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IEEE International Workshop on Robot and Human Communication (ROMAN)

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

This paper reports a failed attempt to predict deviations of human arm movements from task elicited by perception of simultaneous robot arm movements (motor interference) using subjective ratings of anthropomorphism. This motor interference effect is known to result from unconscious imitation of movements of a human-like counterpart due to mirror-neuron activation. A study is conducted, with test participants and a robot conducting vertical and horizontal arm movements in front of each other in all combinations, and additionally with and without a robot head being present, in a between-subjects design. Perceived anthropomorphism is acquired using the evaluated tests HRIES and Godspeed. A correlation analysis on the dependency of motor interference on anthropomorphism did not reveal significant results of the constructs targeting human-like appearance, intelligence or movement behavior, which were expected to represent relevant predictor parameters. Further, scatter plots do not show a clear pattern. Only the likability Godspeed sub-scale is significant, which however may not be related to the targeted effect. Additionally, the ‘consciousness’ item within the ‘anthropomorphism sub-scale of Godspeed is significant, however, with unclear effect. So, no evidence could be found, that subjective assessments of anthropomorphism with existing tests may be suitable for prediction of motor interference and further research is required.

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GS-LIVM: Real-Time Photo-Realistic LiDAR-Inertial-Visual Mapping with Gaussian Splatting

arXiv

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arXiv 2024年

作者： Xie, Yusen Huang, Zhenmin Wu, Jin Ma, Jun Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

In this paper, we introduce GS-LIVM, a real-time photorealistic LiDAR-Inertial-Visual mapping framework with Gaussian Splatting tailored for outdoor scenes. Compared to existing methods based on Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), our approach enables real-time photo-realistic mapping while ensuring high-quality image rendering in large-scale unbounded outdoor environments. In this work, Gaussian Process Regression (GPR) is employed to mitigate the issues resulting from sparse and unevenly distributed LiDAR observations. The voxel-based 3D Gaussians map representation facilitates real-time dense mapping in large outdoor environments with acceleration governed by custom CUDA kernels. Moreover, the overall framework is designed in a covariance-centered manner, where the estimated covariance is used to initialize the scale and rotation of 3D Gaussians, as well as update the parameters of the GPR. We evaluate our algorithm on several outdoor datasets, and the results demonstrate that our method achieves state-of-the-art performance in terms of mapping efficiency and rendering quality. The source code is available on GitHub . Copyright © 2024, The Authors. All rights reserved.

关键词： Mapping

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CLEAR-Net: A Cascaded Local and External Attention Network for Enhanced Dehazing of Remote Sensing Images

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Ali, Anas M. El-Rabaie, El-Sayed M. Ramadan, Khalil F. El-Samie, Fathi E. Abd El-Shafai, Walid Prince Sultan University Robotics and Internet-of-Things Laboratory Riyadh12435 Saudi Arabia Faculty of Electronic Engineering Menoufia University Department of Electronics and Electrical Communications Engineering Menouf32952 Egypt Computer Science Department Riyadh11586 Saudi Arabia

Atmospheric haze degrades remote sensing images by obscuring critical details, diminishing data interpretation accuracy. This paper presents CLEAR-Net: Cascaded Local and External Attention Resource Network, a novel dehazing framework designed to enhance clarity and detail in remote sensing images. CLEAR-Net employs a two-block cascaded architecture to address both global and local haze effects. The first block utilizes an Outer Global Attention (OGA) mechanism, processing global features via a U-Net Inner Attention (UIA) module to mitigate widespread haze. The second block focuses on Outer Local Attention (OLA), refining local image features through reintegration with the UIA module. An ensemble method selects the most effective model weights based on perceptual quality metrics, and a composite loss function emphasizes the preservation of high-frequency details to maintain textural and edge fidelity. CLEAR-Net's efficacy is evaluated on the SateHaze1k and RICE datasets, outperforming existing state-of-theart dehazing techniques. On SateHaze1k, CLEAR-Net achieves an average PSNR of 27.876 dB, surpassing the next best model by approximately 4.81%;under moderate fog, it attains a PSNR of 30.824 dB, about 9.8% higher than existing methods. On the RICE dataset, which includes challenging cloud cover, CLEARNet achieves PSNRs of 36.873 dB on RICE1 and 34.624 dB on RICE2, improving over next-best models by approximately 18.2% and 12%, respectively, and attains the lowest LPIPS values, indicating superior perceptual quality. © 2008-2012 IEEE.

