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5th IEEE International Conference on Smart systems and Technologies, SST 2022

作者： Stuhne, Dario Vatavuk, Ivo Hrabar, Ivan Vasiljević, Goran Kovačić, Zdenko University of Zagreb Faculty of EE&C Zagreb Croatia University of Zagreb LARICS Laboratory for Robotics and Intelligent Control Systems Faculty of Electrical Engineering and Computing Zagreb10000 Croatia

ISBN: (数字)9781665482158

ISBN: (纸本)9781665482158

The research presented in this paper focuses on the development of fully automated and autonomous vine suckering, a seasonal viticulture activity required to remove all unwanted shoots from the vine trunks. This requires human workers to do a lot of strenuous work, often using only their hands or tools such as clippers or shears. The suckering is done during the early growing season (usually April-May) to provide more energy, water, and nutrients to the main shoots. Commercially available suckering machines appear to be a large-scale solution. However, their performance varies between 10% and 85% and their purely mechanical nature can easily cause damage to the vines. In this paper, we present a concept for autonomous suckering using a mobile robot equipped with a special suckering tool driven by a torque-controlled DC motor that protects the vine from damage. The proposed suckering system was designed, analysed and experimentally validated in a real vineyard. The results showed that the system was able to remove about two thirds of the unwanted shoots, reaching a suckering rate of more than 86 vines per hour. © 2022 IEEE.

关键词： Mobile robots

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Scale-Invariant Specifications for Human-Swarm systems

Field Robotics

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Field robotics 2025年 3卷 368-391页

作者： Joel Meyer Ahalya Prabhakar Allison Pinosky Ian Abraham Annalisa Taylor Millicent Schlafly Katarina Popovich Giovani Diniz Brendan Teich Borislava Simidchieva Shane Clark Todd Murphey The Center for Robotics and Biosystems at Northwestern University École Polytechnique Fédérale de Lausanne (EPFL) The Department of Mechanical Engineering at Yale University Unmanned Laboratory Raytheon BBN Technologies Systems and Technology Research

We present a method for controlling a swarm using its spectral decomposition—that is, by describing the set of trajectories of a swarm in terms of a spatial distribution throughout the operational domain—guaranteeing scale invariance with respect to the number of agents both for computation and for the operator tasked with controlling the swarm. We use ergodic control, decentralized across the network, for implementation. In the DARPA OFFSET program field setting, we test this interface design for the operator using the STOMP interface—the same interface used by Raytheon BBN throughout the duration of the OFFSET program. In these tests, we demonstrate that our approach is scale-invariant—the user specification does not depend on the number of agents; it is persistent—the specification remains active until the user specifies a new command; and it is real-time—the user can interact with and interrupt the swarm at any time. Moreover, we show that the spectral/ergodic specification of swarm behavior degrades gracefully as the number of agents goes down, enabling the operator to maintain the same approach as agents become disabled or are added to the network. We demonstrate the scale invariance and dynamic response of our system in a field-relevant simulator on a variety of tactical scenarios with up to 50 agents. We also demonstrate the dynamic response of our system in the field with a smaller team of agents. Lastly, we make the code for our system available.

关键词： User interfaces Space exploration Hardware Visualization Trajectory Touch sensitive screens Robot sensing systems Real-time systems Graphical models Distribution functions

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Procedural Generation of Synthetic Dataset for Robotic Applications in Sweet Pepper Cultivation 5

Procedural Generation of Synthetic Dataset for Robotic Appli...

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5th IEEE International Conference on Smart systems and Technologies, SST 2022

作者： Vuletić, Jelena Polić, Marsela Orsag, Matko University of Zagreb Faculty of EE&C Zagreb Croatia University of Zagreb LARICS Laboratory for Robotics and Intelligent Control Systems Faculty of Electrical Engineering and Computing Zagreb10000 Croatia

ISBN: (数字)9781665482158

ISBN: (纸本)9781665482158

In this paper, a deep learning based object detection model is developed for robotic applications in structured agricultural cultivation of sweet peppers (Capsicum annuum). We propose a realistic simulation based method for procedural generation of a labeled sweet pepper dataset. Realistic 3D models of fruit, leaves, and other relevant plant body parts are generated through photogrammetry, enabling generation of a large and relevant dataset. The dataset is labeled for both object detection and semantic segmentation by scene rendering in multiple layers and using artificial depth information. Smart augmentation techniques are deployed in procedural generation, as well as label filtering techniques for more realistic dataset labels. A MobileNet-SSD based deep learning model is trained through transfer learning on the generated dataset. The trained model is tested both in simulated environment, as well as on a relevant real-world setup. © 2022 IEEE.

