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Development and Evaluation of a Machine Learning Model for Cobot Action Prediction: Optimizing Algorithms Through Cognitive Workload Metrics

Development and Evaluation of a Machine Learning Model for C...

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international conference on robotics and Artificial Intelligence (ICRAI)

作者： Lala Rukh Yasir Ahmad Uzair Khaleeq Uz Zaman Mohsin Islam Tiwana Afshan Naseem Department of Engineering Management NUST College of Electrical and Mechanical Engineering Islamabad Pakistan Mechanical Engineering Department NUST College of Electrical and Mechanical Engineering Islamabad Pakistan Mechatronics Engineering Department NUST College of Electrical and Mechanical Engineering Islamabad Pakistan

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

Collaborative robots (Cobots) have replaced traditional industrial robots on the factory floors, enhancing automation and efficiency. From the perspective of Industry 5.0, centering around H.F. (Human Factors), concerns about cognitive ergonomics persist as Cobots' unpredictable actions can increase cognitive workload and reduce productivity. Identifying and prioritising the operators' mental and physical well-being is necessary for safer and more efficient Human Robot Collaboration (HRC). This paper proposes a novel machine-learning model for Cobot action prediction and evaluates which algorithm is best for predicting Cobot actions based on operators' cognitive states. 75 participants with different levels of technophobia, technical expertise, and age collaborated with a Cobot to solve a complex maze puzzle. Firstly, each participant's Cognitive Workload (CWL) was measured using a combination of subjective and objective metrics (NASA-Task Load index, Heart Rate Variability, and Error Rate), followed by the development of a machine learning model that takes human factors as input parameters and predicts Cobot's actions accordingly. This study evaluates various machine learning models for Cobot action prediction and identifies that the Random Forest Classifier outperforms other models with an accuracy of 90%. The model can be implemented in various industrial settings to improve HRC safety and operators' cognitive ergonomics.

关键词： Productivity Service robots Ergonomics Collaborative robots Human factors Predictive models Prediction algorithms Safety Random forests Load modeling

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Identification of Mangrove Leaf Disease in Deep Convolutional Neural Network Approach

Identification of Mangrove Leaf Disease in Deep Convolutiona...

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Control & automation, Electronics, robotics, Internet of Things, and Artificial Intelligence (CERIA), IEEE international conference on

作者： Romi Fadillah Rahmat Opim Salim Sitompul Maya Silvi Lydia Fahmi Fahmi Raska Almashura Fahrurrozi Lubis Department of Information Technology Universitas Sumatera Utara Medan Indonesia Department of Electrical Engineering Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798331511166

ISBN: (纸本)9798331511173

Mangrove forests in Indonesia are distributed over many coastal regions, exhibiting significant biodiversity and diverse structural forms. Mangroves serve as a mitigating factor against wave action and intense wind energy resulting from adverse weather conditions. Another purpose of mangroves is to provide as a food source for various marine species. Mangroves consist of many principal components, including roots, stems, leaves, and blooms. For optimal growth, some portions of mangrove plants must be safeguarded from several diseases, whether induced by parasites/pests or by environmental conditions. The leaves of the mangrove are the most vulnerable to disease; yet, pests visible on the leaves are typically inadequately managed, resulting in the plant being immediately removed when all its leaves are infested. This study uses a Deep Convolutional Neural Network to recognize these pests effectively. The image processing methods used in this study include smoothing and sharpening. The study findings indicate that the used approach effectively identifies mangrove leaf diseases, with an accuracy of 99.2%.

关键词： Accuracy Smoothing methods Wind energy Noise Sea measurements Real-time systems Mobile handsets Convolutional neural networks Robots Diseases

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Prediction and Identification of Multiple Sclerosis Patients from Gait Features Using Machine Learning Algorithms and Ensemble methods in Embedded Sensors Environment 2

Prediction and Identification of Multiple Sclerosis Patients...

