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IEEE International Conference on Soft robotics (RoboSoft)

作者： Monica S. Li Brian H. Do Caitlin L. Le Corey S. O’Hern Rebecca Kramer-Bottiglio Department of Mechanical Engineering & Materials Science Yale University New Haven CT USA Collaborative Robotics and Intelligent Systems (CoRIS) Institute Oregon State University Corvallis OR USA

ISBN: (数字)9798331520205

ISBN: (纸本)9798331520212

Soft robots can achieve exceptional adaptability through tunable morphological and mechanical properties. Incorporating materials with dynamically adjustable characteristics can enhance this versatility further. Granular meta-materials, consisting of discrete particles with individually variable properties, offer a promising approach to bulk property adaptation by adjusting the properties of constituent particles. This work introduces variable size and variable stiffness (VS 2 ) particles, in which both particle size and stiffness are independently modulated through concentric pneumatic chambers. We characterize the achievable workspace, mapping particle responses to independent chamber inflation. To demonstrate their use in a granular assembly, we arrange an array of VS 2 particles in a hexagonal packing and validate that behavior in packed configurations aligns with free-space characterizations. This study establishes a foundation for adaptive granular materials and provides a platform for further computational and experimental exploration of 2D and 3D granular metamaterials with tunable properties.

关键词： Fabrication Three-dimensional displays Shape Windings Soft robotics Metamaterials Wrapping Probes Assembly Stress

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A research on optimization of fog estimation process in Peplography

A research on optimization of fog estimation process in Pepl...

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International Conference on Control, Automation and systems ( ICCAS)

作者： Seiya Ono Jin-Ung Ha Hyun-Woo Kim Myungjin Cho Min-Chul Lee Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Fukuoka Japan School of ICT Robotics and Mechanical Engineering IITC Hankyong National University Anseong Republic of Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

In recent years, imaging techniques are essential in our lives, such as security cameras and drive recorders. However, the visualization quality of these techniques sometimes is affected by scattering media, such as smoke at a fire or fog during driving. To solve this problem, Peplography has been proposed. Peplography is an optical algorithm that emphasizes the object information presented in the scattering media, even when the density of the scattering media varies within the image. On the other hand, Peplography estimates the scattering media in each local region, and when the region is not defined appropriately, the estimation of the scattering media will not be visualized. Therefore, the purpose of this research is to find the optimal processing for each environment by experiment with various situations, such as when the size of the object in the image is small, when the density of the scattering media is heavy, and when the target object is various.

关键词： YOLO Visualization Optical recording Scattering Process control Estimation Media Security Optical scattering Optimization

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Kalman filtering optimization in Digital Holographic Microscopy (DHM)

Kalman filtering optimization in Digital Holographic Microsc...

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International Conference on Control, Automation and systems ( ICCAS)

作者： Taishi Ono Jongpil Jeong Hyun-Woo Kim Myungjin Cho Min-Chul Lee Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Fukuoka Japan School of ICT Robotics and Mechanical Engineering IITC Hankyong National University Anseong Republic of Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

Digital Holographic Microscopy (DHM) can obtain a 3D profile of a micro-object. However, the obtained 3D profile includes the noise. One reason for this is the problem of the extraction method of phase information in DHM. Conventional methods cannot extract only the phase information from the Fourier domain, and noise components are included. To solve this problem, we propose a use of the Kalman filter in DHM systems. In this method, multiple frequency components are obtained from the Fourier domain as time-series data for Kalman filtering. 3D profiles are obtained from each frequency component and stored in an array as time-series data. This is used to remove randomly generated noise. In this process, it remains unclear how many 3D profiles obtained from frequency components are needed as time-series data. Therefore, the optimization of the number of windowed sidebands in the Fourier domain is the objective in this paper. The objective is to use the Kalman filter efficiently by performing quantitative evaluation using image quality metrics and optimization based on processing time.

关键词： Measurement Time-frequency analysis Three-dimensional displays Accuracy Filtering Microscopy Noise Kalman filters Data mining Optimization

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Flying is for Droids: A Survey of Research into Generating Certification Evidence for AI/ML Algorithms to Replace Human Pilots

Flying is for Droids: A Survey of Research into Generating C...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Donald Costello Huan Xu Weapons Robotics and Control Engineering United States Naval Academy Annapolis Maryland United States Aerospace Engineering/Institute for Systems Research University of Maryland College Park Maryland United States

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

While academia and industry have shown that they can develop systems that exhibit high levels of autonomy across multiple domains (air, sea, and land systems), as of now, regulations do not exist to allow a system to operate autonomously (without a human in or on the loop to ultimately be accountable for the actions of the system). Without these regulations, a truly autonomous system will remain a demon-stration program or simply science fiction. The United States Navy has publicly announced that it intends to dramatically increase its fleet of uncrewed aerial systems. To support an ongoing effort for fielding autonomy, researchers have been trying to define the problem and identify possible solutions. This survey paper details the work that has gone into generating certification evidence for artificial intelligence/machine learning algorithms to replace humans within naval aviation. It is hoped that the lessons learned and standards developed can be used to spread autonomous functionality to other domains.

