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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Ayham Alharbat Gabriele Ruscelli Roberto Diversi Abeje Mersha Smart Mechatronics and Robotics research group Saxion University of Applied Science Enschede The Netherlands Robotics and Mechatronics research group Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Enschede The Netherlands Department of Electrical Electronic and Information Engineering University of Bologna Bologna Italy

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

This paper presents an external wrench estimator that uses a hybrid dynamics model consisting of a firstprinciples model and a neural network. This framework addresses one of the limitations of the state-of-the-art modelbased wrench observers: the wrench estimation of these observers comprises the external wrench (e.g. collision, physical interaction, wind); in addition to residual wrench (e.g. model parameters uncertainty or unmodeled dynamics). This is a problem if these wrench estimations are to be used as wrench feedback to a force controller, for example. In the proposed framework, a neural network is combined with a first-principles model to estimate the residual dynamics arising from unmodeled dynamics and parameters uncertainties, then, the hybrid trained model is used to estimate the external wrench, leading to a wrench estimation that has smaller contributions from the residual dynamics, and affected more by the external wrench. This method is validated with numerical simulations of an aerial robot in different flying scenarios and different types of residual dynamics, and the statistical analysis of the results shows that the wrench estimation error has improved significantly compared to a model-based wrench observer using only a firstprinciples model.

关键词： Uncertain systems Analytical models Statistical analysis Atmospheric modeling Dynamics Neural networks Observers Autonomous aerial vehicles Numerical simulation Numerical models

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Preoperative imaging in primary hyperparathyroidism patients using 4DCT subtraction maps, a report of three cases

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Radiology Case Reports 2023年第8期18卷 2814-2822页

作者： Krol, Jorian P. Veerbeek, Tessa Deden, Laura N. Joosten, Frank B.M. Slump, Cornelis H. Oyen, Wim J.G. Department of Radiology & Nuclear Medicine Rijnstate Hospital Arnhem Netherlands Department of Robotics and Mechatronics Faculty of Electrical Engineering Mathematics and Computer Sciences University of Twente Enschede Netherlands Department of Biomedical Sciences and Humanitas Clinical and Research Centre Department of Nuclear Medicine Humanitas University Milan Italy Department of Radiology & Nuclear Medicine Radboud University Medical Centre Nijmegen Netherlands

Four-dimensional computed tomography (4DCT) is one of the preoperative imaging modalities that can be used to localize a parathyroid adenoma in primary hyperparathyroidism patients however, sensitivity differs in literature and could be improved especially for multiglandular hyperplasia or double adenomas. The most robust feature on the 4DCT for the differentiation between parathyroid adenoma and thyroid gland tissue is arterial enhancement. To make this better visible, we have developed a subtraction map that shows arterial enhancement as a color scale to increase sensitivity for 4DCT. In this report of 3 cases, we present the usefulness of this subtraction map in a 54-year-old male, a 57-year-old female and a 51-year-old male. Subtraction maps may increase sensitivity for 4DCT, especially for multiglandular hyperplasia or double adenomas. © 2023

关键词： Four dimensional computed tomography Parathyroid adenoma Parathyroid hyperplasia Primary hyperparathyroidism Subtraction map

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A Deep Learning-based Global and Segmentation-based Semantic Feature Fusion Approach for Indoor Scene Classification

arXiv

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arXiv 2023年

作者： Pereira, Ricardo Barros, Tiago Garrote, Luís Lopes, Ana Nunes, Urbano J. University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Portugal Polytechnic Institute of Tomar Portugal

This work proposes a novel approach that uses a semantic segmentation mask to obtain a 2D spatial layout of the segmentation-categories across the scene, designated by segmentation-based semantic features (SSFs). These features represent, per segmentation-category, the pixel count, as well as the 2D average position and respective standard deviation values. Moreover, a two-branch network, GS2F2App, that exploits CNN-based global features extracted from RGB images and the segmentation-based features extracted from the proposed SSFs, is also proposed. GS2F2App was evaluated in two indoor scene benchmark datasets: the SUN RGB-D and the NYU Depth V2, achieving state-of-the-art results on both datasets. © 2023, CC BY.

