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Proceedings of the 12th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2023

作者： Salazar, Pablo J. Prescott, Tony J. Department of Computer Science and Sheffield Robotics University of Sheffield SheffieldS1 4DP United Kingdom

ISBN: (纸本)9783031388569

It has been previously demonstrated in robots that the mimicking of functional characteristics of biologic memory can be beneficial for providing accurate learning and recognition in circumstances of social human-robot-interaction. The effective encoding of social and physical salient features has been demonstrated through the use of Bayesian Latent Variable Models as abstractions of memories (Simple Synthetic Memories). In this work, we explore the capabilities of formation and recall of tactile memories associated to the encoding of geometric and spatial qualities. Compression and pattern separation are evaluated against the use of raw data in a nearest neighbour regression model, obtaining a substantial improvement in accuracy for prediction of geometric properties of the stimulus. Additionally, pattern completion is assessed with the generation of ‘imagined touch’ streams of data showing similarities to real world tactile observations. The use of this model for tactile memories offers the potential for robustly perform sensorimotor tasks in which the sense of touch is involved. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Encoding (symbols)

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Single-Shot VR 23

Single-Shot VR

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Special Interest Group on computer Graphics and Interactive Techniques Conference - Emerging Technologies, SIGGRAPH 2023

作者： Qin, Yingsi Chen, Wei-Yu O'Toole, Matthew Sankaranarayanan, Aswin C. Image Science Lab at ECE Department Carnegie Mellon University United States Robotics Institute & Computer Science Department Carnegie Mellon University United States

ISBN: (纸本)9798400701542

The physical world has contents at varying depths, allowing our eye to squish or relax to focus at different distances;this is commonly referred to as the accommodation cue for human eyes. To allow a realistic 3D viewing experience, it is crucial to support the accommodation cue - the 3D display needs to show contents at different depths. However, supporting the native focusing of the eye has been an immense challenge to 3D displays. Commercial near-eye VR displays, which use binocular disparity as the primary cue for inducing depth perception, fail this challenge since all contents they show arise from a fixed depth - ignoring the focusing of the eye. Many research prototypes of VR displays do account for the accommodation cue;however, supporting accommodation cues invariably comes with performance loss among other typically assessed criteria for 3D displays. To tackle these challenges, we present a novel kind of near-eye 3D display that can create 3D scenes supporting realistic accommodation cues in a single shot, i.e., without using time multiplexing or eye tracking. This display, which we present in our demo, can stream 3D content over a large depth range, at 4K spatial resolution, and in real-time. Our display offers an exciting step forward towards a truly immersive real-time 3D experience. Participants will get to enjoy 3D movies and play interactive games in their demo experience. © 2023 Owner/Author.

关键词： Three dimensional displays

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Maximizing Resource Efficiency in Cloud Data Centers through Knowledge-Based Flower Pollination Algorithm (KB-FPA)

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computers, Materials & Continua 2024年第6期79卷 3757-3782页

作者： Nidhika Chauhan Navneet Kaur Kamaljit Singh Saini Sahil Verma Kavita Ruba Abu Khurma Pedro A.Castillo University Institute of Computing Department Chandigarh UniversityPunjab140413India Department of Computer Science and Engineering Chandigarh UniversityPunjab140413India Universidade Federal do Piauí TeresinaPiauí64049-550Brazil MEU Research Unit Faculty of Information TechnologyMiddle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Department of Computer Engineering Automatics and RoboticsUniversity of GranadaGranada18071Spain

Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates *** paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing *** motivation stems from the pressing issue of accommodating fluctuating levels of user demand *** adhering to the proposed resource allocation method,we aim to achieve a substantial reduction in energy *** reduction hinges on the precise and efficient allocation of resources to the tasks that require those most,aligning with the broader goal of sustainable and eco-friendly cloud computing *** enhance the resource allocation process,we introduce a novel knowledge-based optimization *** this study,we rigorously evaluate its efficacy by comparing it to existing algorithms,including the Flower Pollination Algorithm(FPA),Spark Lion Whale Optimization(SLWO),and Firefly *** findings reveal that our proposed algorithm,Knowledge Based Flower Pollination Algorithm(KB-FPA),consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption *** paper underscores the profound significance of resource allocation in the realm of cloud *** addressing the critical issue of adaptability and energy efficiency,it lays the groundwork for a more sustainable future in cloud computing *** contribution to the field lies in the introduction of a new resource allocation strategy,offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures.