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2nd international conference on Computer Vision and Internet of Things, ICCVIoT 2024

作者： Rupani, Daksh Pal, Granth Soni, Kushagra Kumar, Suraj Kishoreraja, P.C. Sivakumar, R. Tamilnadu Vellore632014 India Tamilnadu Vellore632014 India Tamilnadu Vellore632014 India Tamilnadu Vellore632014 India

ISBN: (纸本)9781837242672

Multiple sclerosis is a chronic autoimmune disease prevalent among mid-aged to younger adults. The objectives of implementing advanced computational techniques in gait feature analysis are to refine the analytical process, improve the accuracy of gait assessment and understanding human locomotion patterns to predict and identify the multiple sclerosis patient using logistic regression, Support Vector Machine and K-Nearest Neighbours classifiers. We aim to automate the analysis of gait feature data collected from various sensors to expedite the prediction and identification of multiple sclerosis. This automation facilitates the early detection of subtle deviations in gait dynamics, which could signify neurological impairments. Logistic regression, Support Vector Machine and KNN models are capable of processing intricate gait data to discern meaningful patterns and characteristics indicative of multiple sclerosis conditions with minimum number of gait features. This paper introduces new gait features to improve the model's performance and differentiate the multiple sclerosis from healthy control group. KNN demonstrates superior accuracy (96.55%) compared to Support Vector Machine (90.05%) and logistic regression (86.35%). KNN with bagging achieved the highest prediction accuracy (94.4%), while logistic regression with bagging had the lowest (83.3%). This paper proposes an ensemble learning method that uses weight averaging to get combine the predictions of above models with ensemble accuracy of 90.12%. A new index called match index is created for the identification of multiple sclerosis. © The Institution of Engineering & Technology 2024.

关键词： Logistic regression

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Gen2Sim: Scaling up Robot Learning in Simulation with Generative models

Gen2Sim: Scaling up Robot Learning in Simulation with Genera...

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IEEE international conference on robotics and automation (ICRA)

作者： Pushkal Katara Zhou Xian Katerina Fragkiadaki Carnegie Mellon University

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Generalist robot manipulators need to learn a wide variety of manipulation skills across diverse environments. Current robot training pipelines rely on humans to provide kinesthetic demonstrations or to program simulation environments and to code up reward functions for reinforcement learning. Such human involvement is an important bottleneck towards scaling up robot learning across diverse tasks and environments. We propose Generation to Simulation (Gen2Sim), a method for scaling up robot skill learning in simulation by automating generation of 3D assets, task descriptions, task decompositions and reward functions using large pre-trained generative models of language and vision. We generate 3D assets for simulation by lifting open-world 2D object-centric images to 3D using image diffusion models and querying LLMs to determine plausible physics parameters. Given URDF files of generated and human-developed assets, we chain-of-thought prompt LLMs to map these to relevant task descriptions, temporal decompositions, and corresponding python reward functions for reinforcement learning. We show Gen2Sim succeeds in learning policies for diverse long horizon tasks, where reinforcement learning with non temporally decomposed reward functions fails. Gen2Sim provides a viable path for scaling up reinforcement learning for robot manipulators in simulation, both by diversifying and expanding task and environment development, and by facilitating the discovery of reinforcement-learned behaviors through temporal task decomposition in RL. Our work contributes hundreds of simulated assets, tasks and demonstrations, taking a step towards fully autonomous robotic manipulation skill acquisition in simulation.

关键词： Training Solid modeling Three-dimensional displays Training data Reinforcement learning Manipulators Robot learning

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Deep Model Predictive Optimization

Deep Model Predictive Optimization

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IEEE international conference on robotics and automation (ICRA)

作者： Jacob Sacks Rwik Rana Kevin Huang Alex Spitzer Guanya Shi Byron Boots University of Washington Seattle WA USA Robotics Institute Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

