Pub Date : 2025-01-01Epub Date: 2025-07-29DOI: 10.1016/j.ifacol.2025.07.044
Junkai Wang , Ziqiao Zhang , Fumin Zhang
In this paper, we develop a neural network-based method to study opinion behaviors under a covariance-based dissensus algorithm. Driven by this dissensus algorithm, the opinions are updated based on relative interactions and gradually converge to dissensus on the sphere. This proposed neural network-based method samples data and trains a neural network to ensure the Lyapunov conditions, which significantly simplifies the Lyapunov function design for stability analysis. The regions of attraction for different dissensus equilibria can also be estimated under opinion dynamics on a unit sphere by training a neural network to approximate the solution of Zubov’s equation. Simulations demonstrate the performance of the proposed method.
{"title":"Neural Network-based Stability Guarantee for Dissensus Opinion Behaviors on the Sphere⁎","authors":"Junkai Wang , Ziqiao Zhang , Fumin Zhang","doi":"10.1016/j.ifacol.2025.07.044","DOIUrl":"10.1016/j.ifacol.2025.07.044","url":null,"abstract":"<div><div>In this paper, we develop a neural network-based method to study opinion behaviors under a covariance-based dissensus algorithm. Driven by this dissensus algorithm, the opinions are updated based on relative interactions and gradually converge to dissensus on the sphere. This proposed neural network-based method samples data and trains a neural network to ensure the Lyapunov conditions, which significantly simplifies the Lyapunov function design for stability analysis. The regions of attraction for different dissensus equilibria can also be estimated under opinion dynamics on a unit sphere by training a neural network to approximate the solution of Zubov’s equation. Simulations demonstrate the performance of the proposed method.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 4","pages":"Pages 55-60"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a comprehensive investigation into the capability of Large Language Models (LLMs) to successfully complete a semester-long undergraduate control systems course. Through evaluation of 115 course deliverables, we assess LLM performance using ChatGPT under a “minimal effort” protocol that simulates realistic student usage patterns. The investigation employs a rigorous testing methodology across multiple assessment formats, from auto-graded multiple choice questions to complex Python programming tasks and long-form analytical writing. Our analysis provides quantitative insights into AI’s strengths and limitations in handling mathematical formulations, coding challenges, and theoretical concepts in control systems engineering. The LLM achieved a B-grade performance (82.24%), approaching but not exceeding the class average (84.99%), with strongest results in structured assignments and greatest limitations in open-ended projects. The findings inform discussions about course design adaptation in response to AI advancement, moving beyond simple prohibition towards thoughtful integration of these tools in engineering education. Additional materials including syllabus, examination papers, design projects, and example responses can be found at the project website: https://gradegpt.github.io.
{"title":"The Lazy Student’s Dream: ChatGPT Passing an Engineering Course on Its Own","authors":"Gokul Puthumanaillam, Timothy Bretl, Melkior Ornik","doi":"10.1016/j.ifacol.2025.08.049","DOIUrl":"10.1016/j.ifacol.2025.08.049","url":null,"abstract":"<div><div>This paper presents a comprehensive investigation into the capability of Large Language Models (LLMs) to successfully complete a semester-long undergraduate control systems course. Through evaluation of 115 course deliverables, we assess LLM performance using ChatGPT under a “minimal effort” protocol that simulates realistic student usage patterns. The investigation employs a rigorous testing methodology across multiple assessment formats, from auto-graded multiple choice questions to complex Python programming tasks and long-form analytical writing. Our analysis provides quantitative insights into AI’s strengths and limitations in handling mathematical formulations, coding challenges, and theoretical concepts in control systems engineering. The LLM achieved a B-grade performance (82.24%), approaching but not exceeding the class average (84.99%), with strongest results in structured assignments and greatest limitations in open-ended projects. The findings inform discussions about course design adaptation in response to AI advancement, moving beyond simple prohibition towards thoughtful integration of these tools in engineering education. Additional materials including syllabus, examination papers, design projects, and example responses can be found at the project website: <span><span>https://gradegpt.github.io</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 7","pages":"Pages 213-218"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144989757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-09-04DOI: 10.1016/j.ifacol.2025.08.056
Zhen Shen , Hongyu Li , Jing Yang , Xiaojun Wang , Daniel Horti , Martin Ferenc Dömény , Szatmáry Sára , Adina Chotbaeva , Jimei Ma , Zsombor Zrubka , Fei-Yue Wang
This paper presents a novel educational framework that integrates additive manufacturing (3D printing), social manufacturing platforms, large language models (LLMs), iSTREAMS, and iCDIOS to bridge academic learning, industrial practice, and research innovation. The framework adopts a “learning-by-doing” approach, where students engage in project-driven tasks that span 2D-to-3D modeling, material property optimization, and collaborative problem-solving. By leveraging AI tools for design iteration and real-time feedback, students develop competencies in critical thinking, technical execution, and interdisciplinary collaboration. The framework also emphasizes dynamic assessment mechanisms that combine human evaluation with AI-driven analytics to provide personalized feedback and inform curriculum updates. Through this integrated approach, students are equipped with the skills necessary to navigate the complexities of modern Cyber-Physical-Social Systems (CPSS)-driven industries. A case study is provided to demonstrate the framework’s implementation and its potential to foster interdisciplinary control research and curriculum development, highlighting the synergy between iSTREAMS and iCDIOS in enhancing educational outcomes.