关键词： Cloud computing resource allocation energy consumption optimization algorithm flower pollination algorithm

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Contact-Rich Task Learning on an Articulated Soft Robot Arm Through Simulation 25th

Contact-Rich Task Learning on an Articulated Soft Robot Ar...

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25th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2024

作者： Elstner, Laurenz Kyrkjebø, Erik Stoelen, Martin F. Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Førde Norway Fieldwork Robotics Ltd. Cambridge United Kingdom

ISBN: (纸本)9783031720611

Traditional rigid robots face limitations in exploration during learning processes in dynamic and unknown environments where contact and impacts are common. Soft robots that leverage compliant materials, for example as part of variable stiffness actuators (VSAs), offer promising avenues for enhanced adaptability and safety. Deep Reinforcement Learning (RL), while proven to be a powerful tool, requires a lot of training data that is time-consuming to gather. This paper introduces a novel approach to simulating robots driven by antagonistic VSAs, as well as full-task learning with a soft robot arm in three environments with different contact types: path following (no contact), door opening (kinematic constraint), and surface wiping (continuous contact). We show that the tendon-driven robot can learn all three tasks, even if learning in a very low-level actuation space. The stretchable tendons enables it to perform relatively well on the contact-rich tasks. Further, using the proposed variable stiffness controller the robot is able to develop a stable behaviour in terms of forces applied to the environment. The results are promising for extending the approach to the physical robot. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Tendons

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Deep Prior Based Limited-Angle Tomography 27th

Deep Prior Based Limited-Angle Tomography

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Bappy, D.M. Kang, Donghwa Lee, Jinkyu Lee, Youngmoon Baek, Hyeongboo Department of Computer Science and Engineering Incheon National University Incheon Korea Republic of Department of Computer Science and Engineering Sungkyunkwan University Suwon Korea Republic of Department of Robotics Hanyang University Ansan Korea Republic of Department of Artificial Intelligence University of Seoul Seoul Korea Republic of

ISBN: (纸本)9783031781940

In the process of reconstructing images from data acquired within a limited angular range, we encounter what is termed limited-angle tomography. The deficiency of complete data in this context results in artifacts, commonly appearing as streaks or missing structures, which can significantly compromise the quality of the reconstructed slice. This degradation gives rise to issues such as boundary distortion, blurred edges, and intensity bias, potentially leading to misinterpretation of the images. Hence, addressing artifacts in limited-angle tomography is crucial for clinical applications. Although deep learning-based reconstruction has shown impressive results in recent times, concerns about its robustness persist. To bolster the robustness of our proposed technique, we integrate prior information from a modified U-net with preprocessed input into the Relative Variation - Simultaneous Algebraic Reconstruction Technique (RV-SART) to provide insights into unmeasured data. Subsequently, the method extracts structure from the initially reconstructed slice through structure-texture decomposition. This process facilitates the reconstruction of high-quality CT images while suppressing pattern-like artifacts. Extensive experiments demonstrate that our approach surpasses both traditional and state-of-the-art learning techniques in terms of reconstruction quality and preservation of fine structures in noisy limited-angle reconstruction problems. Our technique provides improvements over the recent LRIP-net for a 90-degree scanning range in quantitative metrics such as PSNR by 17.48%, RMSE by 46.36%, and SSIM by 6.18%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image reconstruction

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Advanced Endoscopy Imaging with Automatic Feedback 27th

Advanced Endoscopy Imaging with Automatic Feedback

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Bappy, D.M. Kang, Donghwa Lee, Jinkyu Lee, Youngmoon Koo, Minsuk Baek, Hyeongboo Department of Computer Science and Engineering Incheon National University Incheon Korea Republic of Department of Computer Science and Engineering Sungkyunkwan University Suwon Korea Republic of Department of Robotics Hanyang University Ansan Korea Republic of Department of Artificial Intelligence University of Seoul Seoul Korea Republic of