关键词： Semantic Segmentation

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CHAN: Cross-Modal Hybrid Attention Network for Temporal Language Grounding in Videos

CHAN: Cross-Modal Hybrid Attention Network for Temporal Lang...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Wen Wang Ling Zhong Guang Gao Minhong Wan Jason Gu Zhejiang Lab Research Center for Intelligent Robotics China Department of Electrical and Computer Engineering Dalhousie University Canada

The goal of temporal language grounding (TLG) task is to temporally localize the most semantically matched video segment with respect to a given sentence query in an untrimmed video. How to effectively incorporate the cross-modal interactions between video and language is the key to improve grounding performance. Previous approaches focus on learning correlations by computing the attention matrix between each frame-word pair, while ignoring the global semantics conditioned on one modality for better associating the complex video contents and sentence query of the target modality. In this paper, we propose a novel Cross-modal Hybrid Attention Network, which integrates two parallel attention fusion modules to exploit the semantics of each modality and interactions in cross modalities. One is Intra-Modal Attention Fusion, which utilizes gated self-attention to capture the frame-by-frame and word-by-word relations conditioned on the other modality. The other is Inter-Modal Attention Fusion, which utilizes query and key features derived from different modalities to calculate the co-attention weights and further promote inter-modal fusion. Experimental results show that our CHAN significantly outperforms several existing state-of-the-arts on three challenging datasets (ActivityNet Captions, Charades-STA and TACOS), demonstrating the effectiveness of our proposed method.

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Reactive Multi-agent Coordination using Auction-based Task Allocation and Behavior Trees

Reactive Multi-agent Coordination using Auction-based Task A...

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Control Technology and Applications (CCTA),

作者： Niklas Dahlquist Björn Lindqvist Akshit Saradagi George Nikolakopoulos Department of Computer Science Electrical and Space Engineering Robotics and AI Group Luleå University of Technology Luleå Sweden

This article presents an architecture for multi-agent task allocation and task execution, through the unification of a market-inspired task-auctioning system with Behavior Trees for managing and executing lower level behaviors. We consider the scenario with multi-stage tasks, such as ’pick and place’, whose arrival times are not known a priori. In such a scenario, a coordinating architecture is expected to be reactive to newly arrived tasks and the resulting rerouting of agents should be dependent on the stage of completion of their current multi-stage tasks. In the novel architecture proposed in this article, a central auctioning system gathers bids (cost-estimates for completing currently available tasks) from all agents, and solves a combinatorial problem to optimally assign tasks to agents. For every agent, it’s participation in the auctioning system and execution of an assigned multi-stage task is managed using behavior trees, which switch among several well-defined behaviors in response to changing scenarios. The auctioning system is run at a fixed rate, allowing for newly added tasks to be incorporated into the auctioning system, which makes the solution reactive and allows for rerouting of some agents (subject to the states of the behavior trees). We demonstrate that the proposed architecture is especially well-suited for multistage tasks, where high costs are incurred when rerouting agents who have completed one or more stages of their current tasks. The proposed framework is experimentally validated in multiple scenarios in a lab environment. A video of a demonstration can be found at: https://***/ZdEkoOOlB2g.

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Density-aware Domain Generalization for LiDAR Semantic Segmentation

Density-aware Domain Generalization for LiDAR Semantic Segme...