关键词： Surveys Industries Stars Learning (artificial intelligence) Military aircraft Regulation Safety

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EFFECTS OF VISUAL DISPLAY MODALITY ON SIMULATED ON- ORBIT INSPECTION PERFORMANCE: INITIAL RESULTS FROM HUMAN EXPLORATION RESEARCH ANALOG CAMPAIGN 6 66th

EFFECTS OF VISUAL DISPLAY MODALITY ON SIMULATED ON- ORBIT IN...

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66th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2022

作者： Liu, Andrew M. Weiss, Hannah Stirling, Leia Human Systems Laboratory Massachusetts Institute of Technology CambridgeMA United States Industrial and Operations Engineering University of Michigan Ann ArborMI United States Robotics Institute University of Michigan Ann ArborMI United States

To reduce risk in future human spaceflights, free-flying teleoperated inspector robots could be a viable alternative to extravehicular activity inspection operations. Teleoperation depends on accurate operator situation awareness;consequently, a key to successful operations is providing appropriate feedback through an intuitive interface. During each week of a 45-day mission in NASA Human Exploration Research Analog Campaign 6, four crew members perform two simulated telerobotic inspection tasks (Synchronous Inspection task, Asynchronous Inspection task) while using three distinct visual displays (2D, 3D, AR). Preliminary results from Campaign 6 Mission 1 suggest that task performance and efficiency generally improved with each repetition of the task. Performance with the AR display did not show a performance benefit compared to using the 2D or 3D displays. These results are also in general agreement with a prior laboratory study using the same experiment protocol. © 2022 by Human Factors and Ergonomics Society. All rights reserved.

关键词： NASA

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Late-Fusion Multimodal Human Detection Based on RGB and Thermal Images for Robotic Perception

Late-Fusion Multimodal Human Detection Based on RGB and Ther...

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European Conference on Mobile Robots (ECMR)

作者： Elísio Sousa Kennedy O. S. Mota Iago P. Gomes Luís Garrote Denis F. Wolf Cristiano Premebida Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Portugal Institute of Mathematics and Computer Science University of São Paulo Brazil

This paper addresses the problem of detecting humans in RGB and Thermal (long-wave IR) images taken by cameras mounted onboard a mobile robot. Human/Pedestrian detection is currently one of the most pertinent object detection problems, mainly due to safety concerns in autonomous vehicles. The majority of approaches apply deep-learning techniques based solely on RGB images. However, they have a few shortcomings, namely that during foggy weather, nighttime, and low-light scenarios, these images may not contain sufficient information. To address these issues, this work studies the use of thermal cameras as a complementary source of information for human detection in indoor and outdoor environments. The proposed approach uses YOLOv5 to detect pedestrians in both thermal and RGB images. Moreover, the different modalities are combined using early and late fusion techniques. Evaluation of the proposed approach is carried out in the FLIR Aligned dataset and in a new in-house dataset. Results indicate that the use of fusion techniques highlights a promising way to improve the overall performance in this application domain.

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Interaction-aware Maneuver Prediction for Autonomous Vehicles using Interaction Graphs

Interaction-aware Maneuver Prediction for Autonomous Vehicle...

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IEEE Symposium on Intelligent Vehicle

作者： Iago Pachêco Gomes Cristiano Premebida Denis Fernando Wolf Institute of Mathematics and Computer Science University of São Paulo Brazil Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Portugal

Intention prediction (IP) is a challenging task for intelligent vehicle’s perception systems. IP provides the likelihood, or probability, of a target vehicle to perform a maneuver subjected to a finite set of possibilities. There are many factors that influence the decision-making process of a driver, which should be considered in a prediction framework. In addition, the lack of labeled large-scale dataset with maneuver annotation imposes another challenge to the task. This paper proposes an Interaction-aware Maneuver Prediction framework, called IAMP, using interaction graphs to extract complex interaction features from traffic scenes. In addition, we explored a semi-supervised approach called Noisy Student to take advantage of unlabeled data in the training step. Experimental results show relevant improvement when using unlabeled data, increasing the average performance of a classifier by 7.17% of accuracy. Moreover, this approach also made it possible to obtain an intention predictor with similar results to a classifier., even when using a shorter observation horizon.