ISBN: (纸本)9783031781940

As we move towards a future where minimally invasive methods become the norm for surgeries and diagnostic procedures, it is increasingly vital to improve our strategies for viewing the organs and complex structures within our bodies. Image stitching presents an enticing solution, expanding our field of view by seamlessly weaving together a sequence of images. While existing stitching techniques do lean on the capabilities of endoscopy imaging, they, unfortunately, overlook the critical need for automated feedback when grappling with the complexities and challenges innate to endoscopy imaging. these methods struggle to stand firm against deformations and regions with low texture. In this paper, we introduce a robust endoscopic image-stitching algorithm designed to thrive in adversity. Its unique resilience to deformations and low-texture regions is reinforced by the inclusion of a radial basis function weighting that is paired harmoniously with location-dependent homography based on the corresponding locations of the strong features extracted by affine shape-adapted Hessian-Laplace detector. Crucially, this algorithm is steered by a sophisticated automatic feedback mechanism. This feedback system makes astute evaluations based on an image quality metric and the structural comparison between the sequences of endoscopy images. We have thoroughly validated the efficacy of our new approach using two public datasets, namely EndoSLAM and EndoAbS, under demanding conditions. The results eloquently illustrate the superior benefits of our technique. Our proposed method surpasses commonly employed techniques, delivering superior performance in quantitative metrics, including precision at 30.07%, recall at 114.89%, F1-score at 84.62%, and TRE at 46.07%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Endoscopy

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Automatic Heliostat Directing System Based On Microcontroller ESP32

Automatic Heliostat Directing System Based On Microcontrolle...

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2023 International Conference on Electrical Engineering and Informatics, ICEEI 2023

作者： Medyanto, Richard Sanjaya, Nicholas Liawatimena, Suryadiputra Binus Aso School of Engineering Bina Nusantara University Automotive & Robotics Program Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350381290

Solar energy is one of the most abundant sources of renewable energy in Indonesia. Solar energy is now typically harnessed using solar panels, but the low efficiency of photovoltaic cells requires the development of other alternatives. The heliostat is a sunlight directing device with mirrors that can be used in a concentrated solar power system. Current heliostats require high capital investment due to their large frames and expensive components. This research was undertaken to develop a lower cost heliostat using a smaller frame, ESP32 microcontroller, servo motor and low-cost components. The position of the sun can be determined using an algorithm based on the National Oceanic and Atmospheric Administration (NOAA) solar calculator, and the mirror is moved to maintain the sun's reflection on a target. The result of this research is a set of heliostat prototypes consisting of the frame and control system. Tests were carried out to test the performance of the designed heliostat and it was found that the heliostat has an accuracy of about 60 cm and can raise temperatures up to 3.41°C. The conclusion is that the heliostat can be used in a concentrated solar power system to heat boilers in solar power towers. © 2023 IEEE.

关键词： Solar energy

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Motion planning for robotics:A review for sampling-based planners

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Biomimetic Intelligence & robotics 2025年第1期5卷 15-34页

作者： Liding Zhang Kuanqi Cai Zewei Sun Zhenshan Bing Chaoqun Wang Luis Figueredo Sami Haddadin Alois Knoll School of Computation Information and Technology(CIT)Technical University of MunichMunich 85748Germany Munich Institute of Robotics and Machine Intelligence(MIRMI) Technical University of MunichMunich 80992Germany School of Control Science and Engineering Shandong UniversityJinan 250061China School of Computer Science University of NottinghamNottingham 315199United Kingdom

Recent advancements in robotics have transformed industries such as manufacturing,logistics,surgery,and planetary exploration.A key challenge is developing efficient motion planning algorithms that allow robots to navigate complex environments while avoiding collisions and optimizing metrics like path length,sweep area,execution time,and energy *** the available algorithms,sampling-based methods have gained the most traction in both research and industry due to their ability to handle complex environments,explore free space,and offer probabilistic completeness along with other formal *** their widespread application,significant challenges still *** advance future planning algorithms,it is essential to review the current state-of-the-art solutions and their *** this context,this work aims to shed light on these challenges and assess the development and applicability of sampling-based ***,we aim to provide an in-depth analysis of the design and evaluation of ten of the most popular planners across various *** findings highlight the strides made in sampling-based methods while underscoring persistent *** work offers an overview of the important ongoing research in robotic motion planning.