{"title":"Interdisciplinary Control Research and Curriculum Development in CPSS: A Case Study with 3D Printing and Social Manufacturing","authors":"Zhen Shen , Hongyu Li , Jing Yang , Xiaojun Wang , Daniel Horti , Martin Ferenc Dömény , Szatmáry Sára , Adina Chotbaeva , Jimei Ma , Zsombor Zrubka , Fei-Yue Wang","doi":"10.1016/j.ifacol.2025.08.056","DOIUrl":"10.1016/j.ifacol.2025.08.056","url":null,"abstract":"<div><div>This paper presents a novel educational framework that integrates additive manufacturing (3D printing), social manufacturing platforms, large language models (LLMs), iSTREAMS, and iCDIOS to bridge academic learning, industrial practice, and research innovation. The framework adopts a “learning-by-doing” approach, where students engage in project-driven tasks that span 2D-to-3D modeling, material property optimization, and collaborative problem-solving. By leveraging AI tools for design iteration and real-time feedback, students develop competencies in critical thinking, technical execution, and interdisciplinary collaboration. The framework also emphasizes dynamic assessment mechanisms that combine human evaluation with AI-driven analytics to provide personalized feedback and inform curriculum updates. Through this integrated approach, students are equipped with the skills necessary to navigate the complexities of modern Cyber-Physical-Social Systems (CPSS)-driven industries. A case study is provided to demonstrate the framework’s implementation and its potential to foster interdisciplinary control research and curriculum development, highlighting the synergy between iSTREAMS and iCDIOS in enhancing educational outcomes.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 7","pages":"Pages 255-260"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144989763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-09-04DOI: 10.1016/j.ifacol.2025.08.023
Gabriel Conde , Luis de la Torre , Raquel Dormido , Sebastián Dormido
Laboratory-based learning plays a crucial role in engineering education, especially in fields like automation and control. However, traditional industrial training labs often face challenges in terms of both realism and accessibility, limiting students’ exposure to real-world systems. In this paper, we present the transformation of a complex industrial process control plant into a fully remote-operable laboratory. By integrating new advanced hardware elements, we have enabled remote monitoring and control of the system. These modifications offer students industry-level realism and hands-on experience in industrial automation, accessible from any location. This work demonstrates the potential of the industrial remote lab to enhance engineering education by providing a flexible and realistic learning environment.
{"title":"Adapting an Industrial Control Laboratory for Remote Access⁎","authors":"Gabriel Conde , Luis de la Torre , Raquel Dormido , Sebastián Dormido","doi":"10.1016/j.ifacol.2025.08.023","DOIUrl":"10.1016/j.ifacol.2025.08.023","url":null,"abstract":"<div><div>Laboratory-based learning plays a crucial role in engineering education, especially in fields like automation and control. However, traditional industrial training labs often face challenges in terms of both realism and accessibility, limiting students’ exposure to real-world systems. In this paper, we present the transformation of a complex industrial process control plant into a fully remote-operable laboratory. By integrating new advanced hardware elements, we have enabled remote monitoring and control of the system. These modifications offer students industry-level realism and hands-on experience in industrial automation, accessible from any location. This work demonstrates the potential of the industrial remote lab to enhance engineering education by providing a flexible and realistic learning environment.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 7","pages":"Pages 60-65"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144989879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-10-21DOI: 10.1016/j.ifacol.2025.10.044
Cédric Join , Emmanuel Delaleau , Michel Fliess
Model predictive control (MPC) is a popular control engineering practice, but requires a sound knowledge of the model. Model-free predictive control (MFPC), a burning issue today, also related to reinforcement learning (RL) in AI, is reformulated here via a linear differential equation with constant coefficients, thanks to a new perspective on optimal control combined with recent advances in the field of model-free control (MFC). It is replacing Dynamic Programming, the Hamilton-Jacobi-Bellman equation, and Pontryagin’s Maximum Principle. The computing burden is low. The implementation is straightforward. Two nonlinear examples, a chemical reactor and a two tank system, are illustrating our approach. A comparison with the HEOL setting, where some expertise of the process model is needed, shows only a slight superiority of the later. A recent identification of the two tank system via a complex ANN architecture might indicate that a full modeling and the corresponding machine learning mechanism are not always necessary neither in control, nor, more generally, in AI.
