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IEEE International Conference on robotics and Automation (ICRA)

作者： Tom P. Huck Yuvaraj Selvaraj Constantin Cronrath Christoph Ledermann Martin Fabian Bengt Lennartson Torsten Kröger Intelligent Process Automation and Robotics Lab Institute of Anthropomatics and Robotics (IAR-IPR) Karlsruhe Institute of Technology India Department of Electrical Engineering Division of Systems and Control Chalmers University of Technology Gothenburg Sweden

Safety critical systems are typically subjected to hazard analysis before commissioning to identify and analyse potentially hazardous system states that may arise during operation. Currently, hazard analysis is mainly based on human reasoning, past experiences, and simple tools such as checklists and spreadsheets. Increasing system complexity makes such approaches decreasingly suitable. Furthermore, testing-based hazard analysis is often not suitable due to high costs or dangers of physical faults. A remedy for this are model-based hazard analysis methods, which either rely on formal models or on simulation models, each with their own benefits and drawbacks. This paper proposes a two-layer approach that combines the benefits of exhaustive analysis using formal methods with detailed analysis using simulation. Unsafe behaviours that lead to unsafe states are first synthesised from a formal model of the system using Supervisory Control Theory. The result is then input to the simulation where detailed analyses using domain-specific risk metrics are performed. Though the presented approach is generally applicable, this paper demonstrates the benefits of the approach on an industrial human-robot collaboration system.

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Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning

arXiv

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arXiv 2024年

作者： Salazar, Teresa Gama, João Araujo, Helder Abreu, Pedro Henriques The Centre for Informatics and Systems Department of Informatics Engineering University of Coimbra Coimbra3030-790 Portugal INESC TEC The Faculty of Economy University of Porto Porto4200-465 Portugal The Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra3004-531 Portugal

In the evolving field of machine learning, ensuring fairness has become a critical concern, prompting the development of algorithms designed to mitigate bias in decision-making processes. However, achieving fairness in the presence of group-specific concept drift remains an unexplored frontier, and our research represents pioneering efforts in this regard. Group-specific concept drift refers to situations where one group experiences concept drift over time while another does not, leading to a decrease in fairness even if accuracy remains fairly stable. Within the framework of Federated Learning, where clients collaboratively train models, its distributed nature further amplifies these challenges since each client can experience group-specific concept drift independently while still sharing the same underlying concept, creating a complex and dynamic environment for maintaining fairness. The most significant contribution of our research is the formalization and introduction of the problem of group-specific concept drift and its distributed counterpart, shedding light on its critical importance in the realm of fairness. Additionally, leveraging insights from prior research, we adapt an existing distributed concept drift adaptation algorithm to tackle group-specific distributed concept drift which uses a multi-model approach, a local group-specific drift detection mechanism, and continuous clustering of models over time. The findings from our experiments highlight the importance of addressing group-specific concept drift and its distributed counterpart to advance fairness in machine *** Codes 68T01 © 2024, CC BY.

关键词： Machine learning

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Adaptive Fuzzy generalized Predictive Control of pH in Tubular Photobioreactors on Microalgae Plant

Adaptive Fuzzy Generalized Predictive Control of pH in Tubul...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Mafalda Amaro Jérôme Mendes Tiago Matias Rui Araújo Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Coimbra Portugal Department of Mechanical Engineering CEMMPRE ARISE University of Coimbra Coimbra Portugal Oncontrol Technologies Coimbra Portugal

Culture conditions in a photobioreactor on a microalgae plant are essential for production rates, and the pH value is one of the critical variables for a harmonizing process. However, the biological nature of microalgae growth represents a complex reaction that is arduous to model and control. This work proposes an approach of the application of adaptive identification and predictive control to regulate the pH in a photobioreactor in a microalgae production system. The proposed approach is composed of two main stages: identification and control. The identification is performed by a Takagi-Sugeno (T-S) fuzzy model, which will be learned with an offline strategy, by Fuzzy c-means, for the antecedent part, and by the Least Squares Method (LSM), for the consequent part. Then, the model is updated with an online method, the Recursive Least Squares with Adaptive Directional Forgetting (RLS-ADF) factor algorithm. The control scheme is based on a generalized Predictive Control approach, which is a Model Predictive Controller (MPC), with the adaptive T-S fuzzy model designed in the identification stage. In this way, the model parameters from GPC are online adapted by RLS-ADF. To validate the control structure, the proposed approach was tested by using an estimated model from real data of a microalgae production process.

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Late-Fusion Multimodal Human Detection Based on RGB and Thermal Images for Robotic Perception

Late-Fusion Multimodal Human Detection Based on RGB and Ther...

