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arXiv 2024年

作者： Stathoulopoulos, Nikolaos Saucedo, Mario A.V. Koval, Anton Nikolakopoulos, George The Robotics and AI Group Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå971 87 Sweden

In the field of resource-constrained robots and the need for effective place recognition in multi-robotic systems, this article introduces RecNet, a novel approach that concurrently addresses both challenges. The core of RecNet’s methodology involves a transformative process: it projects 3D point clouds into range images, compresses them using an encoder-decoder framework, and subsequently reconstructs the range image, restoring the original point cloud. Additionally, RecNet utilizes the latent vector extracted from this process for efficient place recognition tasks. This approach not only achieves comparable place recognition results but also maintains a compact representation, suitable for sharing among robots to reconstruct their collective maps. The evaluation of RecNet encompasses an array of metrics, including place recognition performance, the structural similarity of the reconstructed point clouds, and the bandwidth transmission advantages, derived from sharing only the latent vectors. Our proposed approach is assessed using both a publicly available dataset and field experiments1, confirming its efficacy and potential for real-world applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Robots

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Why Sample Space Matters: Keyframe Sampling Optimization for LiDAR-based Place Recognition

arXiv

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arXiv 2024年

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

Recent advances in robotics are driving real-world autonomy for long-term and large-scale missions, where loop closures via place recognition are vital for mitigating pose estimation drift. However, achieving real-time performance remains challenging for resource-constrained mobile robots and multi-robot systems due to the computational burden of high-density sampling, which increases the complexity of comparing and verifying query samples against a growing map database. Conventional methods often retain redundant information or miss critical data by relying on fixed sampling intervals or operating in 3-D space instead of the descriptor feature space. To address these challenges, we introduce the concept of sample space and propose a novel keyframe sampling approach for LiDAR-based place recognition. Our method minimizes redundancy while preserving essential information in the hyper-dimensional descriptor space, supporting both learning-based and handcrafted descriptors. The proposed approach incorporates a sliding window optimization strategy to ensure efficient keyframe selection and real-time performance, enabling seamless integration into robotic pipelines. In sum, our approach demonstrates robust performance across diverse datasets, with the ability to adapt seamlessly from indoor to outdoor scenarios without parameter tuning, reducing loop closure detection times and memory requirements. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots

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Belief Scene Graphs: Expanding Partial Scenes with Objects through Computation of Expectation

arXiv

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arXiv 2024年

作者： Saucedo, Mario A.V. Patel, Akash Saradagi, Akshit Kanellakis, Christoforos Nikolakopoulos, George Robotics & AI Team Department of Computer Electrical and Space Engineering Luleå University of Technology LuleåSE-97187 Sweden

In this article, we propose the novel concept of Belief Scene Graphs, which are utility-driven extensions of partial 3D scene graphs, that enable efficient high-level task planning with partial information. We propose a graph-based learning methodology for the computation of belief (also referred to as expectation) on any given 3D scene graph, which is then used to strategically add new nodes (referred to as blind nodes) that are relevant to a robotic mission. We propose the method of Computation of Expectation based on Correlation Information (CECI), to reasonably approximate real Belief/Expectation, by learning histograms from available training data. A novel Graph Convolutional Neural Network (GCN) model is developed, to learn CECI from a repository of 3D scene graphs. As no database of 3D scene graphs exists for the training of the novel CECI model, we present a novel methodology for generating a 3D scene graph dataset based on semantically annotated real-life 3D spaces. The generated dataset is then utilized to train the proposed CECI model and for extensive validation of the proposed method. We establish the novel concept of Belief Scene Graphs (BSG), as a core component to integrate expectations into abstract representations. This new concept is an evolution of the classical 3D scene graph concept and aims to enable high-level reasoning for task planning and optimization of a variety of robotics missions. The efficacy of the overall framework has been evaluated in an object search scenario, and has also been tested in a real-life experiment to emulate human common sense of unseen-objects. For a video of the article, showcasing the experimental demonstration, please refer to the following link: https: //***/hsGlSCa12iY Copyright © 2024, The Authors. All rights reserved.