{"title":"Model-Free Predictive Control: Introductory Algebraic Calculations, and a Comparison with HEOL and ANNs","authors":"Cédric Join , Emmanuel Delaleau , Michel Fliess","doi":"10.1016/j.ifacol.2025.10.044","DOIUrl":"10.1016/j.ifacol.2025.10.044","url":null,"abstract":"<div><div>Model predictive control (MPC) is a popular control engineering practice, but requires a sound knowledge of the model. Model-free predictive control (MFPC), a burning issue today, also related to reinforcement learning (RL) in AI, is reformulated here via a linear differential equation with constant coefficients, thanks to a new perspective on optimal control combined with recent advances in the field of model-free control (MFC). It is replacing Dynamic Programming, the Hamilton-Jacobi-Bellman equation, and Pontryagin’s Maximum Principle. The computing burden is low. The implementation is straightforward. Two nonlinear examples, a chemical reactor and a two tank system, are illustrating our approach. A comparison with the HEOL setting, where some expertise of the process model is needed, shows only a slight superiority of the later. A recent identification of the two tank system via a complex ANN architecture might indicate that a full modeling and the corresponding machine learning mechanism are not always necessary neither in control, nor, more generally, in AI.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 13","pages":"Pages 255-260"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145327295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-12-05DOI: 10.1016/j.ifacol.2025.11.828
Ioannis Panagopoulos , Robert D. McAllister , Simon van Mourik , Tamás Keviczky
This paper introduces a cascaded climate control framework in which a primary economic model predictive controller (EMPC) determines climate bounds for a secondary rule-based controller, based on industrial practice. The proposed controller may therefore serve as a blueprint for control design for existing greenhouse climate control systems while retaining the reliability and safety of legacy systems. The framework’s performance is evaluated through simulations of a lettuce greenhouse model and compared against a state-of-the-art EMPC that controls all actuators directly. The results show that the proposed approach achieves comparable performance to the ideal state-of-the-art EMPC, demonstrating negligible performance loss from retaining rule-based control in the climate control system.
{"title":"A Cascaded Economic Model Predictive Control Approach to Greenhouse Climate Control","authors":"Ioannis Panagopoulos , Robert D. McAllister , Simon van Mourik , Tamás Keviczky","doi":"10.1016/j.ifacol.2025.11.828","DOIUrl":"10.1016/j.ifacol.2025.11.828","url":null,"abstract":"<div><div>This paper introduces a cascaded climate control framework in which a primary economic model predictive controller (EMPC) determines climate bounds for a secondary rule-based controller, based on industrial practice. The proposed controller may therefore serve as a blueprint for control design for existing greenhouse climate control systems while retaining the reliability and safety of legacy systems. The framework’s performance is evaluated through simulations of a lettuce greenhouse model and compared against a state-of-the-art EMPC that controls all actuators directly. The results show that the proposed approach achieves comparable performance to the ideal state-of-the-art EMPC, demonstrating negligible performance loss from retaining rule-based control in the climate control system.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 23","pages":"Pages 443-448"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-11-28DOI: 10.1016/j.ifacol.2025.11.151
Jie Liu , Jizhi Mao , Lidong Zhang
This paper studies the trajectory generation of the air traffic control (ATC) automation system and the trajectory tracking of aircraft, and further realizes the air-ground collaborative operation based on 4D trajectories. Firstly, a ground trajectory generation model based on kinematic and performance models is established. By comparing with real flight data, the time-consuming deviation is 0.08%, which proves that the ground trajectory generation model meets the requirements of ATC. Secondly, a six-degree-of-freedom flight simulation model is established. Through comparison with the simulation of PEP software, the model can stably follow the target trajectory in horizontal position, altitude, and speed, with the rate of climb/descent deviation not exceeding ±5 0 ft/min and the speed deviation not exceeding ±0.5 kt. Finally, taking the route from Beijing to Shanghai as an example, where the ground generates the trajectory and the aircraft follows it in the air. The trajectory tracking error elimination time is set to 8s during the climb and descent phases and 5s during the cruise phase. The simulation results show that the aircraft in the air can stably follow the ground-generated trajectory, and the maximum time difference when passing through waypoints is less than 3s, with an average time difference of 1.6s.
