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Holistic Construction Automation With Modular Robots: From High-Level Task Specification to Execution

IEEE Transactions on Automation Science and engineering 2025年 22卷 16716-16727页

作者： Jonathan Külz Michael Terzer Marco Magri Andrea Giusti Matthias Althoff Department of Computer Engineering Technical University of Munich Munich Germany Munich Center for Machine Learning (MCML) Munich Germany Robotics and Intelligent Systems Engineering Unit Fraunhofer Italia Research Bolzano Italy

In situ robotic automation in construction is challenging due to constantly changing environments, a shortage of robotic experts, and a lack of standardized frameworks bridging robotics and construction practices. This work proposes a holistic framework for construction task specification, optimization of robot morphology, and mission execution using a mobile modular reconfigurable robot. Users can specify and monitor the desired robot behavior through a graphical interface. In contrast to existing, monolithic solutions, we automatically identify a new task-tailored robot for every task by integrating Building Information Modeling (BIM). Our framework leverages modular robot components that enable the fast adaption of robot hardware to the specific demands of the construction task. Other than previous works on modular robot optimization, we consider multiple competing objectives, which allow us to explicitly model the challenges of real-world transfer, such as calibration errors. We demonstrate our framework in simulation by optimizing robots for drilling and spray painting. Finally, experimental validation demonstrates that our approach robustly enables the autonomous execution of robotic drilling. Note to Practitioners—Construction sites are unpredictable and constantly changing, making automation difficult. To tackle this, we propose a modular robotic system that can adapt to specific tasks. In this work, we address the challenge of automating robotic drilling and introduce a general framework that also supports other construction tasks. We use Building Information Modelling (BIM) data to automatically determine the best robot configuration by optimizing factors such as setup time and robustness to positioning errors. Through an intuitive interface, workers can specify tasks, after which they assemble the robot following the provided instructions. The result of this work is a robotic system that can be deployed on diverse construction sites while requiring only r

关键词： Robots Optimization Service robots Drilling Automation Morphology Robustness Robot control Navigation Calibration

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Remote Aerial Vehicle Solutions for Weed Detection in Precision Agriculture

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IEEE Access 2025年 13卷 101440-101454页

作者： Shekhar S. Borah Aryan Anand Prabha Sundaravadivel Reginald Fletcher Krishna N. Reddy Department of Electrical and Computer Engineering Center for Robotics and Intelligent Systems The University of Texas at Tyler Tyler TX USA Crop Production Systems Research Unit USDA-Agricultural Research Service (ARS) Stoneville MS USA

This study presents a novel Remote Aerial Vehicle-based approach for detecting pigweeds in soybean (Glycine max) fields using a combination of deep learning and advanced image processing techniques. A custom, high-resolution dataset comprising RGB (red, green, and blue) and multispectral images was collected from USDA operated fields and manually annotated for target pigweed detection. Beyond incorporating YOLOv8 variants for real-time weed classification, this study integrates a comprehensive image processing pipeline, incorporating global thresholding, k-means clustering, 3D surface mapping, and spectral signature analysis to enhance interpretability and detection accuracy. A comparative evaluation of YOLOv8 nano, small, medium, and large models, along with Faster R-CNN, confirmed YOLOv8’s balance between detection accuracy and real-time performance for deployment in precision agriculture. The YOLOv8 nano model emerged as the most balanced in terms of precision (75.6%), recall (81.7%), and mAP@0.5 (81.5%), demonstrating effective weed detection performance under real field conditions. In addition, quadrant-level weed coverage and spatial heatmaps were generated to support targeted interventions. This study advances the current state of RAV based weed detection by providing a field-ready, explainable, and resource-efficient solution, contributing to sustainable farming and data-driven weed management practices.

