关键词： Efficiency

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