关键词： Robot programming

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PRODUCING MULTIFUNCTIONAL PA6/ORGANOSHEET COMPOSITES WITH SUSTAINABLE PLANT BASED GRAPHENE COATINGS

PRODUCING MULTIFUNCTIONAL PA6/ORGANOSHEET COMPOSITES WITH SU...

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SAMPE 2024 Conference and Exhibition

作者： Mulqueen, Daniel W. Wright, Joseph Xiao, Shu Kravchenko, Oleksandr G. Climate Robotics INC HoustonTX77064 United States Department of Mechanical and Aerospace Engineering Old Dominion University Kaufman Hall NorfolkVA23508 United States Department of Electrical and Computer Engineering Old Dominion University Frank Reidy Research Center Suite 300 NorfolkVA23508 United States

ISBN: (纸本)9781934551455

The use of composite components in battery enclosures for electric vehicles requires the addition of electrically conductive elements to provide shielding from Electromagnetic Interference (EMI). Graphene, a highly conductive nanomaterial, can be useful for creating conductive paths on the surface and in interlaminar layers of the composite to provide shielding. In this study we examined impacts of plant-based graphene nanoplatelets (pGNP) surface coating on PA6/glass fiber organosheet with regards to EMI shielding, surface resistance, and flame retardancy. Suspensions of pGNP—derived from agricultural waste materials—were examined for their effectiveness in coating the PA6 surface. Ε-Caprolactam and polyurethane (PU) were examined for their impacts on the sprayed surface and interactions with the substrate. pGNP surface application at 10 g/m2 gave a 790 Ω/sq surface resistance, an improvement over untreated PA6 of seven orders of magnitude. Ε-Caprolactam and PU addition improve adhesion on the PA6 surface, scratch resistance of the coating, and show evidence of in-situ polymerization with pGNP. This material is cost effective for automotive applications and allows for rapid manufacturing using thermoforming processes. We propose a conductive pGNP coating that can be applied after the forming operation, thus avoiding limitations of thermoforming of the conductive veils. © 2024 Soc. for the Advancement of Material and Process engineering. All rights reserved.

关键词： Thermoforming

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EfficientNet-SAM: A Novel EffecientNet with Spatial Attention Mechanism for COVID-19 Detection in Pulmonary CT Scans

EfficientNet-SAM: A Novel EffecientNet with Spatial Attentio...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Ramy Farag Parth Upadhay Jacket Demby’s Yixiang Gao Katherin Garces Montoya Seyed Mohamad Ali Tousi Gbenga Omotara Guilherme DeSouza Department of Electrical Engineering and Computer Science Vision-Guided and Intelligent Robotics Lab - ViGIR Lab University of Missouri-Columbia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Manual analysis and diagnosis of COVID-19 through the examination of Computed Tomography (CT) images of the lungs can be time-consuming and result in errors, especially given high volume of patients and numerous images per patient. So, we address the need for automation of this task by developing a new deep learning-based pipeline. Our motivation was sparked by the CVPR Workshop on "Domain Adaptation, Explainability and Fairness in AI for Medical Image Analysis", more specifically, the "COVID-19 Diagnosis Competition (DEF-AI-MIA COV19D)" under the same Workshop. This challenge provides an opportunity to assess our proposed pipeline for COVID-19 detection from CT scan images. The same pipeline incorporates one of the architectures in the EfficientNet "family", but with an added Spatial Attention Mechanism: EfficientNet-SAM. Also, unlike the traditional/past pipelines, which relied on a preprocessing step, our pipeline takes the raw selected input images without any such step, except for an image-selection step to simply reduce the number of CT images required for training and/or testing. Moreover, our pipeline is computationally efficient, as, for example, it does not incorporate a decoder for segmenting the lungs. It also does not combine different models nor combine RNN with a backbone, as other pipelines in the past did. Nevertheless, our pipeline outperformed all approaches presented by other teams in last year’s instance of the same challenge using the validation subset. It also placed 5th in this year’s competition, ranking less than 1.3% below the 1st place and close to 3.5% above the 6th place based on the macro-F1 score.