关键词： Crops Deep learning Accuracy Autonomous aerial vehicles Agriculture Real-time systems Feature extraction Soil Precision agriculture Drones

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Accurate modulation of photoprinting under stiffness imaging feedback for engineering ECMs with high-fidelity mechanical properties

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Microsystems & Nanoengineering 2022年第3期8卷 291-301页

作者： Xin Li Huaping Wang Xinyi Dong Qing Shi Tao Sun Shingo Shimoda Qiang Huang Toshio Fukuda Intelligent Robotics Institute School of Mechatronical EngineeringBeijing Institute of TechnologyBeijing 100081China The Key Laboratory of Biomimetic Robots and Systems(Beijing Institute of Technology) Ministry of EducationBeijing 100081China School of Medical Technology Beijing Institute of TechnologyBeijing 100081China Intelligent Behavior Control Collaboration Unit RIKEN Center of Brain Science463-0003 NagoyaJapan

Engineered extracellular matrices(ECMs)that replicate complex in-vivo features have shown great potential in tissue *** hydrogel microstructures have been widely used to replace these native ECMs for physiologically relevant ***,accurate reproduction of the 3D hierarchical and nonuniform mechanical stffness inside one integrated microstructure to mimic the complex mechanical properties of native ECMs presents a major ***,by using digital holographic microscopy(DHM)-based stffness imaging feedback,we propose a novel closed-loop control algorithm to achieve high-accuracy control of mechanical properties for hydrogel microstructures that recapitulate the physiological properties of native ECMs with high *** photoprinting,the photocuring area of the hydrogel is divided into microscale grid areas to locally control the photocuring *** the assistance of a motorized microfluidic channel,the curing thickness is controlled with layer-by-layer *** DHM-based stiffness imaging feedback allows accurate adjustment of the photocuring degree in every grid area to change the crosslinking network density of the hydrogel,thus enabling large-span and high-resolution modulation of mechanical ***,the gelatin methacrylate was used as a typical biomaterial to construct the highfidelity biomimetic *** Young's modulus could be flexibly modulated in the 10 kPa to 50 kPa ***,the modulus gradient was accurately controlled to within 2.9 *** engineering ECM with locally different mechanical properties,cell spreading along the stff areas was observed *** believe that this method can regenerate complex biomimetic ECMs that closely recapitulate in-vivo mechanical properties for further applications in tissue engineering and biomedical research.

关键词： microstructure ECM curing

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Resistorless Grounded Memristor Emulator Using OTA-OTRA for low power edge computing 25

Resistorless Grounded Memristor Emulator Using OTA-OTRA for ...

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Proceedings of the Great Lakes Symposium on VLSI 2025

作者： Shekhar Suman Borah Prabha Sundaravadivel Krishna Reddy Center for Robotics and Intelligent Systems (CeRIS) Department of Electrical and Computer Engineering The University of Texas at Tyler Tyler Texas USA Crop Production Systems Research Unit USDA-Agricultural Research Service Stoneville Mississipi USA

ISBN: (纸本)9798400714962

This paper presents a novel charge-controlled, resistorless memristor emulator configuration based on current-mode building blocks, specifically an Operational Transconductance Amplifier (OTA), an Operational Transresistance Amplifier (OTRA), and a grounded MOS capacitor. The proposed design focuses exclusively on grounded incremental and decremental memristor configurations. It eliminates the need for resistors, analog multipliers, or ADC/DAC components, simplifying the architecture and enhancing its integration potential. The emulator's memristance is electronically tunable through the bias voltage of the OTRA and is accurately modeled as a function of the input signal flux. Simulation results, using Cadence Virtuoso with 0.18 µm CMOS technology, confirm stable pinched hysteresis behavior up to 20 MHz. The design exhibits robust performance under various conditions, including temperature variations, process corners, and a range of capacitor values. The proposed configuration demonstrates low power consumption, measured at just 1.019 mW. Its non-volatile characteristics are validated through pulsed signal testing, confirming its suitability for memory-retaining applications. Also, experimental verification using commercially available ICs supports the simulation outcomes. This grounded memristor emulator offers a practical, energy-efficient, and scalable solution for integration into analog and neuromorphic systems.

关键词： Current-mode circuit

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Automated Food Intake Monitoring System to Prevent Malnutrition Using the Tiago Robot Camera

Automated Food Intake Monitoring System to Prevent Malnutrit...