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Automated Disassembly Sequence Prediction for Industry 4.0 Using Enhanced Genetic Algorithm

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Computers, Materials & Continua 2021年第11期69卷 2531-2548页

作者： Anil Kumar Gulivindala M.V.A.Raju Bahubalendruni R.Chandrasekar Ejaz Ahmed Mustufa Haider Abidi Abdulrahman Al-Ahmari Department of Mechanical Engineering Industrial Robotics and Manufacturing Automation LaboratoryNational Institute of TechnologyKariakal609609India Department of Computer Science Engineering National Institute of TechnologyKariakal609609India Raytheon Chair for Systems Engineering Advanced Manufacturing InstituteKing Saud UniversityRiyadh11421Saudi Arabia Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The evolution of Industry 4.0 made it essential to adopt the Internet of Things(IoT)and Cloud Computing(CC)technologies to perform activities in the new age of *** technologies enable collecting,storing,and retrieving essential information from the manufacturing *** collected at sites are shared with others where execution automatedly *** obtained information must be validated at manufacturing to avoid undesirable data losses during the de-manufacturing ***,information sharing from the assembly level at the manufacturing stage to disassembly at the product end-of-life state is a major *** current research validates the information optimally to offer a minimum set of activities to complete the disassembly *** optimal disassembly sequence plan(DSP)can possess valid information to organize the necessary actions in ***,finding an optimal DSP is complex because of its combinatorial *** genetic algorithm(GA)is a widely preferred artificial intelligence(AI)algorithm to obtain a near-optimal solution for the DSP *** converging nature at local optima is a limitation in the traditional *** study improvised the GA workability by integrating with the proposed priori crossover *** optimality function is defined to reduce disassembly effort by considering directional changes as *** enhanced GA method is tested on a real-time product to evaluate the *** obtained results reveal that diversity control depends on the operators employed in the disassembly *** proposed method’s solution can be stored in the cloud and shared through IoT devices for effective resource allocation and disassembly for maximum recovery of the *** effectiveness of the proposed enhanced GA method is determined by making a comparative assessment with traditional GA and other AI methods at different population sizes.

关键词： Automation internet of things disassembly sequence planning priori cross over operator enhanced GA disassembly predicates

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Renderable Street View Map-Based Localization: Leveraging 3D Gaussian Splatting for Street-Level Positioning

Renderable Street View Map-Based Localization: Leveraging 3D...

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

作者： Howoong Jun Hyeonwoo Yu Songhwai Oh Artificial Intelligence (IPAI) Seoul National University and Automation and Systems Research Institute (ASRI) and Sequor Robotics Inc. Seoul Korea Republic of Department of Intelligent Robotics & Mechanical Engineering Sungkyunkwan University (SKKU) Suwon-si Korea Republic of Department of Electrical and Computer Engineering (ECE) & Interdisciplinary Program in Artificial Intelligence (IPAI) Seoul National University and Automation and Systems Research Institute (ASRI) and Sequor Robotics Inc. Seoul Korea Republic of

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In this paper, we introduce a new method that first utilizes 3D Gaussian splatting in street-level localization problem. Robust localization with street-level real-world images such as street view is a major issue for autonomous vehicle, augmented reality (AR) navigation, and outdoor mobile robots. The objective is to determine the position and orientation of a query image that matches a street view database composed of RGB images. However, given the limited information available in the street view images, accurately determining the location solely based on this data presents a significant challenge. To address this challenge, we propose a novel method called renderable street view map-based localization (RSM-Loc). This approach enhances the localization process by augmenting 2D street view images into a renderable 3D map using 3D Gaussian splatting, to resolve street-level localization problems. Upon receiving a query RGB image without geometry information, the proposed method renders 2D images from a pre-made renderable map and compares image pose similarities between the rendered images and the query image. Through iterations of this process, the proposed method eventually estimates the pose of the given query image. The experimental results demonstrate that RSM-Loc outperforms the baselines with neural-field-based localization. Additionally, we conduct deep analysis on the proposed method to show that our method can serve as a new concept for the street-level localization problem.

关键词： Location awareness Visualization Matched filters Three-dimensional displays Image resolution Navigation Noise measurement Mobile robots Intelligent robots Image reconstruction

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A Novel Mobile Robot Localization Method Based on Global Vision System 18th

A Novel Mobile Robot Localization Method Based on Global Vi...

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18th Chinese Intelligent systems Conference, CISC 2022

作者： He, Yu Cheng, Lei Wang, Kunkun Ding, Ao School of Information Science and Engineering/School of Artificial Intelligence Wuhan University of Science and Technology Wuhan430010 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan430010 China

ISBN: (纸本)9789811962028

Localization is the primary problem of mobile robot navigation. Most of the existing research on mobile robot localization algorithms is based on LiDAR or stereo cameras. Each of these methods requires the necessary sensors to be installed on the robot. These sensors are expensive and have high requirements for mounting locations, which has caused inconvenience to the design of the robot. In this paper, we propose a robot localization method based on a global vision system. A deep learning-based object detection algorithm is used to identify the robot’s position on the image from a third-person view monocular camera. Then we map and correct the coordinate of the detection results to obtain the world coordinate of robots. Experiments have demonstrated that our method can achieve an average localization accuracy of 0.14 m while having real-time performance. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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