关键词： COVID-19 Training Visualization Attention mechanisms Computed tomography Conferences Pipelines

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Addressing Biases in Gastric Cancer Diagnosis Through Generative Models and Vision-Based Surface Tactile Sensing

Addressing Biases in Gastric Cancer Diagnosis Through Genera...

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Medical Imaging 2025: Image-Guided Procedures, Robotic Interventions, and Modeling

作者： Kapuria, Siddhartha Ikoma, Naruhiko Chinchali, Sandeep Alambeigi, Farshid Walker Department of Mechanical Engineering and The Texas Robotics The University of Texas at Austin Austin TX United States Texas Robotics The University of Texas at Austin Austin TX United States Department of Surgical Oncology Division of Surgery The University of Texas MD Anderson Cancer Center Houston TX United States Chandra Family Department of Electrical and Computer Engineering and The Texas Robotics The University of Texas at Austin Austin TX United States

ISBN: (纸本)9781510685949

Towards developing an informed, intuitive, and generalized artificial intelligence model for endoscopic diagnosis of Advanced Gastric Cancer (AGC) lesions, in this work, we extend the use of generative models to augment limited and biased training datasets. This approach aims to enhance model performance and generalizability, tackling the challenge of acquiring sizable and well-balanced datasets in the clinical domain. We applied this approach to our unique dataset comprised of textural images of additively-manufactured realistic AGC tumor phantoms collected using a robotic data collection system and our unique Vision-based Tactile Sensor - HySenSe. Our approach was particularly suited to this dataset, since HySenSe's limited sensing area captures only partial images of the much larger AGC lesions, which reduces the amount of information available in a single image. We trained a single Class-Conditioned Latent Diffusion Model (CC-LDM) on an artificially imbalanced dataset of HySenSe images, logging performance using Fréchet Inception Distance (FID), and exploring different hyperparameter configurations. Using the high-quality synthetic images generated using this model, we performed experiments using our previously developed Dilated ResNet model for AGC tumor classification with the following objectives: (i) study the amount of synthetic data required during training, and (ii) compare different data addition strategies during model training using cross-validation. Note that while the models were trained using both real and synthetic textural images, testing was performed solely on real images. Our experiments showed that the trained classifier demonstrates improved generalizability when trained on a mixture of real and synthetic images, but not all methods of mixing the data are equally effective. Overall, the model shows improved performance even under mixed morphological tumor conditions and partial sensor contact. © 2025 SPIE

关键词： gastric cancer diagnosis generative models machine learning Vision-based tactile sensing

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Design and Development of a Small UGV for Object Retrieval in Domestic Environments

Design and Development of a Small UGV for Object Retrieval i...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Jogeshwar, Bhaavin Kishore Bohannon, Joshua Sevil, Hakki Erhan Department of Intelligent Systems and Robotics PensacolaFL32514 United States Department of Electrical and Computer Engineering PensacolaFL32514 United States

ISBN: (纸本)9798331504847

The objective of this project is to develop an unmanned ground vehicle (UGV) prototype capable of retrieving small objects in domestic environments. As the robotics field continues to advance, the demand for autonomous systems that enhance daily life is rapidly increasing. Autonomous systems have the potential to improve efficiency by performing routine tasks, thereby reducing human effort and promoting convenience in household settings. This study explores the development of a small UGV designed to identify, navigate toward, and retrieve small objects while autonomously avoiding obstacles such as walls and furniture in domestic environments. The system utilizes neural networks for real-time object detection. The UGV design consists of an object retrieval mechanism suitable for handling items typically left scattered in domestic environments. The proof-of-concept results show the potential of designed UGV domestic, and could potentially enable further advancements in autonomous household assistance technologies. © 2025 IEEE.