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

作者： Jaeyeul Kim Jungwan Woo Ukcheol Shin Jean Oh Sunghoon Im Department of Electrical Engineering and Computer Science DGIST Daegu Republic of Korea Robotics Institute Carnegie Mellon University Pittsburgh Pennsylvania United States

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

3D LiDAR-based perception has made remarkable advancements, leading to the widespread adoption of LiDAR in autonomous driving systems. Despite these technological strides, variations in LiDAR sensors and environmental conditions can significantly deteriorate the performance of perception models, primarily due to changes in the density of point clouds. Recent studies in domain generalization have aimed to mitigate this challenge; however, they often rely on the availability of sequential data and ego-motion, which limits their applicability. To address these limitations, we propose two novel methods that enable network operation in a density-aware fashion without any constraints, thereby ensuring consistent performance despite fluctuations in point cloud density. First, we design the network to be density-aware by utilizing the kernel occupancy information from the 3D sparse convolution as geometric features. Subsequently, we further enhance density awareness by incorporating voxel-wise density prediction as an auxiliary task in a self-supervised manner. Our method demonstrates superior performance over current state-of-the-art approaches, achieving this without the need for specific data prerequisites. Our approach is compatible with a variety of 3D backbone architectures, enhancing domain generalization performance by 18.4% while adding a minimal computational overhead of only 7ms.

关键词： Point cloud compression Three-dimensional displays Laser radar Fluctuations Convolution Semantic segmentation Robustness Sensors Kernel Intelligent robots

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Proactive Robot Control for Collaborative Manipulation Using Human Intent

arXiv

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arXiv 2023年

作者： Rysbek, Zhanibek Li, Siyu Shervedani, Afagh Mehri Žefran, Miloš The Robotics Laboratory Department of Electrical and Computer Engineering The University of Illinois Chicago ChicagoIL60607 United States

Collaborative manipulation task often requires negotiation using explicit or implicit communication. An important example is determining where to move when the goal destination is not uniquely specified, and who should lead the motion. This work is motivated by the ability of humans to communicate the desired destination of motion through back- and-forth force exchanges. Inherent to these exchanges is also the ability to dynamically assign a role to each participant, either taking the initiative or deferring to the partner's lead. In this paper, we propose a hierarchical robot control framework that emulates human behavior in communicating a motion destination to a human collaborator and in responding to their actions. At the top level, the controller consists of a set of finite-state machines corresponding to different levels of commitment of the robot to its desired goal configuration. The control architecture is loosely based on the human strategy observed in the human-human experiments, and the key component is a real-time intent recognizer that helps the robot respond to human actions. We describe the details of the control framework, and feature engineering and training process of the intent recognition. The proposed controller was implemented on a UR10e robot (Universal Robots) and evaluated through human studies. The experiments show that the robot correctly recognizes and responds to human input, communicates its intent clearly, and resolves conflict. We report success rates and draw comparisons with human-human experiments to demonstrate the effectiveness of the approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Controllers

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Feature Extraction of Motor Imagination EEG Signals for a Collaborative Exoskeleton Robot Based on PSD Analysis 42

Feature Extraction of Motor Imagination EEG Signals for a Co...

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42nd Chinese Control Conference, CCC 2023

作者： Wu, Yunfeng Li, Junze Ren, Zichen Lu, Bo Zhang, Changzhu Qi, Peng Tongji University Department of Control Science and Engineering No. 1239 Siping Road Shanghai200092 China Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47906 United States School of Mechanicaland Electric Engineering Soochow University Robotics and Microsystems Center Suzhou215021 China

ISBN: (纸本)9789887581543

The integration of brain-machine interface and exoskeleton robot has been widespread application in gait correction, walking assistance, and numerous other scenarios. To effectively extract the electroencephalogram (EEG) signal features of motor imagery while wearing an exoskeleton, this study proposes a frequency band pre-determination method based on power spectral density (PSD) that enables common spatial patterns (CSP) to extract features from the frequency band with the highest energy in the EEG. The signal power spectral density of all channels is obtained at or near the average frequency of the maximum short interval frequency component energy. A second round of filtering is performed on the data, and the signal components are used as input for the subsequent feature extraction step. The CSP method extracts features from the spatial domain signal and generates feature maps. Finally, Support Vector Machines (SVM) are utilized to classify the EEG signals. Based on the pre-set gait of the exoskeleton robot and the motor imagery paradigm, feature extraction and classification of motor imagery EEG data during exoskeleton use were conducted, with an average accuracy rate of 77%. The experimental results demonstrate the effectiveness of this method in extracting the motor imagery EEG features during exoskeleton use. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Electroencephalography

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Edge AI-Driven Video Analytics: A Mobile Deep Learning Framework for Victim Detection in SAR robotics

Edge AI-Driven Video Analytics: A Mobile Deep Learning Frame...

