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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Tan, Yijian Lin, Yunhan Liu, Tong Min, Huasong Wuhan University of Science and Technology College of Computer Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan430081 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

In this paper, Prioritized Experience Replay (PER) strategy and Long Short Term Memory (LSTM) neural network are introduced to the path planning process of mobile robots, which solves the problems of slow convergence and inaccurate perception of dynamic obstacles with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. We dubbed this new method as PL-TD3. Firstly, we improve the convergence speed of the algorithm by introducing PER strategy. Secondly, we use LSTM neural network to achieve the improvement of the algorithm for dynamic obstacle perception. In order to verify the method of this paper, we design static environment, dynamic environment and adaptability to dynamic experiments to compare and analyze the methods before and after improvement. The experimental results show that PL-TD3 outperforms TD3 in terms of execution time and execution path length in all environments. © 2022 IEEE.

关键词： Motion planning

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Classification of Jackfruit Species Using Deep Learning Model

Classification of Jackfruit Species Using Deep Learning Mode...

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Recent Advances in Information Technology for Sustainable Development (ICRAIS), International Conference on

作者： Veeresha R K Shilpa Karegoudra Bhavin Kumar Pavan Pavan Department of Robotics and Artificial Intelligence NMAM Institute of Technology (NMAMIT) Nitte (Deemed to be University) Udupi India Department of Computer Science and Engineering NMAM Institute of Technology (NMAMIT) Nitte (Deemed to be University) Udupi India

ISBN: (数字)9798350354461

ISBN: (纸本)9798350354522

Identifying Jackfruit spices using a deep learning model involves leveraging advanced neural network architectures to classify spices accurately. The model is trained on a dataset containing images of various spices commonly used with Jackfruit, such as deng surya, manmohan, prakashchandra, vietnamearly, and others. Through multiple layers of convolutional neural networks (CNNs) and possibly recurrent neural networks (RNNs), the model learns intricate patterns and features within the images to differentiate between different species. Transfer learning techniques might also be employed to adapt pre-trained models to the specific task of spice identification. The model's performance is evaluated on a separate test dataset to measure its accuracy, precision, recall, and F1 score. The paper provides a comprehensive overview of the deep learning approach to identifying Jackfruit spices, highlighting its potential applications in culinary research, food industry automation, and dietary analysis.

关键词： Deep learning Training Productivity Adaptation models Accuracy Recurrent neural networks Transfer learning Predictive models Sustainable development Information technology

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Analysis on Chest X-ray and Computed Tomography Images to Detect COVID-19 Based on Deep Learning Techniques

Analysis on Chest X-ray and Computed Tomography Images to De...

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2022 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2022

作者： Kanimozhi, J. Mohanasuganthi, N. Priyadharshini, A. Rohini, M.S. Swathilakshmi, V. Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Chennai India Robotics and Automation Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Computer Science and Engineering Sri Manakula Vinayagar Engineering College Puducherry India

ISBN: (纸本)9798350333848

More than 6.3 million individuals have died as a result of the Corona Virus Disease 2019 (COVID-19), which spoiled many more human health globally. Since COVID-19 is a pandemic that is rapidly spreading, early discovery is essential to halting the infection's spread. Images of the lungs are utilised to identify coronavirus infection. For the identification of Corona Virus Disease, chest X-ray (CXR) and computed tomography (CT) images are available. Deep learning methods are proved to be effective and perform better in medical imaging applications. This study examines lung CT pictures, classifies and segments them, and uses the results to identify whether a patient tested is affected by COVID-19 or not using Deep learning techniques. The COVID detection performance of the deep learning architectures GG19, MobileNet, COVID-Net (PEPX), Squeez Net, U-Net, DarkNet and VGG16 are analysed - it was shown that U-Net combined VGG16 (acc98.89%) and VGG19 (acc-98.05%) performs the best, followed by MobileNet and QueezNet. © 2022 IEEE.

