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Underwater Robot-To-Human Communication Via Motion: Implementation and Full-Loop Human Interface Evaluation 18th

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Underwater Robot-To-Human Communication Via Motion: Implemen...

18th robotics: Science and Systems, RSS 2022

作者： Fulton, Michael Mehtaz, Muntaqim Sattar, Junaed Queeglay, Owen Deptartment of Computer Science & Engineering Minnesota Robotics Institute University of Minnesota Twin Cities United States Department of Electrical Engineering Stanford University United States

ISBN: (纸本)9780992374785

Autonomous underwater vehicles (AUVs) have long lagged behind other types of robots in supporting natural communication modes for human-robot interaction. Due to the limitations of the environment, most AUVs use digital displays or topside human-in-the-loop communications as their primary or only communication vectors. Natural methods for robotto-human communication such as robot "gestures" have been proposed, but never evaluated on non-simulated AUVs. In this paper, we enhance, implement and evaluate a robot-to-human communication system for AUVs called Robot Communication Via Motion (RCVM), which utilizes explicit motion phrases (kinemes) to communicate with a dive partner. We present a small pilot study that shows our implementation to be reasonably effective in person followed by a large-population study, comparing the communication effectiveness of our RCVM implementation to three baseline systems. Our results establish RCVM as an effective method of robot-to-human communication underwater and reveal the differences with more traditional communication vectors in how accurately communication is achieved at different viewpoints and types of information payloads. © 2022, MIT Press Journals. All rights reserved.

关键词： Autonomous underwater vehicles

Balancing Accuracy and Efficiency for Large-Scale SLAM: A Minimal Subset Approach for Scalable Loop Closures

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arXiv 2025年

作者： Stathoulopoulos, Nikolaos Kanellakis, Christoforos Nikolakopoulos, George Robotics and AI Group Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå971 87 Sweden

Typical LiDAR SLAM architectures feature a front-end for odometry estimation and a back-end for refining and optimizing the trajectory and map, commonly through loop closures. However, loop closure detection in large-scale missions presents significant computational challenges due to the need to identify, verify, and process numerous candidate pairs for pose graph optimization. Keyframe sampling bridges the front-end and back-end by selecting frames for storing and processing during global optimization. This article proposes an online keyframe sampling approach that constructs the pose graph using the most impactful keyframes for loop closure. We introduce the Minimal Subset Approach (MSA), which optimizes two key objectives: redundancy minimization and information preservation, implemented within a sliding window framework. By operating in the feature space rather than 3-D space, MSA efficiently reduces redundant keyframes while retaining essential information. In sum, evaluations on diverse public datasets show that the proposed approach outperforms naive methods in reducing false positive rates in place recognition, while delivering superior ATE and RPE in metric localization, without the need for manual parameter tuning. Additionally, MSA demonstrates efficiency and scalability by reducing memory usage and computational overhead during loop closure detection and pose graph optimization. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph algorithms

Autonomous Point Cloud Segmentation for Power Lines Inspection in Smart Grid

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IFAC-PapersOnLine 2023年第2期56卷 11754-11761页

作者： Alexander Kyuroson Anton Koval George Nikolakopoulos Robotics & AI Team Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå SE-97187 Sweden

LiDAR is currently one of the most utilized sensors to effectively monitor the status of power lines and facilitate the inspection of remote power distribution networks and related infrastructures. To ensure the safe operation of the smart grid, various remote data acquisition strategies, such as Airborne Laser Scanning (ALS), Mobile Laser Scanning (MLS), and Terrestrial Laser Scanning (TSL) have been leveraged to allow continuous monitoring of regional power networks, which are typically surrounded by dense vegetation. In this article, an unsupervised Machine Learning (ML) framework is proposed, to detect, extract and analyze the characteristics of power lines of both high and low voltage, as well as the surrounding vegetation in a Power Line Corridor (PLC) solely from LiDAR data. Initially, the proposed approach eliminates the ground points from higher elevation points based on statistical analysis that applies density criteria and histogram thresholding. After denoising and transforming of the remaining candidate points by applying Principle Component Analysis (PCA) and Kd-tree, power line segmentation is achieved by utilizing a two-stage DBSCAN clustering to identify each power line individually. Finally, all high elevation points in the PLC are identified based on their distance to the newly segmented power lines. Conducted experiments illustrate that the proposed framework is an agnostic method that can efficiently detect the power lines and perform PLC-based hazard analysis.

关键词： Power line Segmentation Unsupervised learning DBSCAN UAV

A Tree-based Next-best-trajectory Method for 3D UAV Exploration

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arXiv 2024年

作者： Lindqvist, Björn Patel, Akash Löfgren, Kalle Nikolakopoulos, George The Robotics and Artificial Intelligence Group Department of Computer Electrical and Space Engineering Luleå University of Technology LuleåSE-97187 Sweden The Department of Computer Electrical and Space Engineering Luleå University of Technology Sweden

This work presents a fully integrated tree-based combined exploration-planning algorithm: Exploration-RRT (ERRT). The algorithm is focused on providing real-time solutions for local exploration in a fully unknown and unstructured environment while directly incorporating exploratory behavior, robot-safe path planning, and robot actuation into the central problem. ERRT provides a complete sampling and tree-based solution for evaluating"where to go next" by considering a tradeoff between maximizing information gain, and minimizing the distances travelled and the robot actuation along the path. The complete scheme is evaluated in extensive simulations, comparisons, as well as real-world field experiments in constrained and narrow subterranean and GPS-denied environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

Evaluation of Emotional Intelligence for Academic Stress Analysis

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Evaluation of Emotional Intelligence for Academic Stress Ana...

