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12th RSI International Conference on robotics and Mechatronics, ICRoM 2024

作者： Khaloujini, Amirmasood Talaeizadeh, Amin Taheri, Alireza Alastya, Aria Advanced Research Lab for Control and Agricultural Robotics Sharif University of Technology Social and Cognitive Robotics Lab. Department of Mechanical Engineering Tehran Iran

ISBN: (纸本)9798331529734

Optimal route planning for autonomous robots is a crucial element of contemporary agriculture, especially for activities like spraying and pollination. This paper presents a Graph Convolutional Network (GCN) model especially developed to address the Coverage Path Planning (CPP). The aim of this paper is to convert the CPP problem into a Traveling Salesman Problem (TSP), so that by solving the TSP using the proposed neural network, an optimal route can be extracted for each agricultural field in the shortest possible time and with sufficient accuracy, ensuring full coverage of the field's area. The proposed model is a GCN, augmented with a transformer layer and a Multi-Layer Perceptron (MLP), was trained using datasets produced by a Genetic Algorithm (GA) Method. The model obtained a network error of 0.12 while solving TSP cases with 20 points. It showed robust generalization skills, as evidenced by validation and test errors of 0.1261 and 0.1264, respectively. Significantly, the GCN model reduces the solution time to approximately one-tenth compared to conventional approaches, a benefit that increases as the number of points increases. The effectiveness of the model in producing ideal coverage routes highlights its potential for wider use in optimization and coverage path planning, especially in dynamic and extensive agricultural environments. © 2024 IEEE.

关键词： Traveling salesman problem

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Transparency-Based Action (TBA) Model for a Simulated Teleoperated Mobile Robot 16th

Transparency-Based Action (TBA) Model for a Simulated Teleop...

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16th International Conference on Social robotics, ICSR + AI 2024

作者： Aharony, Naama Arie, Tal Oron-Gilad, Tal Edan, Yael Department of Industrial Engineering and Management Agricultural Biological Cognitive Robotics Initiative Ben-Gurion University of the Negev Beer-Sheva Israel

ISBN: (纸本)9789819635214

This study expands an existing Transparency-Based Action (TBA) model to the domain of variable autonomy for teleoperated robot navigation. A simulation of a teleoperated robot equipped with two Levels of Automation (LOAs): Low teleoperation and High-conditional navigation, was developed and evaluated for a dynamic office environment. Participants engaged in a navigation task under two switch control modes: robot-initiated (RI) and human-initiated (HI). The study implemented two TBA levels, High and Low, to assess their impact on task performance and participant perception. The findings revealed that the High TBA level improved task performance and user acceptance in the RI control mode, whereas minor differences were observed in the HI mode. Results suggest that higher transparency levels enhance performance and perception in robot-initiated control situations but are less critical in human-initiated control. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Human robot interaction

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Investigating the Proxemics Shape in Social Navigation: An Exploratory User Study 16th

Investigating the Proxemics Shape in Social Navigation: An E...

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16th International Conference on Social robotics, ICSR + AI 2024

作者： Nahum, Ehud Edan, Yael Oron-Gilad, Tal Department of Industrial Engineering and Management Agricultural Biological Cognitive Robotics Initiative Ben-Gurion University of the Negev Beer-Sheva Israel

ISBN: (纸本)9789819635214

Proxemics is a crucial aspect of social robot navigation that studies how people utilize the physical space around them and position themselves relative to others. This exploratory user study looked at the interaction distances users preferred in human-robot interaction with a mobile teleoperated robot;the distances were utilized to design appropriate proxemics shapes. A within-the-group experimental design was conducted with four independent variables: two positions (sitting/standing) and two approach directions (front/back);each user experienced all four trials. Results indicate that the proxemics distance is not affected by the participant's position but by the robot’s approach direction. Participants required more distance from the robot when approaching them from the front. The outcome distances combined with findings from the literature led to the formation of an asymmetric proxemics shape with higher distances than Hall’s [1] traditional circular shape and distance zones, representing real-world interactions and distance preferences in human-robot encounters. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Machine design