{"title":"Research on Air-Ground Collaborative Operation Based on 4D Trajectory","authors":"Jie Liu , Jizhi Mao , Lidong Zhang","doi":"10.1016/j.ifacol.2025.11.151","DOIUrl":"10.1016/j.ifacol.2025.11.151","url":null,"abstract":"<div><div>This paper studies the trajectory generation of the air traffic control (ATC) automation system and the trajectory tracking of aircraft, and further realizes the air-ground collaborative operation based on 4D trajectories. Firstly, a ground trajectory generation model based on kinematic and performance models is established. By comparing with real flight data, the time-consuming deviation is 0.08%, which proves that the ground trajectory generation model meets the requirements of ATC. Secondly, a six-degree-of-freedom flight simulation model is established. Through comparison with the simulation of PEP software, the model can stably follow the target trajectory in horizontal position, altitude, and speed, with the rate of climb/descent deviation not exceeding ±5 0 ft/min and the speed deviation not exceeding ±0.5 kt. Finally, taking the route from Beijing to Shanghai as an example, where the ground generates the trajectory and the aircraft follows it in the air. The trajectory tracking error elimination time is set to 8s during the climb and descent phases and 5s during the cruise phase. The simulation results show that the aircraft in the air can stably follow the ground-generated trajectory, and the maximum time difference when passing through waypoints is less than 3s, with an average time difference of 1.6s.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 20","pages":"Pages 208-213"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-11-28DOI: 10.1016/j.ifacol.2025.11.168
Fan Zhang , Zexu Zhang , Zhuo Song , Yicheng Mao
Current vision-based spatial pose estimation methods primarily rely on CNNs for feature extraction. However, CNN-based approaches struggle to model the global relationships within an image, leading to suboptimal performance when handling variations in target scale and complex spatial backgrounds. This paper proposes a novel spatial pose estimation method for non-cooperative targets based on a vision transformer. Specifically, we design a keypoint position regression network that utilizes Swin Transformer to extract multi-level features from target images, capturing both global structural information and fine texture details. To adapt to variations in target scale, we fuse feature maps at different resolutions to construct a feature pyramid. Finally, a series of convolutional modules regress the target keypoint positions. By combining the estimated 3D coordinates of keypoints with the EPNP algorithm, we obtain the final target pose. Experimental results demonstrate that the proposed method achieves high-precision pose estimation even under varying target scales and complex background conditions.
{"title":"SwinFPN: A Pose Estimation Method Adapted to Multi-Scale Variations of Space Targets⁎","authors":"Fan Zhang , Zexu Zhang , Zhuo Song , Yicheng Mao","doi":"10.1016/j.ifacol.2025.11.168","DOIUrl":"10.1016/j.ifacol.2025.11.168","url":null,"abstract":"<div><div>Current vision-based spatial pose estimation methods primarily rely on CNNs for feature extraction. However, CNN-based approaches struggle to model the global relationships within an image, leading to suboptimal performance when handling variations in target scale and complex spatial backgrounds. This paper proposes a novel spatial pose estimation method for non-cooperative targets based on a vision transformer. Specifically, we design a keypoint position regression network that utilizes Swin Transformer to extract multi-level features from target images, capturing both global structural information and fine texture details. To adapt to variations in target scale, we fuse feature maps at different resolutions to construct a feature pyramid. Finally, a series of convolutional modules regress the target keypoint positions. By combining the estimated 3D coordinates of keypoints with the EPNP algorithm, we obtain the final target pose. Experimental results demonstrate that the proposed method achieves high-precision pose estimation even under varying target scales and complex background conditions.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 20","pages":"Pages 309-314"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-12-05DOI: 10.1016/j.ifacol.2025.11.816
Marco Bignardi , Nikos Tsoulias , Andreas Heiß , Dimitrios S. Paraforos
Climate change significantly impacts viticulture by harming plant and fruit growth, resulting in lower quality and storage issues. Therefore, there is growing scientific interest in the carbon fluxes of vineyard management activities, including efforts to measure carbon capture and storage in annual biomass. Precision Viticulture and Machine Vision techniques can help assess variations in vines’ biomass, which relate to the vines’ carbon balance. The present study examined the feasibility of a light detection and ranging (LiDAR) for vines’ (Vitis vinifera L. cv. Riesling) annual biomass reconstruction and its role in the annual carbon cycle. The leaves dry weight showed a high correlation coefficient of R² = 0.87 with the LiDAR-based leaf area estimation. Thus, making the proposed sensing system reliable for biomass elemental carbon assessment. Nevertheless, no significant correlation was found for the monitoring of leaf area/fruit ratio. The proposed study showcases the potential and the limits of LiDAR-based vine biomass assessment.