关键词： Object detection

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CPR-Coach: Recognizing Composite Error Actions Based on Single-Class Training

CPR-Coach: Recognizing Composite Error Actions Based on Sing...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Shunli Wang Shuaibing Wang Dingkang Yang Mingcheng Li Haopeng Kuang Xiao Zhao Liuzhen Su Peng Zhai Lihua Zhang Academy for Engineering and Technology Fudan University Cognition and Intelligent Technology Laboratory Engineering Research Center of Al and Robotics Ministry of Education Al and Unmanned Systems Engineering Research Center of Jilin Province

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Fine- grained medical action analysis plays a vital role in improving medical skill training efficiency, but it faces the problems of data and algorithm shortage. Cardiopul-monary Resuscitation (CPR) is an essential skill in emer-gency treatment. Currently, the assessment of CPR skills mainly depends on dummies and trainers, leading to high training costs and low efficiency. For the first time, this pa-per constructs a vision-based system to complete error action recognition and skill assessment in CPR. Specifically, we define 13 types of single-error actions and 74 types of composite error actions during external cardiac compres-sion and then develop a video dataset named CPR-Coach. By taking the CPR-Coach as a benchmark, this paper in-vestigates and compares the performance of existing action recognition models based on different data modalities. To solve the unavoidable “Single-class Training & Multi-class Testing” problem, we propose a human-cognition-inspired framework named ImagineNet to improve the model's multi-error recognition performance under restricted supervision. Extensive comparison and actual deployment experiments verify the effectiveness of the framework. We hope this work could bring new inspiration to the computer vision and medical skills training communities simultaneously. The dataset and the code are publicly available on https://***/Shunli-Wang/CPR-Coach.

关键词： Training Computer vision Image recognition Costs Computational modeling Data models Cognition

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Dual-modal Motion of Magnetically Controlled Soft Microrobots with Environmentally Responsive Reversible Deformation 24

Dual-modal Motion of Magnetically Controlled Soft Microrobot...

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2nd International Conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Xin, Zhengyuan Wu, Anping Bai, Kailun Yang, Haotian Lin, Kaijun Shi, Qing Wang, Huaping Hou, Yaozhen Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing China School of Medical Technology Beijing Institute of Technology Beijing China Science and Technology on Space Physics Laboratory Beijing China School of Mechatronical Engineering Beijing Institute of Technology Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China

ISBN: (纸本)9798400710681

Soft microrobots have garnered attention in the biomedical field due to their ability to access hard-to-reach regions within living organisms for precise manipulation. However, the inability to alter the stable morphology after design and fabrication remains a pressing issue for existing soft microrobots. This limitation results in a fixed range of locomotion modes, thereby restricting the diversity of operational environments. Addressing this challenge, this study proposes and designs a biocompatible X-shaped soft microrobot that achieves alterable stable morphologies through an innovative mechanism. We developed a deformable soft hydrogel robot based on alginate gel’s ability to reversibly swell in response to ion concentration changes. Based on the deformation characteristics of this microrobot, we have designed undulating and rolling locomotion modes that are suitable for different morphologies, along with their corresponding magnetic field manipulation systems, enabling multi-morphology locomotion of the robot. Experimental validations demonstrate that the designed flexible microrobot can achieve a deformation deflection angle of up to 150° under manipulation. This robot not only performs forward undulating locomotion in its expanded state but also executes rolling motion in its spherical configuration, exhibiting high locomotion flexibility and adaptability. © 2024 Copyright held by the owner/author(s).

关键词： Microrobots

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A Humanoid Robot to Assess the Affective Imitative Expression Abilities of Children with Autism Spectrum Disorder

A Humanoid Robot to Assess the Affective Imitative Expressio...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Hao-Yu Ji Bo-Wen Chen Zhi-Yong Wang Han-Wei Ma Rui-Han Lin Yi-Xuan Sheng Hong-Hai Liu State Key Laboratory of Robotics and Systems School of Mechanical Engineering and Automation Harbin Institute of Technology (Shenzhen) Shenzhen China

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social interaction and communication, repetitive behaviors and restricted interests. Assessments and interventions targeting affective expression are crucial for supporting the development of children with ASD. However, existing approaches face challenges related to complex interventions, lack of unified metrics, and inadequate immersion and guidance in providing effective affective assessment. In this paper, an innovative humanoid robot is designed and evaluated to address specific challenges and assess the ability of children with ASD in affective imitative expression. The robot, with its meticulously designed mechanical and hardware structure, incorporates an assessment protocol for affective imitative expression and algorithms for assessing affective expression abilities. It utilizes several specially designed assessment metrics to generate a uniquely tailored assessment scale for affective imitative expression. A preliminary experiment involving ten neurotypical adults has been conducted to validate the feasibility and accuracy of the proposed system. The results demonstrate potential in supporting rehabilitation planning and research for ASD assessments.