关键词： Demulsification

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Competitive Deep Learning Methods for COVID-19 Detection using X-ray Images

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Journal of The Institution of Engineers (India): Series B 2021年第6期102卷 1177-1190页

作者： Swapnarekha, H. Behera, Himansu Sekhar Roy, Debanik Das, Sunanda Nayak, Janmenjoy Department of Information Technology Veer Surendra Sai University of Technology BurlaOdisha768018 India Department of Robotics Engineering The Neotia University South 24 Pargana SarishaWest Bengal743368 India Department of Computer Science and Engineering Mody University SikarRajasthan India Department of Computer Science and Engineering Aditya Institute of Technology and Management TekkaliAndhra Pradesh532201 India

After the World War II, every country throughout the world is experiencing the biggest crisis induced by the devastating Coronavirus disease (COVID-19), which initially arose in the city of Wuhan in December 2019. This global pandemic has severely affected not only the health of billions of people but also the economy of countries all over the world. It has been evident that novel virus has infected a total of 20,674,903 lives as on 12 August 2020. The dissemination of the virus can be regulated by detecting the positive COVID cases as soon as possible. The reverse-transcriptase polymerase chain reaction (RT-PCR) is the basic approach used in the identification of the COVID-19. As RT-PCR is less sensitive to determine the novel virus at the beginning stage, it is worthwhile to develop more robust and other diagnosis approaches for the detection of the novel coronavirus. Due to the accessibility of medical datasets comprising of radiography images publicly, more robust diagnosis approaches are contributed by the researchers and technocrats for the identification of COVID-19 images using the techniques of deep leaning. In this paper, we proposed VGG16 and MobileNet-V2, which makes use of ADAM and RMSprop optimizers for the automatic identification of the COVID-19 images from other pneumonia chest X-ray images. Then, the efficiency of the proposed methodology has been enhanced by the application of data augmentation and transfer learning approach which is used to overcome the overfitting problem. From the experimental outcomes, it can be deduced that the proposed MobileNet-V2 model using ADAM and RMSprop optimizer achieves better accomplishment in terms of accuracy, sensitivity and specificity when contrasted with the VGG 16 using ADAM and RMSprop optimizers. © 2021, The Institution of Engineers (India).

关键词： COVID-19

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Towards Efficient Motion Planning for UAVs: Lazy A* Search with Motion Primitives

arXiv

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arXiv 2024年

作者： Wang, Wentao Shen, Yi Chen, Kaiyang Lu, Kaifan Department of Computer Science University of Southern California LA United States Robotics Department University of Michigan MI United States Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign Champaign United States

Search-based motion planning algorithms have been widely utilized for unmanned aerial vehicles (UAVs). However, deploying these algorithms on real UAVs faces challenges due to limited onboard computational resources. The algorithms struggle to find solutions in high-dimensional search spaces and require considerable time to ensure that the trajectories are dynamically feasible. This paper incorporates the lazy search concept into search-based planning algorithms to address the critical issue of real-time planning for collision-free and dynamically feasible trajectories on UAVs. We demonstrate that the lazy search motion planning algorithm can efficiently find optimal trajectories and significantly improve computational efficiency. © 2024, CC BY.

关键词： Trajectories

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Subsurface Feature-based Ground Robot/Vehicle Localization Using a Ground Penetrating Radar

Subsurface Feature-based Ground Robot/Vehicle Localization U...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Haifeng Li Jiajun Guo Dezhen Song Department of Computer Science Civil Aviation University of China Tianjin China Department of Robotics Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE Department of Computer Science and Engineering Texas A&M University TX USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Robot localization using subsurface features captured by Ground-Penetrating Radar (GPR) complements and improves robustness over existing common sensor modalities, as subsurface features are less sensitive to weather, season and surface scene changes. Here, we propose a novel subsurface feature-based localization method that uses only GPR measurements with a known subsurface map. An efficient feature descriptor, the dominant energy curve (DEC), is designed to identify different locations in cluttered conditions. Specifically, image processing techniques that involve background segmentation, energy point detection, and energy curve refinement are designed to extract DEC features from a 2D radargram. With DECs features obtained, a metric subsurface feature map is constructed. Finally, we perform robot localization by feature matching under a particle swarm optimization framework. We have implemented our method and tested it with the public CMU-GPR dataset. The results show that our algorithm improves accuracy and robustness with real-time performance for robot localization tasks. Specifically, the mean localization error is 0.50 m for all cases.

关键词： Location awareness Ground penetrating radar Feature extraction Robot sensing systems Robot localization Robustness Real-time systems

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Resilient distributed optimization under mobile malicious attacks

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IFAC-PapersOnLine 2023年第2期56卷 997-1002页

作者： Yuan Wang Changxin Liu Hideaki Ishii Karl Henrik Johansson Department of Robotics Hunan University Changsha China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and Digital Futures Stockholm Sweden Department of Computer Science Tokyo Institute of Technology Yokohama Japan

This article addresses the distributed optimization problem in the presence of malicious adversaries that can move within the network and induce faulty behaviors in the attacked nodes. We first investigate the vulnerabilities of a consensus-based secure distributed optimization protocol under mobile adversaries. Then, a modified resilient distributed optimization algorithm is proposed. We develop conditions on the network structure for both complete and non-complete directed graph cases, under which the proposed algorithm guarantees that the estimates by regular nodes converge to the convex combination of the minimizers of their local functions. Simulations are carried out to verify the effectiveness of our approach.