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2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Konstantakopoulos, Fotios S. Plati, Daphni K. Pliakou, Labrina A. Di Luzio, Francesco Scotto Tagliamonte, Nevio Luigi Zollo, Loredana Tsiknakis, Manolis Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department Greece Forth Biomedical Research Institute IoanninaGR 45110 Greece Campus Bio-Medico University Research Unit of Advanced Robotics and Human-Centred Technologies Rome Italy Forth Institute of Computer Science Crete HeraklionGR 70013 Greece

ISBN: (纸本)9798350383386

The management of a daily diet is a significant concern among individuals in modern culture. The utilization of dietary assessment systems has significantly contributed to the efficient management of malnutrition and dietary habits over a period of time. In order to determine the nutritional value of the food that individuals consume, this study introduces a novel food monitoring system and its architecture. By creating a dataset of food images in a tray and using image processing, machine learning, and computer vision techniques, the nutrients and calories consumed by a patient are calculated. The proposed system achieves a 95.6% Intersection over Union score in the segmentation task, 92.3% top-1 accuracy in the classification task, and 4.6 g mean weight absolute error in the weight estimation *** Relevance: The proposed system has the ability to calculate the nutritional composition, encompassing calories, proteins, fats, and carbs, of food consumed by a patient, making it suitable for nutritional monitoring applications and healthcare systems that aim to monitor malnutrition in hospital patients. © 2023 IEEE.

关键词： Nutrition

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The Smart Hospital: Data and AI Challenges

The Smart Hospital: Data and AI Challenges

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2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Plati, Daphni K. Konstantakopoulos, Fotios S. Kalatzis, Theofanis Manousos, Dimitrios Kassiotis, Thomas Di Luzio, Francesco Scotto Tagliamonte, Nevio Luigi Zollo, Loredana Tsiknakis, Manolis Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department Greece Forth Biomedical Research Institute IoanninaGR 45110 Greece Forth Institute of Computer Science Crete HeraklionGR 70013 Greece Campus Bio-Medico University Research Unit of Advanced Robotics and Human-Centred Technologies Rome Italy

ISBN: (纸本)9798350383386

Healthcare systems generally constitute a combination of healthcare facilities and providers that collaborate in order to offer a complete range of healthcare services. As the primary providers of healthcare, hospitals hold an essential place in the healthcare system. This study introduces three novel automated Artificial Intelligence (AI) systems by using the robotic platform of the TIAGo robot: (i) an image-based system that monitors the food consumption of the patients and provides information regarding their energy intake to prevent malnutrition;(ii) a rehabilitation system that monitors patients as they perform prescribed exercises to prevent loss of mobility, and (iii) a monitoring system for the correctness of the oxygen therapy in terms of the proper positioning of the oxygen mask to prevent hypoxia complications. A well-designed observational clinical study is planned to evaluate and validate the utility and effectiveness of the proposed AI systems in improving patient stay in the hospital. For the abovementioned systems, three novel datasets were *** Relevance: Through the implementation of AI systems, the proposed research intends to provide specialists with an extra tool to aid in the monitoring of hospitalized patients, thus helping to prepare the way for the Smart Hospital. © 2023 IEEE.

关键词： Hospitals

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Unmanned Aerial Vehicle Solutions for Weed Detection in Precision Agriculture

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IEEE Access 2025年

作者： Borah, Shekhar Anand, Aryan Sundaravadivel, Prabha Fletcher, Reginald Reddy, Krishna The University of Texas at Tyler Center for Robotics and Intelligent Systems Dept. of Electrical and Computer Engineering Tyler 75701 TX United States USDA-Agricultural Research Service (ARS) Crop Production Systems Research Unit Stoneville 38776 MS United States

This study presents a novel Unmanned Aerial Vehicle (UAV)-based approach for detecting pigweed in soybean (Glycine max) fields using a combination of deep learning and advanced image processing techniques. A custom, high-resolution dataset comprising RGB and multispectral images was collected from USDA operated fields and manually annotated to target pigweed detection. Beyond incorporating YOLOv8 variants for real-time weed classification, this research integrates a comprehensive image processing pipeline, incorporating global thresholding, k-means clustering, 3D surface mapping, and spectral signature analysis to enhance interpretability and detection accuracy. A comparative evaluation of YOLOv8 nano, small, medium, and large models was performed to identify the most practical model for deployment in precision agriculture. The YOLOv8 nano model emerged as the most balanced in terms of precision (75.6%), recall (81.7%), and mAP@0.5 (81.6%), demonstrating effective weed detection performance under real field conditions. Also, quadrant-level weed coverage and spatial heatmaps were generated to support targeted intervention. This work advances the current state of UAV based weed detection by providing a field-ready, explainable, and resource-efficient solution, contributing to sustainable farming and data-driven weed management practices. © 2013 IEEE.

