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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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Robot-Assisted Wide-Area Photoacoustic System

Robot-Assisted Wide-Area Photoacoustic System

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Gao, Shang Ma, Xihan Zhang, Haichong K. Worcester Polytechnic Institute Department of Robotics Engineering Worcester United States Worcester Polytechnic Institute Department of Biomedical Engineering Worcester United States Worcester Polytechnic Institute Department of Computer Science Worcester United States

ISBN: (纸本)9798350346459

Liver ischemia and reperfusion injury (IRI) is a vascular complication that can lead to hepatic functional impairment. IRI is relevant in liver transplantation and during liver surgery performed with intermittent vascular inflow occlusion. Detecting intraoperative liver oxygenation impairment and ischemia could help prevent IRI, which is challenging partially due to the multiple hepatic vascular inflows. We propose a robot-assisted wide-area photoacoustic (PA) imaging system to monitor the liver oxygenation level intraoperatively. PA imaging has been widely used for oxygenation detection in preclinical research. We used a robotic arm to actuate the 2D PA probe and scan across the liver surface. An accurate volumetric tomography can be reconstructed via registering 2D images to the recorded robot trajectory. An ex vivo porcine liver sample was scanned using the proposed system. The result suggested that the robot-assisted wide-area PA tomography has the potential to be incorporated into clinical practice to evaluate liver SO2 intraoperatively. © 2023 IEEE.

关键词： Photoacoustic effect

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A Framework and Environment Design of Human-Machine Cooperations in Junior science Education 8

A Framework and Environment Design of Human-Machine Cooperat...

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8th International Symposium on System Security, Safety, and Reliability, ISSSR 2022

作者： Meng, Sa Luo, Liang Liu, Jun Dai, Yuanshun School of Computing and Artificial Intelligence Southwest Jiaotong University Sichuan Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA United States

ISBN: (数字)9781665453974

ISBN: (纸本)9781665453974

Human-Machine-Environment (HME) Tri-Co (Coexisting, Cooperative, Cognitive) Education is an indispensable prerequisite and main content of Artificial Intelligence empowering junior science education. Moreover, it is also a critical requirement for improving the quality of education and promoting the high-end development of education information. Building a unique learning environment with scene perception and the intelligent cognitive process is a tremendous challenge for diverse needs in junior scientific learning scenarios. Therefore, it is necessary to design advanced human-computer interaction scenarios and functions. This paper aims to tackle the critical scientific problem of this field for HME Tri-Coeducation, i.e., HME multimodal real-time scene perception and Tri-Co collaborative analysis via Multi-source data, fundamental theories, application method, and verification system, and to build HME Tri-Coeducation environment in 'Perception-Analysis-Interaction' architecture. For the requirements of an education environment empowered by artificial intelligence, this paper will also study critical technologies of human-machine cooperative and education environment building driven by big data. It is significant to promote the cross-integration of multi-disciplines such as pedagogy, intelligent technology, and cognitive science. © 2022 IEEE.

关键词： Human computer interaction

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A Study on Coherence Length and Interferometry in Digital Holographic Microscopy 23

A Study on Coherence Length and Interferometry in Digital Ho...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Koga, Takahiro Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Fukuoka Iizuka820-8502 Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Kyonggi-do Anseong17579 Korea Republic of

ISBN: (纸本)9788993215274

Digital Holographic Microscopy (DHM) can display 3D profiles of microscopic objects and is expected to be used in a wide range of fields such as microbiology, microstructural investigation, and disease diagnosis. Since these fields require detailed information of the object, acquiring an accurate 3D profile is the most important. However, DHM requires several experimental equipments, and it does not define appropriate lasers for interferometers. Therefore, in this paper, we implement an experiment and find the optimum laser for the most accurate 3D profiles of interferometers by comparing 3D profiles using two types of interferometers and three types of lasers with different wavelengths. The results show that the relationship between the laser's coherence length and the optical path length of the interferometer is related to the accuracy of the 3D profiles. They also show that the accuracy of the 3D profile is higher when the coherence length is longer than the optical path length plus twice the height of the object. © 2023 ICROS.

关键词： Computational Imaging

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Improvement of Depth Mapping and Occlusion Removal Method Using Integral Imaging 23

Improvement of Depth Mapping and Occlusion Removal Method Us...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Sakanoue, Haruka Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Fukuoka Iizuka820-8502 Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Kyonggi-do Anseong17579 Korea Republic of

ISBN: (纸本)9788993215274

In this paper, we propose the depth mapping and occlusion removal method using integral imaging (InIm). InIm is a method to obtain 3D information by capturing and reconstructing an object from multiple viewpoints. InIm can remove occlusion using parallax obtained by photographing an object from multiple viewpoints. However, occlusion removal uses a unit matrix to calculate the number of overlaps, which results in an overall darkening of the image. To solve this problem, this paper aims to improve occlusion removal by using the matrix of the mask image to calculate the number of overlaps. InIm can provide depth mapping that shows the distance from an object as pixel values. However, when there is an occlusion in front of the object, it is difficult to provide depth mapping object due to the influence of the occlusion. To solve this problem, we propose a method of depth mapping and occlusion removal method using InIm. Our method is expected to make great progress in improving safety in the field of autonomous driving. © 2023 ICROS.

