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Passive Quadrupedal Gait Synchronization for Extra Robotic Legs Using a Dynamically Coupled Double Rimless Wheel Model

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Passive Quadrupedal Gait Synchronization for Extra Robotic L...

IEEE International Conference on robotics and Automation (ICRA)

作者： Daniel J. Gonzalez H. Harry Asada Robotics Research Center in the Department of Electrical Engineering and Computer Science United States Military Academy West Point NY USA d’Arbeloff Laboratory for Information Systems and Technology in the Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

The Extra Robotic Legs (XRL) system is a robotic augmentation worn by a human operator consisting of two articulated robot legs that walk with the operator and help bear a heavy backpack payload. It is desirable for the Human-XRL quadruped system to walk with the rear legs lead the front by 25% of the gait period, minimizing the energy lost from foot impacts while maximizing balance stability. Unlike quadrupedal robots, the XRL cannot command the human's limbs to coordinate quadrupedal locomotion. Using a pair of Rimless Wheel models, it is shown that the systems coupled with a spring and damper converge to the desired 25% phase difference. A Poincaré return map was generated using numerical simulation to examine the convergence properties to different coupler design parameters, and initial conditions. The Dynamically Coupled Double Rimless Wheel system was physically realized with a spring and dashpot chosen from the theoretical results, and initial experiments indicate that the desired synchronization properties may be achieved within several steps using this set of passive components alone.

关键词： Legged locomotion Wheels Synchronization Couplers Robot kinematics

Reputation and Pricing Dynamics in Online Markets

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arXiv 2021年

作者： Ma, Qian Huang, Jianwei Bąsar, Tamer Liu, Ji Chen, Xudong The School of Intelligent Systems Engineering Sun Yat-sen University China The School of Science and Engineering Chinese University of Hong Kong Shenzhen Shenzhen518172 China The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China The Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign United States The Department of Electrical and Computer Engineering Stony Brook University United States The Department of Electrical University of Colorado Boulder Computer and Energy Engineering United States

We study the economic interactions among sellers and buyers in online markets. In such markets, buyers have limited information about the product quality, but can observe the sellers' reputations which depend on their past transaction histories and ratings from past buyers. Sellers compete in the same market through pricing, while considering the impact of their heterogeneous reputations. We consider sellers with limited as well as unlimited capacities, which correspond to different practical market scenarios. In the unlimited seller capacity scenario, buyers prefer the seller with the highest reputation-price ratio. If the gap between the highest and second highest seller reputation levels is large enough, then the highest reputation seller dominates the market as a monopoly. If sellers' reputation levels are relatively close to each other, then those sellers with relatively high reputations will survive at the equilibrium, while the remaining relatively low reputation sellers will get zero market share. In the limited seller capacity scenario, we further consider two different cases. If each seller can only serve one buyer, then it is possible for sellers to set their monopoly prices at the equilibrium while all sellers gain positive market shares;if each seller can serve multiple buyers, then it is possible for sellers to set maximum prices at the equilibrium. Simulation results show that the dynamics of reputations and prices in the longerterm interactions will converge to stable states, and the initial buyer ratings of the sellers play the critical role in determining sellers' reputations and prices at the stable state. Copyright © 2021, The Authors. All rights reserved.

关键词： Competition

computer Vision, Imaging and computer Graphics. Theory and Applications 1

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丛书名： Communications in computer and Information Science

1000年

作者： AlpeshKumar Ranchordas João Madeiras Pereira Hélder J. Araújo João Manuel R. S. Tavares

ISBN: (数字)9783642118401

ISBN: (纸本)9783642118395

This book includes extended versions of the selected papers from VISIGRAPP 2009, the International Joint Conference on computer Vision, Imaging and computer Graphics Theory and Applications, which was held in Lisbon, Portugal, during February 5–8, 2009 and organized by the institute for systems and Technologies of Information, Control and Communication (INSTICC). VISIGRAPP comprises three component conferences, namely, the International Conference on computer Vision Theory and Applications (VISAPP), the International Conference on computer Graphics Theory and Applications (GRAPP), and the International Conference on Imaging Theory and Applications (IMAGAPP). VISIGRAPP received a total of 422 paper submissions from more than 50 co- tries. From these, and after a rigorous double-blind evaluation method, 72 papers were published as full papers. These figures show that this conference is now an - tablished venue for researchers in the broad fields of computer vision, computer graphics and image analysis. From the full papers, 25 were selected for inclusion in this book. The selection process was based on the scores assigned by the Program Committee reviewers as well as the Session Chairs. After selection, the papers were further revised and extended by the authors. Our gratitude goes to all contributors and referees, without whom this book would not have been possible.