A major challenge in robotics is to design robust policies which enable complex and agile behaviors in the real world. On one end of the spectrum, we have model-free reinforcement learning (MFRL), which is incredibly flexible and general but often results in brittle policies. In contrast, model predictive control (MPC) continually re-plans at each time step to remain robust to perturbations and model inaccuracies. However, despite its real-world successes, MPC often under-performs the optimal strategy. This is due to model quality, myopic behavior from short planning horizons, and approximations due to computational constraints. And even with a perfect model and enough compute, MPC can get stuck in bad local optima, depending heavily on the quality of the optimization algorithm. To this end, we propose Deep Model Predictive Optimization (DMPO), which learns the inner-loop of an MPC optimization algorithm directly via experience, specifically tailored to the needs of the control problem. We evaluate DMPO on a real quadrotor agile trajectory tracking task, on which it improves performance over a baseline MPC algorithm for a given computational budget. It can outperform the best MPC algorithm by up to 27% with fewer samples and an end-to-end policy trained with MFRL by 19%. Moreover, because DMPO requires fewer samples, it can also achieve these benefits with 4.3× less memory. When we subject the quadrotor to turbulent wind fields with an attached drag plate, DMPO can adapt zero-shot while still outperforming all baselines. Additional results can be found at https://***/mr2ywmnw.

关键词： Runtime Trajectory tracking Computational modeling Predictive models Prediction algorithms Approximation algorithms Trajectory

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Kimera-Multi: a System for Distributed Multi-Robot Metric-Semantic Simultaneous Localization and Mapping

Kimera-Multi: a System for Distributed Multi-Robot Metric-Se...

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IEEE international conference on robotics and automation (ICRA)

作者： Chang, Yun Tian, Yulun How, Jonathan P. Carlone, Luca MIT Lab Informat & Decis Syst 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9781728190778

We present the first fully distributed multi-robot system for dense metric-semantic Simultaneous Localization and Mapping (SLAM). Our system, dubbed Kimera-Multi, is implemented by a team of robots equipped with visual-inertial sensors, and builds a 3D mesh model of the environment in real-time, where each face of the mesh is annotated with a semantic label (e.g., building, road, objects). In Kimera-Multi, each robot builds a local trajectory estimate and a local mesh using Kimera [1] Then, when two robots are within communication range, they initiate a distributed place recognition and robust pose graph optimization protocol with a novel incremental maximum clique outlier rejection;the protocol allows the robots to improve their local trajectory estimates by leveraging inter-robot loop closures. Finally, each robot uses its improved trajectory estimate to correct the local mesh using mesh deformation techniques. We demonstrate Kimera-Multi in photo-realistic simulations and real data. Kimera-Multi (i) is able to build accurate 3D metric-semantic meshes, (ii) is robust to incorrect loop closures while requiring less computation than state-of-the-art distributed SLAM backends, and (iii) is efficient, both in terms of computation at each robot as well as communication bandwidth.

关键词： Solid modeling Three-dimensional displays Simultaneous localization and mapping Protocols Computational modeling Data models Trajectory

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Towards the automation of Wildfire Monitoring with Aerial Vehicles: The FIREFRONT Project 1

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26th international conference on Pattern Recognition, ICPR 2022

作者： Ribeiro, Ricardo Bernardino, Alexandre Cruz, Gonçalo Silva, Diogo Felix, Luís Caetano, João Folgado, Duarte Francisco, João Simões, Nuno Viegas, Carlos Xavier Viegas, Domingos Xavier Harkat, Houda Nascimento, Jose Institute for Systems and Robotics Instituto Superior Técnico Lisbon Portugal CIAFA FAP Sintra Portugal AeroEspaço Science Center - ACTV Torres Vedras Portugal UAVision Torres-Vedras Portugal ADAI Univ Coimbra Coimbra Portugal UNINOVA-CTS Monte Caparica Portugal IT Lisboa Portugal

ISBN: (数字)9783031377426

ISBN: (纸本)9783031377419

This paper describes an integrated system for wildfire monitoring with aerial vehicles. The system is composed of an airborne payload compatible with UAVs and manned aircraft and associated communication and software infrastructure. The system is able to detect fire and smoke in the payload thermal and RGB images, georeference their location in ground coordinates using telemetry (GPS, IMU), and forecast the fire front spread. We describe the progress made in the payload system, acquired datasets, fire/smoke segmentation methods, fire georeferencing, and wildfire spread modeling, providing the reader with a overview of the project and related technical publications. © 2023, Springer Nature Switzerland AG.

关键词： Fires

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Nonlinear Dynamical System Identification Under External Disturbances by Maximum a Posteriori (MAP) Estimation for robotics

Nonlinear Dynamical System Identification Under External Dis...