关键词： Pigweed Detection Soybean Unmanned Aerial Vehicle (UAV) YOLOv8

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Automated Food Intake Monitoring System to Prevent Malnutrition Using the Tiago Robot Camera*

Automated Food Intake Monitoring System to Prevent Malnutrit...

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Data Science and engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Fotios S. Konstantakopoulos Daphni K. Plati Labrina A. Pliakou Francesco Scotto Di Luzio Nevio Luigi Tagliamonte Loredana Zollo Manolis Tsiknakis Dimitrios I. Fotiadis Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department University of Ioannina Biomedical Research Institute FORTH Ioannina GR 45110 Greece Research Unit of Advanced Robotics and Human-Centred Technologies Campus Bio-Medico University Rome Italy Institute of Computer Science FORTH Heraklion Crete GR 70013 Greece

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The Smart Hospital: Data and AI Challenges

The Smart Hospital: Data and AI Challenges

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Data Science and engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Daphni K. Plati Fotios S. Konstantakopoulos Theofanis Kalatzis Dimitrios Manousos Thomas Kassiotis Francesco Scotto Di Luzio Nevio Luigi Tagliamonte Loredana Zollo Manolis Tsiknakis Dimitrios I. Fotiadis Materials Science and Engineering Department Unit of Medical Technology and Intelligent Information Systems University of Ioannina Biomedical Research Institute FORTH Ioannina GR 45110 Greece Institute of Computer Science FORTH Heraklion Crete GR 70013 Greece Research Unit of Advanced Robotics and Human-Centred Technologies Campus Bio-Medico University Rome Italy

Healthcare systems generally constitute a combination of healthcare facilities and providers that collaborate in order to offer a complete range of healthcare services. As the primary providers of healthcare, hospitals hold an essential place in the healthcare system. This study introduces three novel automated Artificial Intelligence (AI) systems by using the robotic platform of the TIAGo robot: (i) an image-based system that monitors the food consumption of the patients and provides information regarding their energy intake to prevent malnutrition; (ii) a rehabilitation system that monitors patients as they perform prescribed exercises to prevent loss of mobility, and (iii) a monitoring system for the correctness of the oxygen therapy in terms of the proper positioning of the oxygen mask to prevent hypoxia complications. A well-designed observational clinical study is planned to evaluate and validate the utility and effectiveness of the proposed AI systems in improving patient stay in the hospital. For the abovementioned systems, three novel datasets were *** Relevance: Through the implementation of AI systems, the proposed research intends to provide specialists with an extra tool to aid in the monitoring of hospitalized patients, thus helping to prepare the way for the Smart Hospital.

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Micro Robotic Manipulation System for the Force Stimulation of Muscle Fiber-like Cell Structure

Micro Robotic Manipulation System for the Force Stimulation ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Xie Chen Qing Shi Shingo Shimoda Tao Sun Huaping Wang Qiang Huang Toshio Fukuda Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing China Intelligent Behavior Control Collaboration Unit RIKEN Center of Brain Science Nagoya Japan Key Laboratory of Biomimetic Robots and Systems Beijing Institute of Technology Ministry of Education Beijing China Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China

Many previous works have facilitated muscle cell (C2C12) alignment to form fiber-like cell structures. However, there still remains a challenge how to induce C2C12 myoblasts in the cell structures to differentiate into matured myocytes to form a functional muscle tissue, while external mechanical stimulation has been proved to have good effects on proliferation and differentiation of myoblasts. In this paper, we proposed a vision-based micro robotic manipulation system to achieve automatic mechanical stimulation for one single muscle fiber-like cell structures (MFCS). A tube, which is attached to a three degree-of-freedom (DOF) manipulator, and a probe are employed to apply the uniaxial mechanical stimulation to train the MFCS. To measure the force applied on MFCS, a vision-based measuring and correction method is utilized, which decrease the error by 74%. Moreover, based on the viscoelastic property of the MFCS, a feedback control algorithm has been applied to compensate for the force loss to realize the force stimulation. And the final value of force remains 699 ± 1μN after 110s experiment.

关键词： Force measurement Force Measurement uncertainty Muscles Manipulators Feedback control Electron tubes

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