关键词： Image Processing and computer Vision Pattern Recognition Programming Techniques computer Imaging, Vision, Pattern Recognition and Graphics computer Graphics Algorithm Analysis and Problem Complexity

From multi-modal property dataset to robot-centric conceptual knowledge about household objects

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arXiv 2019年

作者： Thosar, Madhura Mueller, Christian A. Jaeger, Georg Schleiss, Johannes Pulugu, Narender Chennaboina, Ravi Mallikarjun Jeevangekar, Sai Vivek Birk, Andreas Pfingsthorn, Max Zug, Sebastian Institute for Intelligent Cooperating Systems Faculty of Computer Science Otto-von-Guericke University Magdeburg Germany Robotics Computer Science & Electrical Engineering Department Jacobs University Bremen Germany Institute for Computer Science Technische Universität Bergakademie Freiberg Germany OFFIS Institute for Information Technology Oldenburg Germany

Tool-use applications in robotics require conceptual knowledge about objects for informed decision making and object interactions. State-of-the-art methods employ hand-crafted symbolic knowledge which is defined from a human perspective and grounded into sensory data afterwards. However, due to different sensing and acting capabilities of robots, their conceptual understanding of objects must be generated from a robot's perspective entirely, which asks for robot-centric conceptual knowledge about objects. With this goal in mind, this article motivates that such knowledge should be based on physical and functional properties of objects. Consequently, a selection of ten properties is defined and corresponding extraction methods are proposed. This multi-modal property extraction forms the basis on which our second contribution, a robot-centric knowledge generation is build on. It employs unsupervised clustering methods to transform numerical property data into symbols, and Bivariate Joint Frequency Distributions and Sample Proportion to generate conceptual knowledge about objects using the robot-centric symbols. A preliminary implementation of the proposed framework is employed to acquire a dataset comprising physical and functional property data of 110 houshold objects. This Robot-Centric dataSet (RoCS) is used to evaluate the framework regarding the property extraction methods, the semantics of the considered properties within the dataset and its usefulness in real-world applications such as tool substitution. Copyright © 2019, The Authors. All rights reserved.

关键词： Decision making

Enhancing Precision Agriculture Through Human-in-the-Loop Planning and Control

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Enhancing Precision Agriculture Through Human-in-the-Loop Pl...

IEEE International Conference on Automation Science and engineering (CASE)

作者： Shankar A. Deka Sujet Phodapol Andreu Matoses Gimenez Victor Nan Fernandez-Ayala Rufus Wong Pian Yu Xiao Tan Dimos V. Dimarogonas Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Division of Decision and Control Systems School of EECS Royal Institute of Technology (KTH) Stockholm Sweden Department of Cognitive Robotics Faculty of Mechanical Engineering TU Delft Delft Netherlands Department of Computer Science University of Oxford Oxford UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, we introduce a ROS based framework designed for the planning and control of robotic systems within the context of precision agriculture, with an emphasis on human-in-the-loop capabilities. Utilizing Linear Temporal Logic to articulate complex task specifications, our algorithm creates high-level robotic plans that are not only correct by design but also adaptable in real time by human operators. This dual-focus approach ensures that while humans have the flexibility to modify the high-level plan on-the-fly or even take over low-level control of the robots, the system inherently safeguards against any human actions that could potentially breach the predefined task specifications. We demonstrate our algorithm within the dynamic and challenging environment of a real vineyard, where the collaboration between human workers and robots is critical for tasks such as harvesting and pruning, and show the practical applicability and robustness of our software. This work marks a pioneering application of formal methods to complex, real-world agricultural environments.