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electrical engineering (FORTEI-ICEE), FORTEI-International Conference on

作者： Muhammad Ihsan Karisma Trinanda Putra Muhamad Yusvin Mustar Aufa Faiz Setyawan Hsueh-Ting Chu Hsing-Chung Chen Department of Electrical Engineering Universitas Muhammadiyah Yogyakarta Bantul Indonesia Department of Electrical Engineering Center of Artificial Intelligence and Robotics Studies Universitas Muhammadiyah Y ogyakarta Bantul Indonesia Department of Computer Science and Information Engineering Asia University Taichung Taiwan

ISBN: (数字)9798331542207

ISBN: (纸本)9798331542214

In post-disaster search and rescue (SAR) missions, it is crucial for robots to distinguish between actual victims and dummy objects, despite their similar characteristics. Edge video analytics have demonstrated exceptional performance in such missions, however, the trade-off between high-performing deep learning models and computational complexity often results in inadequate computational efficiency for SAR robotics. This study tackles this challenge by deploying a deep learning (DL) algorithm, i.e., MobileNet Single Shot Detector (SSD), specifically tailored for embedded systems, to perform object detection and classification tasks on lab-scale SAR robots. The proposed algorithm is developed in Python and implemented on a low-powered edge computing device. The effectiveness of the proposed algorithm in object localization is highlighted by its mAP score of 0.6247 and an average IoU of 0.9036. Experimental tests using a confusion matrix showed that the model accurately predicted 42 dummy objects and 43 victim dolls. The model achieved an overall accuracy of 89.47%, with a precision of 1.0, a recall of 0.8947, and an F1 Score of 0.9444. Real-time testing confirmed the model's robust performance, maintaining high accuracy and a stable frame rate of 47 FPS. These findings corroborate the efficacy of the MobileNet SSD algorithm in reliably detecting and differentiating objects within embedded SAR robotic operations.

关键词： Deep learning Analytical models Accuracy Computational modeling Visual analytics Object detection Real-time systems Classification algorithms Robots Testing

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Flexible Multi-DoF Aerial 3D Printing Supported with Automated Optimal Chunking

Flexible Multi-DoF Aerial 3D Printing Supported with Automat...

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

作者： Marios-Nektarios Stamatopoulos Avijit Banerjee George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics and Artificial Intelligence Group Luleå University of Technology Luleå Sweden

The future of 3D printing utilizing unmanned aerial vehicles (UAVs) presents a promising capability to revolutionize manufacturing and to enable the creation of large-scale structures in remote and hard-to-reach areas e.g. in other planetary systems. Nevertheless, the limited payload capacity of UAVs and the complexity in the 3D printing of large objects pose significant challenges. In this article we propose a novel chunk-based framework for distributed 3D printing using UAVs that sets the basis for a fully collaborative aerial 3D printing of challenging structures. The presented framework, through a novel proposed optimisation process, is able to divide the 3D model to be printed into small, manageable chunks and to assign them to a UAV for partial printing of the assigned chunk, in a fully autonomous approach. Thus, we establish the algorithms for chunk division, allocation, and printing, and we also introduce a novel algorithm that efficiently partitions the mesh into planar chunks, while accounting for the inter-connectivity constraints of the chunks. The efficiency of the proposed framework is demonstrated through multiple physics based simulations in Gazebo, where a CAD construction mesh is printed via multiple UAVs carrying materials whose volume is proportionate to a fraction of the total mesh volume.

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