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Stereo Visual Localization Dataset Featuring Event Cameras

Stereo Visual Localization Dataset Featuring Event Cameras

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European Conference on Mobile Robots (ECMR)

作者： Antea Hadviger Vlaho-Josip Štironja Igor Cvišić Ivan Marković Sacha Vražić Ivan Petrović Laboratory for Autonomous Systems and Mobile Robotics University of Zagreb Faculty of Electrical Engineering and Computing Croatia Rimac Automobili Croatia

Visual odometry and SLAM methods are facing increasingly complex scenarios and novel solutions are needed to offer more accurate and reliable results in challenging environments. Standard cameras are challenged under low light conditions or very high-speed motion, as they suffer from motion blur and operate at a limited frame rate. These problems can be alleviated by using event cameras - asynchronous visual sensors that offer complementary advantages compared to standard cameras, as they do not suffer from motion blur and support high dynamic range. Although there are a number of existing datasets intended for visual odometry and SLAM that contain event data, most of them are collected using monocular sensors and limited either in terms of camera resolution or ground truth availability. Our work aims to complement this by further supporting the development of robust stereo visual odometry and SLAM algorithms, allowing to exploit both event data and intensity images. We provide both indoor sequences with 6-DoF motion and outdoor vehicle driving sequences that additionally contain 3D lidar data. All sequences contain data from a synchronized high-resolution stereo event and standard cameras, whereas ground truth trajectories are provided by either a motion capture system or a highly accurate GNSS/INS and AHRS that combines the fibre-optic gyro IMU with a dual antenna RTK GNSS receiver.

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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Towards Instance Segmentation-Based Litter Collection with Multi-Rotor Aerial Vehicle

Towards Instance Segmentation-Based Litter Collection with M...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Filip Zoric Antonio Franchi Matko Orsag Zdenko Kovacic Chiara Gabellieri Laboratory for Robotics and Intelligent Control Systems (LARICS) at the Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Robotics and Mechatronics Group from the University of Twente Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

This paper presents a novel aerial robotics application of instance segmentation-based floating litter collection with a multi-rotor aerial vehicle (MRAV). In the scope of the paper, we present a review of the available datasets for litter detection and segmentation. The reviewed datasets are used to train a Mask-RCNN neural network for instance segmentation. The neural network is off-board deployed on an edge computing device and used for litter position estimation. Based on the estimated litter position, we plan a path based on a quadratic Bezier curve for the litter pickup. We compare different trajectory generation methods for the object pickup. The system is verified in a laboratory environment. Eventually, we present practical considerations and improvements necessary to enable autonomous litter collection with MRAV.

关键词： Instance segmentation Reviews Neural networks Laboratories Estimation Trajectory Aircraft

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Learning-based Model Predictive Control for an Autonomous Formula Student Racing Car

Learning-based Model Predictive Control for an Autonomous Fo...

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IEEE International Conference on robotics and Automation (ICRA)

作者： David R. Gomes Miguel Ayala Botto Pedro U. Lima Laboratory of Robotics and Engineering Systems ISR/IST Universidade de Lisboa Portugal IDMEC Instituto Superior Técnico Universidade de Lisboa Portugal

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Advancements in Automated Driving systems (ADSs) have enabled the achievement of a certain level of autonomy while commuting in a car. However, emergency and high-speed maneuvers still arise as significant challenges for ADSs due to the intrinsic nonlinearity and fast-paced behavior of such events. These maneuvers are a distinctive feature within the recently established motorsport discipline of Autonomous Racing (AR). In this work, we explore the use of Learning-based Model Predictive Control (LMPC) to address possible model mismatches of the first principles model in high-speed racing. To this end, a Model Predictive Contouring Control (MPCC) (a specific formulation of the standard Model Predictive Control, MPC) is formulated, and a Neural Network (NN) that leverages the use of Feedforward and Recurrent layers is employed to learn the errors of the first principles model. By combining the NN with the first principles model, the LMPC is born, capable of accurately predicting the future with a computational effort compatible with real-time feasibility, effectively handling the vehicle at its limits. Furthermore, the controller can adapt to changing environments by training the NN during the race. The MPCC (formulation without the NN) is deployed on a real autonomous formula student racing car showing an improvement of 16 % in mean lap times across the same track between a common geometric controller. The LMPC is analyzed in a high-fidelity simulator, achieving an improvement of 8.9 % in mean lap times when compared to the MPCC.

关键词： Training Adaptation models Computational modeling Artificial neural networks Predictive models Real-time systems Automobiles

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