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European Conference on Mobile Robots (ECMR)

作者： Elísio Sousa Kennedy O. S. Mota Iago P. Gomes Luís Garrote Denis F. Wolf Cristiano Premebida Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Portugal Institute of Mathematics and Computer Science University of São Paulo Brazil

This paper addresses the problem of detecting humans in RGB and Thermal (long-wave IR) images taken by cameras mounted onboard a mobile robot. Human/Pedestrian detection is currently one of the most pertinent object detection problems, mainly due to safety concerns in autonomous vehicles. The majority of approaches apply deep-learning techniques based solely on RGB images. However, they have a few shortcomings, namely that during foggy weather, nighttime, and low-light scenarios, these images may not contain sufficient information. To address these issues, this work studies the use of thermal cameras as a complementary source of information for human detection in indoor and outdoor environments. The proposed approach uses YOLOv5 to detect pedestrians in both thermal and RGB images. Moreover, the different modalities are combined using early and late fusion techniques. Evaluation of the proposed approach is carried out in the FLIR Aligned dataset and in a new in-house dataset. Results indicate that the use of fusion techniques highlights a promising way to improve the overall performance in this application domain.

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Interaction-aware Maneuver Prediction for Autonomous Vehicles using Interaction Graphs

Interaction-aware Maneuver Prediction for Autonomous Vehicle...

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IEEE Symposium on Intelligent Vehicle

作者： Iago Pachêco Gomes Cristiano Premebida Denis Fernando Wolf Institute of Mathematics and Computer Science University of São Paulo Brazil Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Portugal

Intention prediction (IP) is a challenging task for intelligent vehicle’s perception systems. IP provides the likelihood, or probability, of a target vehicle to perform a maneuver subjected to a finite set of possibilities. There are many factors that influence the decision-making process of a driver, which should be considered in a prediction framework. In addition, the lack of labeled large-scale dataset with maneuver annotation imposes another challenge to the task. This paper proposes an Interaction-aware Maneuver Prediction framework, called IAMP, using interaction graphs to extract complex interaction features from traffic scenes. In addition, we explored a semi-supervised approach called Noisy Student to take advantage of unlabeled data in the training step. Experimental results show relevant improvement when using unlabeled data, increasing the average performance of a classifier by 7.17% of accuracy. Moreover, this approach also made it possible to obtain an intention predictor with similar results to a classifier., even when using a shorter observation horizon.

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Enhancing Precision Agriculture Through Human-in-the-Loop Planning and Control

Enhancing Precision Agriculture Through Human-in-the-Loop Pl...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Shankar A. Deka Sujet Phodapol Andreu Matoses Gimenez Victor Nan Fernandez-Ayala Rufus Wong Pian Yu Xiao Tan Dimos V. Dimarogonas Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Division of Decision and Control Systems School of EECS Royal Institute of Technology (KTH) Stockholm Sweden Department of Cognitive Robotics Faculty of Mechanical Engineering TU Delft Delft Netherlands Department of Computer Science University of Oxford Oxford UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, we introduce a ROS based framework designed for the planning and control of robotic systems within the context of precision agriculture, with an emphasis on human-in-the-loop capabilities. Utilizing Linear Temporal Logic to articulate complex task specifications, our algorithm creates high-level robotic plans that are not only correct by design but also adaptable in real time by human operators. This dual-focus approach ensures that while humans have the flexibility to modify the high-level plan on-the-fly or even take over low-level control of the robots, the system inherently safeguards against any human actions that could potentially breach the predefined task specifications. We demonstrate our algorithm within the dynamic and challenging environment of a real vineyard, where the collaboration between human workers and robots is critical for tasks such as harvesting and pruning, and show the practical applicability and robustness of our software. This work marks a pioneering application of formal methods to complex, real-world agricultural environments.

关键词： Precision agriculture Heuristic algorithms Software algorithms Robustness Human in the loop Software Real-time systems Planning Robots Multi-agent systems

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Interaction-Aware Maneuver Intention Prediction Using Gated Mixture-of-Experts Attention Mechanism

Interaction-Aware Maneuver Intention Prediction Using Gated ...

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International Conference on Intelligent Transportation

This paper addresses the task of predicting the behavior of traffic participants, which involves complexities such as road geometry and agent interactions. To overcome these challenges, this paper presents a novel framework called AIMP (Attention-based Interaction-aware Maneuver Prediction). AIMP utilizes interaction graphs to extract intricate interaction features from traffic scenes. The framework incorporates a Gated Mixture-of-Experts Attention Mechanism, which combines information from road geometry, interaction patterns, and motion dynamics. This fusion process also considers prior maneuver intention estimations, enhancing both explainability and informativeness. Experimental results highlight a performance enhancement (approximately 2% ∼ 9% of accuracy) of the proposed AIMP framework compared to alternative fusion methods.