关键词： Mapping

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Photon-Counting Integral Imaging Using Bayesian Estimation with Depth Camera Information 23

Photon-Counting Integral Imaging Using Bayesian Estimation w...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Uchino, Hideaki Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Fukuoka Iizuka820-8502 Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Kyonggi-do Anseong17579 Korea Republic of

ISBN: (纸本)9788993215274

In this paper, we propose a method for visualizing object information under low light conditions using photon-counting integral imaging and depth images as prior information. To visualize 3D objects under low-light conditions, computational photon-counting imaging may be used. It is possible to estimate photons using statistical methods such as the Poisson distribution. Therefore, photon-counting integral imaging can visualize 3D images under low-light conditions by maximum likelihood estimation (MLE). However, MLE does not consider prior information so it may not be an accurate estimation. In addition, it is difficult to obtain accurate depth information as the distance increases due to rounding errors caused by computational reconstruction. Therefore, we propose Bayesian estimation such as a maximum a posteriori estimation method that uses as a priori information images from a depth camera, which can be acquired even in low-light conditions, to improve the quality of 3D images. To evaluate image quality of the proposed method, optical experiments were conducted by calculating image quality metrics. This technology can be utilized for night vision applications, such as object recognition in security camera systems and autonomous driving, using RGB cameras and depth cameras. © 2023 ICROS.

关键词： Maximum likelihood estimation

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DyPNIPP: Predicting Environment Dynamics for RL-based Robust Informative Path Planning

arXiv

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arXiv 2024年

作者： Deolasee, Srujan Kailas, Siva Luo, Wenhao Sycara, Katia Kim, Woojun Robotics Institute Carnegie Mellon University United States Department of Computer Science Georgia Institute of Technology United States Department of Computer Science University of North Carolina at Charlotte United States

Informative path planning (IPP) is an important planning paradigm for various real-world robotic applications such as environment monitoring. IPP involves planning a path that can learn an accurate belief of the quantity of interest, while adhering to planning constraints. Traditional IPP methods typically require high computation time during execution, giving rise to reinforcement learning (RL) based IPP methods. However, the existing RL-based methods do not consider spatio-temporal environments which involve their own challenges due to variations in environment characteristics. In this paper, we propose DyPNIPP, a robust RL-based IPP framework, designed to operate effectively across spatio-temporal environments with varying dynamics. To achieve this, DyPNIPP incorporates domain randomization to train the agent across diverse environments and introduces a dynamics prediction model to capture and adapt the agent actions to specific environment dynamics. Our extensive experiments in a wildfire environment demonstrate that DyPNIPP outperforms existing RL-based IPP algorithms by significantly improving robustness and performing across diverse environment conditions. Copyright © 2024, The Authors. All rights reserved.

关键词： Robot programming

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Targeted Image Transformation for Improving Robustness in Long Range Aircraft Detection

Targeted Image Transformation for Improving Robustness in Lo...

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

作者： Rebecca Martin Clement Fung Nikhil Keetha Lujo Bauer Sebastian Scherer School of Computer Science Robotics Institute Carnegie Mellon University Pittsburgh PA USA School of Computer Science Software and Societal Systems Department Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In the field of aviation, the Detect and Avoid (DAA) problem deals with incorporating collision avoidance capabilities into current autopilot navigation systems. As an application of the Small Object Detection (SOD) problem, DAA presents the difficulties of a low signal-to-noise ratio and far range detection. Visual DAA is also susceptible to changing weather and lighting conditions at deployment. While current literature has presented many solutions for this, prior work has yet to study the robustness of the learning-based models for DAA. In this work, we show that standard techniques for improving robustness for object detection do not produce the desired results for DAA given the SOD constraints. We present targeted transformations, a zero-shot technique that can significantly improve robustness with minimal impact on accuracy. We demonstrate how to construct these transformations and evaluate our method on the current SOTA model for DAA, showing a 53.6% increase in recall. This makes our pipeline more robust to changes in lighting and environmental factors, and better able to detect potential threats. In the future, we hope to automate the transformation selection process, making it easier to adopt in different use cases.

关键词： Visualization Navigation Pipelines Lighting LoRa Object detection Robustness Standards Signal to noise ratio Meteorology

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Noise Removal in Scattering Media Enviorment Using Peploaraphy and DCLGAN 23

Noise Removal in Scattering Media Enviorment Using Peploarap...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Numata, Riki Kim, Hyun-Woo Ono, Seiya Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Fukuoka Iizuka820-8502 Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Kyonggi-do Anseong17579 Korea Republic of

ISBN: (纸本)9788993215274

Noise removal in scattering media environments such as fog and smoke are an important task in reality. For example, it can be applied in many fields such as searching under turbid water and rescuing in smoke. Therefore, the purpose of this research is to remove the noise under the scattering medium environment and to visualize the object. As a conventional method for removing noise, there is a technique called peplography. Peplograhy enables the denoising of dense fog and reconstructing the image in scattering media environments. However, peplography has a problem that complex noise exists after removing particularly deep noise, and as a result, it becomes difficult to visualize the object. This research aims to solve this problem. We propose a new denoising technology that combines peplography with DCLGAN. DCLGAN is an image transformation algorithm using deep learning and an improved model of CycleGAN which is also used in the field of noise reduction. In this study, we evaluate the conventional method and the proposed method for scattering media images. As the evaluation method, visual evaluation and numerical evaluation using the PSNR index are performed to demonstrate the effectiveness of the proposed method. We believe that the proposed method can be applied in various fields such as object visualization under turbid water and automatic driving technology with fog removed. © 2023 ICROS.

关键词： Smoke

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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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