关键词： Fault-tolerant distributed optimization multi-agent systems network security mobile adversary agents

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LucidGrasp: Robotic Framework for Autonomous Manipulation of Laboratory Equipment with Different Degrees of Transparency via 6D Pose Estimation

LucidGrasp: Robotic Framework for Autonomous Manipulation of...

引用

IEEE International Conference on robotics and Biomimetics

作者： Maria Makarova Daria Trinitatova Qian Liu Dzmitry Tsetserukou Intelligent Space Robotics Laboratory Center for Digital Engineering Skolkovo Institute of Science and Technology (Skoltech) Moscow Russia Department of Computer Science and Technology Dalian University of Technology China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Many modern robotic systems operate autonomously, however they often lack the ability to accurately analyze the environment and adapt to changing external conditions, while teleoperation systems often require special operator skills. In the field of laboratory automation, the number of automated processes is growing, however such systems are usually developed to perform specific tasks. In addition, many of the objects used in this field are transparent, making it difficult to analyze them using visual channels. The contributions of this work include the development of a robotic framework with autonomous mode for manipulating liquid-filled objects with different degrees of transparency in complex pose combinations. The conducted experiments demonstrated the robustness of the designed visual perception system to accurately estimate object poses for autonomous manipulation, and confirmed the performance of the algorithms in dexterous operations such as liquid dispensing. The proposed robotic framework can be applied for laboratory automation, since it allows solving the problem of performing non-trivial manipulation tasks with the analysis of object poses of varying degrees of transparency and liquid levels, requiring high accuracy and repeatability.

关键词： Visualization Liquids Automation Accuracy Biomimetics Pose estimation Robustness Robots Visual perception

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Robust Control of Metric Discrete Event Systems Against Bounded Disturbances

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IFAC-PapersOnLine 2024年第1期58卷 108-113页

作者： Yiding Ji Xiang Yin Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Automation Shanghai Jiao Tong University Shanghai China

This work investigates robust supervisory control problems of discrete event systems modeled as finite state automata equipped with metric functions to measure the distance between states. The system may deviate from its nominal behaviors and fail to achieve the specification under disturbances whose effects are considered to be bounded. Accordingly, the supervisor should be designed to ensure that the controlled system degrades gracefully against such adversary. We formally formulate two problems: robustness bound verification and optimal robust supervisor synthesis. For the special case of verification under constant disturbances, a control Lyapunov function approach is introduced. Then we develop a two player game framework for the general verification and synthesis problems. Specifically, a dynamic programming method is proposed on the game structure, which uniformly tackles both problems.

关键词： Metric discrete event systems supervisory control robustness two player games

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Expanding the Surgical robotics Community: An Intuitive Sim-to-Real Control Framework for Raven-II with a Budget-Friendly Gamepad Controller

Expanding the Surgical Robotics Community: An Intuitive Sim-...

引用

International Symposium on Medical robotics (ISMR)

作者： Mai Bui Natalie Chalfant Cuiling Sun Sean Fabrega Haonan Peng Kevin Huang Yun-Hsuan Su Department of Computer Science Mount Holyoke College South Hadley MA Department of Electrical and Computer Engineering University of Washington Seattle WA Picker Engineering Program Smith College Northampton MA

ISBN: (数字)9798350377118

ISBN: (纸本)9798350377125

This paper proposes a low-cost interface and refined digital twin for the Raven-II surgical robot. Previous simulations of the Raven-II, e.g. via the Asynchronous Multibody Framework (AMBF), presented salient drawbacks, including control inputs inconsistent with Raven-II software, and lack of stable, high-fidelity physical contact simulations. This work bridges both of these gaps, both (1) enabling robust, simulated contact mechanics for dynamic physical interactions with the Raven-II, and (2) developing a universal input format for both simulated and physical platforms. The method furthermore proposes a low cost, commodity game-controller interface for controlling both virtual and real realizations of Raven-II, thus greatly reducing the barrier to access for Raven-II research and collaboration. Overall, this work aims to eliminate the inconsistencies between simulated and real representations of the Raven-II. Such a development can expand the reach of surgical robotics research. Namely, providing end-to-end transparency between the simulated AMBF and physical Raven-II platforms enables a software testbed previously unavailable, e.g. for training real surgeons, for creating digital synthetic datasets, or for prototyping novel architectures like shared control strategies. Experiments validate this transparency by comparing joint trajectories between digital twin and physical testbed given identical inputs. This work may be extended and incorporated into recent efforts in developing modular or common software infrastructures for both simulation and control of real robotic devices, such as the Collaborative robotics Toolkit (CRTK).

关键词： Training Medical robotics Costs Collaboration Surgery computer architecture Software

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