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18th IEEE international conference on Advanced Motion Control, AMC 2024

作者： Yanabe, Daisuke Kanoga, Suguru Fremont, Vincent Murakami, Toshiyuki Graduate School of Science and Technology Keio University Kanagawa Japan Tokyo Japan Ecole Centrale de Nantes Nantes Universite and Cnrs LS2N Nantes France Keio University Department of System Design Engineering Kanagawa Japan

ISBN: (纸本)9798350342819

In nonlinear dynamical system identification, a flexible statistical model is commonly used for identification to compensate for non-linear elements. The flexibility in the statistical model induces a high sensitivity to the existence of external disturbances that could deteriorate the identification accuracy substantially. Most of the identification techniques assume a processing noise as a statistical variable and apply a maximum likelihood-based method to identify the dynamical system. Most of these methods do not take into account the effect of external disturbances. In this work, we also considered external disturbances as one of the statistical parameters to estimate. By estimating values for external disturbances and applying maximum a posteriori estimation, identification accuracy could be improved compared to models without considering external disturbances. © 2024 IEEE.

关键词： Nonlinear dynamical systems

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VESSELimg: A Large UAV-Based Vessel Image Dataset for Port Surveillance

VESSELimg: A Large UAV-Based Vessel Image Dataset for Port S...

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international conference on Unmanned Aircraft Systems (ICUAS)

作者： Bartomeu Rubí Jonathan Cacace Javier Rodriguez Rafael Company Mark Tanner Roberto Arzo Julián Cayero Eurecat Centre Tecnlògic de Catalunya Robotics and Automation Unit Cerdanyola del Vallès Barcelona Spain Fundación Valenciaport Valencia Spain

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

In the domain of maritime security and safety, the surveillance and monitoring of vessels in ports are crucial for safeguarding against potential threats and ensuring the overall integrity of port operations. Current port surveillance methods, including radar systems, CCTV networks, and AIS, exhibit limitations, necessitating innovative solutions. The integration of drone-captured imagery holds promise, particularly for detecting non-cooperative vessels that may evade traditional surveillance methods. This paper presents VESSELimg dataset, a meticulously gathered and annotated collection of drone-captured images within port environments. This dataset serves as a benchmark for training and validating deep learning models, specifically tailored for automatic vessel detection, covering diverse vessel types. The implementation of a YOLO-based deep learning model for real-time inference demonstrates the practical applicability of the dataset, underscoring its potential for enhancing security and safety measures in port environments.

关键词： Deep learning Training YOLO Surveillance Seaports Benchmark testing Real-time systems

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Pancreatic Tumor Recognition from CT Images through Advanced Deep Learning Techniques

Pancreatic Tumor Recognition from CT Images through Advanced...

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IEEE international conference on automation, Quality and Testing, robotics, AQTR

作者： Delia Mitrea Raluca Brehar Razvan Itu Sergiu Nedevschi Mihai Socaciu Radu Badea Department of Computer Science Faculty of Automation and Computer Science Technical University of Cluj-Napoca Cluj-Napoca Romania Department of Medical Imaging I. Hatieganu University of Medicine and Pharmacy Cluj-Napoca Romania Octavian Fodor Regional Institute of Gastroenterology and Hepatology Cluj Napoca – Romania Cluj-Napoca Romania

ISBN: (数字)9798350361933

ISBN: (纸本)9798350361940

Cancer constitutes a major affection nowadays, leading to death in many situations. Pancreatic malignant tumors represent the fourth most frequent cause of cancer related death in United States and Europe. The most trustworthy cancer diagnosis method is the biopsy, but this technique raises several risks, the most relevant one being the danger of tumoral spread through the human body. Thus, in the context of our research, we employ computerized methods for achieving highly accurate abdominal tumor recognition and segmentation within medical images. In the current approach, we performed the automatic recognition of pancreatic malignant tumors within Computed Tomography (CT) images, with the aid of Convolutional Neural Networks (CNN), by employing last generation architectures, as well as their improved versions, respectively combinations of these structures at classifier and decision level. The classification performance was assessed through specific metrics, an accuracy above 98% being achieved.

关键词： Image recognition Accuracy Computed tomography Malignant tumors Neural networks Convolutional neural networks Robots

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