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Corn Pest Identification Based on Improved VGGNet Model 5

Corn Pest Identification Based on Improved VGGNet Model

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Luo, Jingsen Zhang, Chunlong College of Engineering China Agricultural University Beijing China Research Center For Intelligent Robotics Zhejiang Lab Hangzhou China

ISBN: (纸本)9798350385557

Corn as an important food crop in China, with good drought resistance, cold resistance, barrenness, by China's large-scale planting, its planting area and total output is second only to wheat and rice ranked third in the country. In addition to corn as people's food, but also food, health care, chemical industry and other indispensable raw materials, national food security is of great significance. In recent years, the frequent occurrence of corn diseases has had a great impact on corn yield, in response to this situation, this paper takes the common corn grey spot, corn rust and corn leaf spot, which are the three most common diseases of corn, as the object of research, and based on the classical serial convolutional neural network structure of VGG Net, we designed a variety of corn leaf disease-aware convolutional neural network suitable for embedded systems, using the data The model training results were optimised using data enhancement and the addition of a Dorpout layer. After experimental testing, the accuracy of the model in the test set reaches 95.93%, which reduces the model prediction time by nearly 5 times compared to the traditional VGGNet with similar recognition accuracy, and improves the recognition accuracy by 4.74% compared to AlexNet. On the embedded device Raspberry Pi 4b, the average predicted frame rate of video transmitted in real time for a single camera is up to 450 frames, which meets the requirement of real-time detection in agricultural production and can be deployed on precision application equipment in large fields. This study provides new ideas for deep learning network lightweight research and deployment of neural networks on embedded devices. © 2024 IEEE.

关键词： Convolutional neural networks

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Model predictive control and replanning for magnetic microrobots in fluid environments

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Science China(Technological Sciences) 2025年第5期68卷 138-150页

作者： Junjian ZHOU Mengyue LI Na LI Huanyu ZHOU Xiaodong WANG Jingyi WANG Lianqing LIU Niandong JIAO State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China University of Chinese Academy of Sciences Beijing 100049China College of Information and Electrical Engineering Shenyang Agricultural UniversityShenyang 110866China

Magnetic microrobots,given their unique characteristics,hold great potential in biomedical applications such as targeted therapy and microscale operations and are receiving widespread *** on the autonomous navigation of magnetic microrobots is highly focused,as it is an essential prerequisite for achieving functions such as targeted delivery in medical *** success of autonomous navigation determines the level of intelligence and precision in the motion of magnetic ***,uncertainties stemming from environmental changes and time-varying disturbances in electromagnetic systems adversely affect the control accuracy of magnetic ***,the random appearance of dynamic obstacles along expected trajectories challenges their autonomous *** this study,we demonstrate a method for the exact autonomous navigation of magnetic microrobots in fluid environments,successfully avoiding dynamic obstacles that suddenly appear in predefined *** versions of the A^(*) algorithm and dynamic window approach are integrated as path planners,that can generate smooth and collision-free trajectories that adhere to kinematic constraints in fluid environments with dynamic obstacles.A learning-based model predictive control strategy is employed,where radial basis function neural networks are used to effectively predict and compensate for fluid disturbances and inevitable errors introduced by electromagnetic system coupling,thereby ensuring the control accuracy of the magnetic microrobot in a flowing *** in a constructed microfluidic environment validate the effectiveness of our navigation approach in motion control,autonomous navigation,and replanning,with an average error of less than 8%of the body length of the microrobot.