关键词： Convolutional neural networks

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Hybrid polynomial-based trajectory planning for lower limb exoskeleton robots 18

Hybrid polynomial-based trajectory planning for lower limb e...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Li, Shupei Zhang, Chi Yi, Feng Lv, Pingping Yuan, Ting Zhang, Meitong University of Chinese Academy of Sciences Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology Ningbo315201 China Ningbo Institute of Materials Technology and Engineering Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo315201 China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315201 China Hebei University of Science & Technology School of Electrical Engineering Ningbo315201 China

ISBN: (纸本)9798350312201

To ensure smooth, continuous, and precise motion of lower limb exoskeleton robots, this paper proposes a 6-5-6 polynomial trajectory planning method (6-5-6 PTPM) that combines 5th and 6th polynomials. By imposing corresponding kinematic constraints, this method achieves the trajectory plan of seven sub-phases of the gait cycle on the kinematic model of the lower limb exoskeleton robot. The simulation is conducted and compared to the 4-3-4 polynomial trajectory planning method (4-3-4 PTPM). The comparison results indicate that the 6-5-6 PTPM is shown to have a lower error between the planned trajectory and the actual trajectory. Additionally, it avoids spikes of the acceleration curve that existed in the 4-3-4 polynomial method. The proposed method enables the acceleration curve smoother and mechanical impact lessened, which demonstrates the feasibility and stability of the proposed method. © 2023 IEEE.

关键词： Trajectories

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TIGRIS: An Informed Sampling-based Algorithm for Informative Path Planning

arXiv

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arXiv 2022年

作者： Moon, Brady Chatterjee, Satrajit Scherer, Sebastian The Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA United States

Informative path planning is an important and challenging problem in robotics that remains to be solved in a manner that allows for wide-spread implementation and real-world practical adoption. Among various reasons for this, one is the lack of approaches that allow for informative path planning in high-dimensional spaces and non-trivial sensor constraints. In this work we present a sampling-based approach that allows us to tackle the challenges of large and high-dimensional search spaces. This is done by performing informed sampling in the high-dimensional continuous space and incorporating potential information gain along edges in the reward estimation. This method rapidly generates a global path that maximizes information gain for the given path budget constraints. We discuss the details of our implementation for an example use case of searching for multiple objects of interest in a large search space using a fixed-wing UAV with a forward-facing camera. We compare our approach to a sampling-based planner baseline and demonstrate how our contributions allow our approach to consistently out-perform the baseline by 18.0%. With this we thus present a practical and generalizable informative path planning framework that can be used for very large environments, limited budgets, and high dimensional search spaces, such as robots with motion constraints or high-dimensional configuration spaces. Copyright © 2022, The Authors. All rights reserved.

关键词： Motion planning

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Stair Ascent Phase-Variable Control of a Powered Knee-Ankle Prosthesis

Stair Ascent Phase-Variable Control of a Powered Knee-Ankle ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Ross J. Cortino Edgar Bolí var-Nieto T. Kevin Best Robert D. Gregg Department of Electrical Engineering and Computer Science Robotics Institute University of Michigan Ann Arbor MI

Passive prostheses cannot provide the net positive work required at the knee and ankle for step-over stair ascent. Powered prostheses can provide this net positive work, but user synchronization of joint motion and power input are critical to enabling natural stair ascent gaits. In this work, we build on previous phase variable-based control methods for walking and propose a stair ascent controller driven by the motion of the user's residual thigh. We use reference kinematics from an able-bodied dataset to produce knee and ankle joint trajectories parameterized by gait phase. We redefine the gait cycle to begin at the point of maximum hip flexion instead of heel strike to improve the phase estimate. Able-bodied bypass adapter experiments demonstrate that the phase variable controller replicates normative able-bodied kinematic trajectories with a root mean squared error of 12.66° and 2.64° for the knee and ankle, respectively. The knee and ankle joints provided on average 0.39 J/kg and 0.21 J/kg per stride, compared to the normative averages of 0.34 J/kg and 0.21 J/kg, respectively. Thus, this controller allows powered knee-ankle prostheses to perform net positive mechanical work to assist stair ascent.

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Communication-efficient Vertical Federated Learning via Compressed Error Feedback

arXiv

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arXiv 2024年

作者： Valdeira, Pedro Xavier, João Soares, Cláudia Chi, Yuejie CMU IST United States IST NOVA CMU United States Department of Electrical and Computer Engineering Carnegie Mellon University United States Institute for Systems and Robotics Instituto Superior Técnico Portugal Department of Computer Science NOVA School of Science and Technology

Communication overhead is a known bottleneck in federated learning (FL). To address this, lossy compression is commonly used on the information communicated between the server and clients during training. In horizontal FL, where each client holds a subset of the samples, such communication-compressed training methods have recently seen significant progress. However, in their vertical FL counterparts, where each client holds a subset of the features, our understanding remains limited. To address this, we propose an error feedback compressed vertical federated learning (EF-VFL) method to train split neural networks. In contrast to previous communication-compressed methods for vertical FL, EF-VFL does not require a vanishing compression error for the gradient norm to converge to zero for smooth nonconvex problems. By leveraging error feedback, our method can achieve a O(1/T) convergence rate for a sufficiently large batch size, improving over the state-of-the-art O(1/√T) rate under O(1/√T) compression error, and matching the rate of uncompressed methods. Further, when the objective function satisfies the Polyak-Lojasiewicz inequality, our method converges linearly. In addition to improving convergence, our method also supports the use of private labels. Numerical experiments show that EF-VFL significantly improves over the prior art, confirming our theoretical results. The code for this work can be found at https://***/Valdeira/EF-VFL. © 2024, CC BY.