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Achieving of magnus effect with agros suite

Periodica polytechnica Electrical engineering and computer s...

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Periodica polytechnica electrical engineering and computer science 2021年第2期65卷 131-137页

作者： Lukin, Aleksandr Demidova, Galina Rassõlkin, Anton Faculty of Control Systems and Robotics Itmo University 49 Kronverksky Pr. St. Petersburg197101 Russia Department of Electrical Power Engineering and Mechatronics School of Engineering Tallinn University of Technology 5 Ehitajate tee Tallinn19086 Estonia

When the rotating body gets into the ambiance flow appears the lifting force, called Magnus Effect. That lifting force may be controlled by changing the revolution speed of the body. That phenomenon uses in many engineering applications like wind turbines and marine ships. In this paper, the Magnus Effect simulation is achieved with Agros Suite, a multiplatform application for the solution of physical problems. The article presents the nature of the Magnus Effect and discusses possible applications in engineering. The research question is focused on demonstrating the Magnus Effect with Agros Suite and evaluating the computational power of the personal computer that runs the simulation. The simulation is made keeping in mind the possible application of the Agros Suite tools for Magnus-Effect-based wind generator control algorithms optimization. The simulation result analysis shows that Agros Suite is a reliable tool in accessing and simulation of such phenomena. © 2021 Budapest University of Technology and Economics. All rights reserved.

关键词： Personal computers

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Trajectory Planning for Non-Prehensile Object Transportation

Trajectory Planning for Non-Prehensile Object Transportation

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Lingyun Chen Haoyu Yu Liding Zhang Abdeldjallil Naceri Abdalla Swikir Sami Haddadin Munich Institute of Robotics and Machine Intelligence and the Chair of Robotics Science and Systems Intelligence Technical University of Munich Germany Centre for Tactile Internet With Human-in-the-Loop (CeTI) Department of Electrical and Electronic Engineering Omar Al-Mukhtar University (OMU) Albaida Libya

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Non-prehensile transportation of unstable objects presents a challenging task in robotics. To ensure the success of the transportation, it is necessary to consider both the object’s stability via contact dynamics and the motion constraints of the robot. We propose two novel trajectory planning methods derived from sampling and dynamic programming algorithms, tested on a 7-DoF Franka Emika robot against common strategies like Model Predictive Control (MPC) and S-curve planning, particularly under the constraint of a non-rotating tray. The results demonstrate the effectiveness of our methodologies in improving transportation speed. This research contributes to advancements in robotic manipulation techniques by tackling non-prehensile manipulation of dynamically unstable objects.

关键词： Trajectory planning Shape Refining Transportation Sampling methods Stability analysis Trajectory Planning Dynamic programming Predictive control

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ORCHNet: A Robust Global Feature Aggregation approach for 3D LiDAR-based Place recognition in Orchards

arXiv

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arXiv 2023年

作者： Barros, T. Garrote, L. Conde, P. Coombes, M.J. Liu, C. Premebida, C. Nunes, U.J. The University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Portugal The Dept. of Aeronautical and Automotive Engineering LUCAS Lab. Loughborough University United Kingdom

Robust and reliable place recognition and loop closure detection in agricultural environments is still an open problem. In particular, orchards are a difficult case study due to structural similarity across the entire field. In this work, we address the place recognition problem in orchards resorting to 3D LiDAR data, which is considered a key modality for robustness. Hence, we propose ORCHNet, a deep-learning-based approach that maps 3D-LiDAR scans to global descriptors. Specifically, this work proposes a new global feature aggregation approach, which fuses multiple aggregation methods into a robust global descriptor. ORCHNet is evaluated on real-world data collected in orchards, comprising data from the summer and autumn seasons. To assess the robustness, We compare ORCHNet with state-of-the-art aggregation approaches on data from the same season and across seasons. Moreover, we additionally evaluate the proposed approach as part of a localization framework, where ORCHNet is used as a loop closure detector. The empirical results indicate that, on the place recognition task, ORCHNet outperforms the remaining approaches, and is also more robust across seasons. As for the localization, the edge cases where the path goes through the trees are solved when integrating ORCHNet as a loop detector, showing the potential applicability of the proposed approach in this task. The code will be publicly available at:https://***/Cybonic/***. © 2023, CC BY.

关键词： Orchards

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