关键词： magnetic microrobot model predictive control path planning radial basis function neural network

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Structure design and rice threshing performance of the variable-speed inertial pulley for simulating artificial threshing

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International Journal of agricultural and Biological engineering 2024年第1期17卷 33-40页

作者： Tiantian Jing Zhong Tang Shuaihua Hao Cheng Shen Ting Wang Meilin Wang School of Agricultural Engineering Jiangsu UniversityZhenjiang 212013JiangsuChina Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province Hangzhou 310058China College of Engineering China Agricultural UniversityBeijing 100083China Nanjing Institute of Agricultural Mechanization Ministry of Agriculture and Rural AffairsNanjing 210014China Jiangsu World Agriculture Machinery Co. Ltd(WORLD A/M)Zhenjiang 212311JiangsuChina

Due to the small models and compact structures,small harvesters have also caused problems such as poor threshing separation performance and large loss *** order to solve unstable cleaning effects of small harvesters when they facing different working conditions,this study selected rice plants in hilly areas for the *** breaking force of different parts of mature rice was tested,which revealed the fracture mechanism of each part under the combined *** threshing method was used to simulate artificial plate bin and design three kinds of non-circular pulley variable speed transmission threshing *** the help of transient inertia force,threshing force was *** paper tested the harvesting performance of the variable speed threshing device with the help of the harvest performance *** show when facing the small rice planting area,the T/2 variable speed threshing device has better cleaning performance,and also the harvest loss rate of T/4 variable speed threshing device is the *** with the constant speed threshing device,the impurity content rate of the variable speed threshing device is increased by 0.64%to 8.76%;the loss rate is reduced by 0.45%to 1.79%,which provides a basis for the optimization design of small combine harvester in hilly areas.

关键词： small harvesters fracture mechanism transient inertia force non-circular pulley

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Enhancing citrus surface defects detection:A priori feature guided semantic segmentation model

Artificial Intelligence in Agriculture

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Artificial Intelligence in Agriculture 2025年第1期15卷 67-78页

作者： Xufeng Xu Tao Xu Zichao Wei Zetong Li Yafei Wang Xiuqin Rao College of Biosystems Engineering and Food Science Zhejiang University866 Yuhangtang RoadHangzhou 310058China Key Laboratory of On Site Processing Equipment for Agricultural Products Ministry of AgricultureHangzhou 310058China Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province Hangzhou 310058China Zhejiang Agricultural Machinery Industry Association Hangzhou 310007China

The accurate detection of citrus surface defects is of great importance for elevating the product quality and augmenting its market ***,due to defect diversity and complexity,existing methods focused on parameter and data enhancement have limitations in detection and ***,this study proposed a citrus surface defect segmentation model guided by prior features,named *** model extracted texture features,boundary features,and superpixel features that were crucial for defect detection and segmentation,as priori features.A Priori Feature Fusion Module(PFFM)was designed to integrate the priori features,thereby establishing a priori feature *** the priori feature branch was integrated into the baseline model SegFormer,with the objective of enhancing key feature learning capacity of the ***,the effectiveness of the priori features in enhancing the performance of the model was demonstrated through the implementation of specific *** result showed that PrioriFormer achieved an mPA(mean Pixel Accuracy),mIoU(mean Intersection over Union),and Dice Coefficient of 91.0%,85.8%,and 91.0%,*** to other semantic segmentation models,the proposed model has achieved the best *** model parameters of PrioriFormer have only increase by 2.7%in comparison to the baseline model,while the mIoU has improved by 3.3%,indicating that the improvement of segmentation performance had less dependence on model *** when trained on few data,PrioriFormer maintained the high segmentation performance,with the reduction of mIoU not exceeding 4.2%.This demonstrated the strong feature learning ability of the model in scenarios with limited ***,validation on external datasets confirmed PrioriFormer's superior performance and adaptability to different *** study found that the proposed PrioriFomer guided by priori features can effectively enhance the accuracy of the citrus surface defect

关键词： Citrus surface defect Semantic segmentation Deep learning Priori feature Quality assessment

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A Survey of Underwater Multi-Robot Systems