气候变化严重影响葡萄种植，损害植物和果实生长，导致质量下降和储存问题。因此，人们对葡萄园管理活动的碳通量越来越感兴趣，包括测量年生物量中碳捕获和储存的努力。精密葡萄栽培和机器视觉技术可以帮助评估葡萄生物量的变化，这与葡萄的碳平衡有关。本研究探讨了葡萄（Vitis vinifera L. cv）的光探测和测距（LiDAR）的可行性。雷司令)年生物量重建及其在年碳循环中的作用。叶片干重与基于激光雷达的叶面积估算具有较高的相关系数R²= 0.87。因此，使所提出的传感系统可靠的生物质元素碳评估。然而，叶面积/果比的监测没有发现显著的相关性。该研究展示了基于激光雷达的藤本植物生物量评估的潜力和局限性。
{"title":"Evaluation of Vine’s Annual Carbon Stocks by Means of LiDAR-Based 3D Reconstruction","authors":"Marco Bignardi , Nikos Tsoulias , Andreas Heiß , Dimitrios S. Paraforos","doi":"10.1016/j.ifacol.2025.11.816","DOIUrl":"10.1016/j.ifacol.2025.11.816","url":null,"abstract":"<div><div>Climate change significantly impacts viticulture by harming plant and fruit growth, resulting in lower quality and storage issues. Therefore, there is growing scientific interest in the carbon fluxes of vineyard management activities, including efforts to measure carbon capture and storage in annual biomass. Precision Viticulture and Machine Vision techniques can help assess variations in vines’ biomass, which relate to the vines’ carbon balance. The present study examined the feasibility of a light detection and ranging (LiDAR) for vines’ (Vitis vinifera L. cv. Riesling) annual biomass reconstruction and its role in the annual carbon cycle. The leaves dry weight showed a high correlation coefficient of R² = 0.87 with the LiDAR-based leaf area estimation. Thus, making the proposed sensing system reliable for biomass elemental carbon assessment. Nevertheless, no significant correlation was found for the monitoring of leaf area/fruit ratio. The proposed study showcases the potential and the limits of LiDAR-based vine biomass assessment.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 23","pages":"Pages 373-377"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145686014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-09-27DOI: 10.1016/j.ifacol.2025.09.006
Aïcha Leroy , An Caris , Benoît Depaire , Teun van Gils , Kris Braekers
Order picking remains a time-sensitive operation in warehousing, with pickers following predetermined routes. Previous research identified potential drivers for deviations from these routes through qualitative studies or descriptive data analysis. We take a novel approach by applying a statistical analysis on two years of data (i.e., 2 448 000 picks). Our mixed-effects logistic regression model shows that factors such as workload, picks completed, congestion and aisle layout may significantly affect the likelihood of route deviations. Such deviations could significantly reduce route efficiency. These insights highlight the need to integrate real-world dynamics into routing models, aiming to enhance overall efficiency in warehouse operations.
{"title":"Empirical analysis of factors contributing to deviations from routing guidelines in order picking: a case study","authors":"Aïcha Leroy , An Caris , Benoît Depaire , Teun van Gils , Kris Braekers","doi":"10.1016/j.ifacol.2025.09.006","DOIUrl":"10.1016/j.ifacol.2025.09.006","url":null,"abstract":"<div><div>Order picking remains a time-sensitive operation in warehousing, with pickers following predetermined routes. Previous research identified potential drivers for deviations from these routes through qualitative studies or descriptive data analysis. We take a novel approach by applying a statistical analysis on two years of data (i.e., 2 448 000 picks). Our mixed-effects logistic regression model shows that factors such as workload, picks completed, congestion and aisle layout may significantly affect the likelihood of route deviations. Such deviations could significantly reduce route efficiency. These insights highlight the need to integrate real-world dynamics into routing models, aiming to enhance overall efficiency in warehouse operations.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 10","pages":"Pages 25-30"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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