关键词： robotics Motion planning Sampling-based algorithms

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ViT-SENet-Tom: machine learning-based novel hybrid squeeze–excitation network and vision transformer framework for tomato fruits classification

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Neural Computing and Applications 2025年第9期37卷 6583-6600页

作者： Swapno, S M Masfequier Rahman Nobel, S. M. Nuruzzaman Islam, Md Babul Bhattacharya, Pronaya Mattar, Ebrahim A. Department of CSE Bangladesh University of Business and Technology Dhaka1216 Bangladesh Department of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Department of Computer Science and Engineering Amity School of Engineering and Technology and Research and Innovation Cell Amity University West Bengal Kolkata India Robotics - Cybernetic College of Engineering University of Bahrain Manama Bahrain

Tomatoes are essential fruits in numerous nations for their vast demand. It is very important to maintain the freshness of tomatoes. One of the primary challenges in the recent culinary landscape is accurately identifying healthy tomatoes while effectively eliminating damaged or rejected ones. Existing approaches employ various strategies for categorizing tomato fruit, but they often suffer from inaccuracies, slow detection, and suboptimal performance. Thus, motivated by this gap, in this paper, we propose a novel machine learning (ML) framework, ViT-SENet-Tom, which is a hybrid vision transformer (ViT) model with squeeze and excitation (SENet) block network for fast, accurate, and efficient tomato fruit classification. The framework works on three tomato classes, respectively, the ripe, unripe, and reject. In developing the proposed model, we utilized advanced and newly designed layers and functions. This integration created a more complex and sophisticated neural network, significantly enhancing efficiency and contributing to the model’s novelty. Our chosen dataset was small initially, but we implemented augmentation techniques to increase its size. This approach made our system more reliable, efficient, and effective. The hybrid ViT-SENet framework employs encoders and self-attention networks with squeeze and excitation channel functions to allow precise, robust, fast, and efficient tomato classification. In simulation, the framework achieves a training accuracy of 99.87% and validation accuracy of 93.87%, indicating the precise classification of tomatoes. Besides, this work tests accuracy using fivefold cross-validation. The highest accuracy seen at fold-5 is 99.90%. These testing results demonstrate the efficacy of the proposed framework in real-deployment scenarios. The implementation has the potential to provide enhanced and more sustainable food security and safety in future. © The Author(s) 2025.

关键词： Fruits

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LCGen: Mining in Low-Certainty Generation for View-consistent Text-to-3D 38

LCGen: Mining in Low-Certainty Generation for View-consisten...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Tao, Zeng Yang, Tong Lin, Junxiong Mai, Xinji Wang, Haoran Wang, Beining Zhou, Enyu Wang, Yan Zhang, Wenqiang Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China School of Computer Science Fudan University Shanghai China Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai China

The Janus Problem is a common issue in SDS-based text-to-3D methods. Due to view encoding approach and 2D diffusion prior guidance, the 3D representation model tends to learn content with higher certainty from each perspective, leading to view inconsistency. In this work, we first model and analyze the problem, visualizing the specific causes of the Janus Problem, which are associated with discrete view encoding and shared priors in 2D lifting. Based on this, we further propose the LCGen method, which guides text-to-3D to obtain different priors with different certainty from various viewpoints, aiding in view-consistent generation. Experiments have proven that our LCGen method can be directly applied to different SDS-based text-to-3D methods, alleviating the Janus Problem without introducing additional information, increasing excessive training burden, or compromising the generation effect. Project page is [here]. © 2024 Neural information processing systems foundation. All rights reserved.

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