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 1-18页

作者： Ziye Zhou Jincun Liu Junzhi Yu State Key Laboratory for Turbulence and Complex System Department of Advanced Manufacturing and RoboticsBeijing Innovation Center for Engineering Science and Advanced TechnologyCollege of EngineeringPeking UniversityBeijing 100871China College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China

As a cross-cutting field between ocean development and multi-robot system(MRS),the underwater multi-robot system(UMRS)has gained increasing attention from researchers and engineers in recent *** this paper,we present a comprehensive survey of cooperation issues,one of the key components of UMRS,from the perspective of the emergence of new *** specifically,we categorize the cooperation in terms of task-space,motion-space,measurement-space,as well as their ***,we analyze the architecture of UMRS from three aspects,i.e.,the performance of the individual underwater robot,the new functions of underwater robots,and the technical approaches of *** conclude,we have discussed related promising directions for future *** survey provides valuable insight into the reasonable utilization of UMRS to attain diverse underwater tasks in complex ocean application scenarios.

关键词： Cooperation formation control multi-robot systems(MRS) taxonomy underwater robots underwater tasks

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A Deeping-Learning-Based Foreign Object Inspection System Design for Overhead Power Transmission Lines 8

A Deeping-Learning-Based Foreign Object Inspection System De...

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8th IEEE International Conference on Advanced robotics and Mechatronics, ICARM 2023

作者： Yue, Xiang Feng, Yan Qi, Kai Zhang, Yang Song, Yifeng Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110866 China College of Engineering Shenyang Agricultural University Shenyang110866 China

ISBN: (纸本)9798350300178

Overhead power transmission lines are widely used to transmit electrical energy for generation plants to distribution substations. Foreign objects such as kites, kite string or balloons may be accidently twisted on the power lines, which may lead to power outages if there are not removed. This paper proposed a foreign object inspection system based on deep learning for power transmission lines to overcome the shortages such as poor operation efficiency, high cost and time consuming of ordinary inspection methods. The system is consisted of inspection equipment and ground control station. The inspection equipment travels on the ground line of power transmission lines, and the deep learning algorithm is embedded in the software installed on the computer of the ground station, which can be used to control the inspection equipment remotely. The YOLOv3 algorithm is used to detect the foreign object, and the model is trained and tested in the field. Experimental results show the inspection system can inspect the lines semi-autonomously and the algorithm is capable of inspecting the foreign objects with 25fps in speed and 84% in accuracy, which helps in regular inspection task of the power transmission lines. © 2023 IEEE.

关键词： Learning algorithms

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Bioinspired fabrication of graphene/PDMS composite materials for high-performance flexible pressure sensor

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Science China Materials 2025年第4期68卷 1184-1195页

作者： Dandan Xu Peilin Zhou Haibo Yu Jianhang Chen Ya Zhong Hongji Guo Xiuli Zhang Yueqing Xia Guangrui Xiang Lianqing Liu College of Mechanical and Electrical Engineering Henan Agricultural UniversityZhengzhou 450002China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110o16China

Flexible pressure sensors show significant potential for applications in the fields of intelligent wearable devices,electronic skins,and health ***,the fast response saturation and high viscoelasticity of the flexible sensing materials often result in reduced sensitivity and increased response hysteresis,limiting the practical application of these ***,achieving flexible pressure sensors with both high sensitivity and wide detection range still remains great *** this study,bioinspired by the forcesensitive sensing mechanism and physiological structure of human skin,we propose a low-cost flexible fabrication method for high-performance piezoresistive flexible pressure sensor based on graphene/polydimethylsiloxane(PDMS)composite *** results show that the sensor has an ultra-high sensitivity(321 kPa^(-1)),wide detection range(0.01-1000 kPa),fast response time(29 ms),and exhibits stability over 5000 *** addition,the successful detections and applications indicate the wide application prospect of the developed sensor in fields of health monitoring,human-machine interactions and intelligent robotic perception.

关键词： bioinspired flexible pressure sensor graphene ultrahigh sensitivity wide detection range

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