关键词： Object detection

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STAGE: Scalable and Traversability-Aware Graph based Exploration Planner for Dynamically Varying Environments

arXiv

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arXiv 2024年

作者： Patel, Akash Saucedo, Mario A.V. Kanellakis, Christoforos Nikolakopoulos, George Robotics & AI Team Department of Computer Electrical and Space Engineering Lulea University of Technology LuleaSE-97187 Sweden

In this article, we propose a novel navigation framework that leverages a two layered graph representation of the environment for efficient large-scale exploration, while it integrates a novel uncertainty awareness scheme to handle dynamic scene changes in previously explored areas. The framework is structured around a novel goal oriented graph representation, that consists of, i) the local sub-graph and ii) the global graph layer respectively. The local sub-graphs encode local volumetric gain locations as frontiers, based on the direct pointcloud visibility, allowing fast graph building and path planning. Additionally, the global graph is build in an efficient way, using node-edge information exchange only on overlapping regions of sequential sub-graphs. Different from the state-of-theart graph based exploration methods, the proposed approach efficiently re-uses sub-graphs built in previous iterations to construct the global navigation layer. Another merit of the proposed scheme is the ability to handle scene changes (e.g. blocked pathways), adaptively updating the obstructed part of the global graph from traversable to not-traversable. This operation involved oriented sample space of a path segment in the global graph layer, while removing the respective edges from connected nodes of the global graph in cases of obstructions. As such, the exploration behavior is directing the robot to follow another route in the global re-positioning phase through path-way updates in the global graph. Finally, we showcase the performance of the method both in simulation runs as well as deployed in real-world scene involving a legged robot carrying camera and lidar sensor. © 2024, CC0.

关键词： Global positioning system

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Explorative Study on Motor Interference During Synchronous Human and Robot Arm Movements Under Varied Presence of a Robot Head

Explorative Study on Motor Interference During Synchronous H...

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IEEE International Conference on robotics and Biomimetics

作者： Mertcan Kaya Kolja Kühnlenz Robotics Research Lab Department of Electrical Engineering and Computer Science Coburg University of Applied Sciences and Arts Coburg Germany

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

This paper investigates the influence of a static robot head on deviations of human hand movements from task direction (motor interference) during simultaneous human and robot arm movements using a collaborative robot arm. Synchronous vertical and horizontal human and robot arm movements are conducted in all combinations (within-subjects factors direction and congruency). The presence of the head (head/ no head) is varied as a between-subjects factor. In this first step, only its motionless presence is deliberately focused at, in order to exclude contributions by eye- or head-movement behavior or facial expressions being difficult variables to generalize from. Participants' hands are tracked optically marker-based and deviations from task-related vertical, respectively, horizontal movements are evaluated. In contrast to expectations, results do not show a significant impact of the robot head on motor interference. Thus, there is evidence, that appearance might not be an activating factor for motor interference in HRI.

关键词： Hands Tracking Neurons Humanoid robots Morphology Interference Motors Manipulators Mirrors Robots

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Toward Life-Long Creative Problem Solving: Using World Models for Increased Performance in Novelty Resolution 13

Toward Life-Long Creative Problem Solving: Using World Model...

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13th International Conference on Computational Creativity, ICCC 2022

作者： Gizzi, Evana Lin, Wo Wei Castro, Mateo Guaman Harvey, Ethan Sinapov, Jivko Department of Computer Science Tufts University MA United States Department of Electrical and Computer Engineering Tufts University MA United States The Robotics Institute Carnegie Mellon University PA United States

ISBN: (纸本)9789895416042

Creative problem solving (CPS) is a skill which enables innovation, often times through repeated exploration of an agent’s world. In this work, we investigate methods for life-long creative problem solving (LLCPS), with the goal of increasing CPS capability over time. We develop two world models to facilitate LLCPS which use sub-symbolic action and object information to predict symbolic meta-outcomes of actions. We experiment with three CPS scenarios run sequentially and in simulation. Results suggest that LLCPS is possible through the use of a world model, which can be trained on CPS exploration trials, and used to guide future CPS exploration. © 2022 Proceedings of the 13th International Conference on Computational Creativity, ICCC 2022. All rights reserved.

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