关键词： Precision agriculture Heuristic algorithms Software algorithms Robustness Human in the loop Software Real-time systems Planning Robots Multi-agent systems

Approaching Metaheuristic Deep Learning Combos for Automated Data Mining

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arXiv 2024年

作者： Assunção, Gustavo Menezes, Paulo Institute of Systems and Robotics University of Coimbra R. Silvio Lima Coimbra3030-194 Portugal Department of Electrical and Computer Engineering University of Coimbra R. Silvio Lima Coimbra3030-194 Portugal

Lack of data on which to perform experimentation is a recurring issue in many areas of research, particularly in machine learning. The inability of most automated data mining techniques to be generalized to all types of data is inherently related with their dependency on those types which deems them ineffective against anything slightly different. Meta-heuristics are algorithms which attempt to optimize some solution independently of the type of data used, whilst classifiers or neural networks focus on feature extrapolation and dimensionality reduction to fit some model onto data arranged in a particular way. These two algorithmic fields encompass a group of characteristics which when combined are seemingly capable of achieving data mining regardless of how it is arranged. To this end, this work proposes a means of combining meta-heuristic methods with conventional classifiers and neural networks in order to perform automated data mining. Experiments on the MNIST dataset for handwritten digit recognition were performed and it was empirically observed that using a ground truth labeled dataset’s validation accuracy is inadequate for correcting labels of other previously unseen data instances. © 2024, CC BY.

关键词： Adversarial machine learning

Modelling the Dependence of Chlorophyll Leaf-Clip Measures on Vegetation Indices Derived from Multispectral UAS Images in Vineyards Parcels

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Modelling the Dependence of Chlorophyll Leaf-Clip Measures o...

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Gil Gonçalves Umberto Andriolo Lúcio Paiva Cristiano Premebida António Ferraz Department of Mathematics University of Coimbra Coimbra Portugal Department of Electrical and Computer Engineering INESC Coimbra Coimbra Portugal Research Centre for Natural Resources Environment and Society Polytechnic Institute of Coimbra Coimbra Agrarian Technical School Coimbra Portugal Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Coimbra Portugal NASA Jet Propulsion Laboratory Pasadena United States

Multispectral images and leaf-clip measurements were used for evaluating the Chlorophyll (Chl) content on grapevine leaves through the use of vegetation indices. The multispectral leaf images were taken by a sensor onboard an UAS, which was placed over a table at a height of 70 cm. Images were radiometrically and geometrically processed to obtain accurate coregistered five band image stacks. Using a Kmeans segmentation, each leaf imaged in a multispectral image was automatically detected and the mean leaf reflectance values were used to compute three vegetation indices: NDVI, NDRE, and GLI. When compared to leaf-clip measurements, the results indicated that the NDRE index was the best predictor of Chl leaf content (R2=0.81). The obtained NDRE regression model can be used in UAS-based multispectral orthomosaics to generate canopy Chl maps at vine-row scale, which can assist vine growers in monitoring the spatial and temporal variability of grapevine vigor.

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Survey on Deep Fuzzy systems in regression applications: a view on interpretability

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arXiv 2022年

作者： Silveira, Jorge Sampaio Mendes, Jerome Souza, Francisco Premebida, Cristiano Department of Electrical and Computer Engineering Pólo II University of Coimbra Institute of Systems and Robotics Coimbra3030-290 Portugal Dept. Analytical Chemistry & Chemometrics Radboud University Nijmegen6525 AJ Netherlands

Regression problems have been more and more embraced by deep learning (DL) techniques. The increasing number of papers recently published in this domain, including surveys and reviews, shows that deep regression has captured the attention of the community due to efficiency and good accuracy in systems with high-dimensional data. However, many DL methodologies have complex structures that are not readily transparent to human users. Accessing the interpretability of these models is an essential factor for addressing problems in sensitive areas such as cyber-security systems, medical, financial surveillance, and industrial processes. Fuzzy logic systems (FLS) are inherently interpretable models, well known in the literature, capable of using nonlinear representations for complex systems through linguistic terms with membership degrees mimicking human thought. Within an atmosphere of explainable artificial intelligence, it is necessary to consider a trade-off between accuracy and interpretability for developing intelligent models. This paper aims to investigate the state-of-the-art on existing methodologies that combine DL and FLS, namely deep fuzzy systems, to address regression problems, configuring a topic that is currently not sufficiently explored in the literature and thus deserves a comprehensive survey. © 2022, CC BY.

关键词： Surveys

Communication-efficient Vertical Federated Learning via Compressed Error Feedback

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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