International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Asif Khan Mohammad Atif Siddiqui Department of Computer Application Integral Robotics Lab Integral University Lucknow India Department of Electrical Engineering Integral Robotics Lab Integral University Lucknow India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

The research study on the "Design and Implementation of an AI-Enhanced Mental Health Tool for Academic Stress" addresses the growing mental health crisis among students, particularly concerning academic pressures. The study highlights alarming statistics, such as over 13,000 student suicides in India in 2021—a 4.5% increase from the previous year and a 70% rise since 2011—indicating an urgent need for targeted interventions. The academic environment, characterized by high expectations, intense pressure, and fierce competition, places considerable strain on students’ mental well-being. Recognizing that one-size-fits-all solutions are inadequate, this research advocates for a personalized approach to address these unique challenges. Artificial Intelligence (AI) is positioned as a key solution in this effort. The proposed AI-enhanced mental health tool aims to provide students with real-time, personalized support. Through proactive interventions and individualized assistance, it seeks to revolutionize mental health care in academic settings. This study underscores the importance of innovative approaches to tackling the student mental health crisis. By fostering resilience and well-being, the AI tool aims to effectively address academic stress and improve mental health outcomes.

关键词： Mental health Real-time systems Artificial intelligence Resilience Strain

Toward Fully Autonomous Flexible Chunk-Based Aerial Additive Manufacturing: Insights from Experimental Validation

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arXiv 2025年

作者： Stamatopoulos, Marios-Nektarios Haluska, Jakub Small, Elias Marroush, Jude Banerjee, Avijit Nikolakopoulos, George Robotics & AI Team Department of Computer Electrical and Space Engineering Luleå University of Technology LuleåSE-97187 Sweden

A novel autonomous chunk-based aerial additive manufacturing framework is presented, supported with experimental demonstration advancing aerial 3D printing. An optimization-based decomposition algorithm transforms structures into sub-components, or chunks, treated as individual tasks coordinated via a dependency graph, ensuring sequential assignment to UAVs considering inter-dependencies and printability constraints for seamless execution. A specially designed hexacopter equipped with a pressurized canister for lightweight expandable foam extrusion is utilized to deposit the material in a controlled manner. To further enhance precise execution of the printing, an offset-free Model Predictive Control mechanism is considered compensating reactively for disturbances and ground effect during execution. Additionally, an interlocking mechanism is introduced in the chunking process to enhance structural cohesion and improve layer adhesion. Extensive experiments demonstrate the framework’s effectiveness in constructing precise structures of various shapes, while seamlessly adapting to practical challenges, proving its potential for a transformative leap in aerial robotic capability for autonomous construction. A video with the overall demonstration can be found here: https://***/WC1rLMLKEg4. © 2025, CC BY-NC-ND.

关键词： Unmanned aerial vehicles (UAV)

Soft Arm-Motor Thrust Characterization for a Pneumatically Actuated Soft Morphing Quadrotor

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arXiv 2025年

作者： Sumathy, Vidya Haluska, Jakub Nikolokopoulos, George Robotics & AI Group Department of Computer Electrical and Space Engineering Luleå University of Technology LuleåSE-97187 Sweden

In this work, an experimental characterization of the configuration space of a soft, pneumatically actuated morphing quadrotor is presented, with a focus on precise thrust characterization of its flexible arms, considering the effect of downwash. Unlike traditional quadrotors, the soft drone has pneumatically actuated arms, introducing complex, nonlinear interactions between motor thrust and arm deformation, which make precise control challenging. The silicone arms are actuated using differential pressure to achieve flexibility and thus have a variable workspace compared to their fixed counterparts. The deflection of the soft arms during compression and expansion is controlled throughout the flight. However, in real time, the downwash from the motor attached at the tip of the soft arm generates a significant and random disturbance on the arm. This disturbance affects both the desired deflection of the arm and the overall stability of the system. To address this factor, an experimental characterization of the effect of downwash on the deflection angle of the arm is conducted. © 2025, CC BY.

关键词： Pneumatics

An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

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computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

Sub-Optimal Decentralized Navigation of Multiple Holonomic Agents in Simply-Connected Workspaces

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Sub-Optimal Decentralized Navigation of Multiple Holonomic A...

IEEE Conference on Decision and Control

作者： Dimitrios Kotsinis Charalampos P. Bechlioulis Department of Electrical and Computer Engineering Athena Research Center Robotics Institute Maroussi Greece University of Patras Greece

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we propose a sub-optimal approach for the multi-agent navigation problem in simply-connected workspaces. We design a decentralized control law exhibiting the following three properties: (1) navigation of each agent with the optimal policy towards its destination, (2) avoidance of collision with other nearby agents and the workspace boundary, and (3) knowledge about the current position and not the destination of nearby agents. Moreover, we refer to the sub-optimal approach because the computational complexity and time needed to calculate the global optimal solution become unrealistic as the number of agents increases. In our case, each agent has a predetermined optimal policy calculated by a novel off-policy iterative method, to go towards its destination and it deviates from it in order to avoid collisions. The purpose of the simulation study is to examine how much the sub-optimal greedy trajectory of each agent deviates from the optimal one.

关键词： Navigation Decentralized control Vectors Trajectory Iterative methods Computational complexity Game theory Multi-agent systems