关键词： Federated learning

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Photon counting integral imaging using Kalman estimation 13

Photon counting integral imaging using Kalman estimation

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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Shinohara, Ryo Kim, Hyun-Woo Lee, Jaehoon Huh, Jong-Hoon Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Iizuka Japan Department of Physics and Information Technology Kyushu Institute of technology Iizuka Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Kyonggi-do Anseong Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

In this paper, we propose 3D photon counting integral imaging using the Kalman estimation. Photons are emitted in unit time and space independently. To visualize the 3D object under low luminance conditions, computational photon counting imaging may be used. It can estimate the photons by statistical methods such as the Poisson random distribution. Then, it can reconstruct the 3D information through the integral imaging. However, since the Poisson random process always generates different photon counting images, the image quality is not consistent and also errors may occur. Therefore, it is difficult to maintain the high 3D image quality under photon-starved conditions. To solve these problems, we utilize Kalman estimation to estimate the photons accurately and generate the high image quality consistently under various low light conditions. To evaluate our proposed method, we implemented optical experiments and image quality evaluation by calculating image quality metrics. © 2022 IEEE.

关键词： Photons

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RETOM: Leveraging Maneuverability for Reactive Tool Manipulation using Wrench-Fields

RETOM: Leveraging Maneuverability for Reactive Tool Manipula...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Felix Eberle Riddhiman Laha Haowen Yao Abdeldjallil Naceri Luis F.C. Figueredo Sami Haddadin Munich Institute of Robotics & Machine Intelligence Technische Universität München (TUM) Germany School of Computer Science University of Nottingham UK MIRMI Technical University of Munich (TUM)

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper investigates the problem of effective tool manipulation for motion planning in complex human-like scenarios. Vector-field-based real-time strategies, although widely used, usually do not account for unwieldy tools or incorporate systematic methods to handle these extra maneuvers needed. Instead, we formalize the problem and propose a novel field-based reactive planner that explicitly accounts for rotational forces for seamless maneuvers based on the tool’s geometry and featured points. Furthermore, we capture and encode robot performance through capability metrics and improve the same using an additional quality distribution method. This enables seamless integration of the robot’s embodiment with the reactive force-torque (wrench) field giving rise to flexible tool usage in non-stationary environments. Extensive simulation analysis on a 7 DoF collaborative robot manipulating a common tool in an unorganized table-top layout reinforces our claim of robustness in stationary and non-stationary scenarios.

关键词： Measurement Geometry Systematics Layout Collaborative robots Robustness Real-time systems

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Granular Ball K-Class Twin Support Vector Classifier

arXiv

引用

arXiv 2024年

作者： Ganaie, M.A. Ahire, Vrushank Girard, Anouck Department of Computer Science and Engineering Indian Institute of Technology Ropar Punjab Rupnagar140001 India Department of Robotics University of Michigan Ann ArborMI48109-2140 United States

This paper introduces the Granular Ball K-Class Twin Support Vector Classifier (GB-TWKSVC), a novel multi-class classification framework that combines Twin Support Vector Machines (TWSVM) with granular ball computing. The proposed method addresses key challenges in multi-class classification by utilizing granular ball representation for improved noise robustness and TWSVM’s non-parallel hyperplane architecture solves two smaller quadratic programming problems, enhancing efficiency. Our approach introduces a novel formulation that effectively handles multi-class scenarios, advancing traditional binary classification methods. Experimental evaluation on diverse benchmark datasets shows that GB-TWKSVC significantly outperforms current state-of-the-art classifiers in both accuracy and computational performance. The method’s effectiveness is validated through comprehensive statistical tests and complexity analysis. Our work advances classification algorithms by providing a mathematically sound framework that addresses the scalability and robustness needs of modern machine learning applications. The results demonstrate GB-TWKSVC’s broad applicability across domains including pattern recognition, fault diagnosis, and large-scale data analytics, establishing it as a valuable addition to the classification algorithm landscape. © 2024, CC BY.

关键词： Vectors

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