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A Synchronized Task Formulation for Robotic Convoy Operations

IEEE robotics and Automation Letters 2025年第7期10卷 6808-6815页

作者： Noren, Charles Vundurthy, Bhaskar Scherer, Sebastian Choset, Howie Travers, Matthew Carnegie Mellon University Robotics Institute School of Computer Science PittsburghPA United States

Future ground logistics missions will require multiple robots to travel in a convoy between locations. As each location may require a different number of robots (e.g. resupply vehicles), these missions will require a mutable convoy formation structure that may be divided to meet operational needs at each location. We model this mission type by modifying the vehicle routing problem with multiple synchronizations (VRPMS) to enforce convoy constraints (VRPMS-CC). This centralized approach to organizing and routing convoys is represented as a graph-based routing problem and then solved as a mixed integer program. A solution of the VRPMS-CC forms convoys by ensuring that agents participating in the same convoy remain spatially and temporally coupled, traversing the same edge of the graph simultaneously. We demonstrate our approach through numerical studies, where we route up to six simulated agents through twenty convoying tasks, and on robotic hardware. These demonstrations motivate two further contributions to specialize our approach to robotic systems. We introduce: 1) a warm-starting heuristic that improves solver times by up to eighty-nine percent and 2) an online multi-depot variant of the VRPMS-CC that responds to a priori unknown impassable environmental obstacles. © 2016 IEEE.

关键词： Multipurpose robots

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A trajectory-level robot skill learning method based on fuzzy systems

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science China(Information sciences) 2025年第5期68卷 38-52页

作者： Chao ZENG Zhen DENG Boyang TI Yongsheng GAO Department of Computer Science University of Liverpool School of Mechanical Engineering and Automation Fuzhou University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics and System Harbin Institute of Technology

Learning from demonstration(LfD) allows for the effective transfer of human manipulation skills to a robot by building a model that represents these skills based on a limited number of demonstrated ***,a skilllearning model that can comprehensively satisfy multiple requirements,such as computational complexity,modeling accuracy,trajectory smoothness,and robustness,is still ***,this work aims to provide such a model by employing fuzzy ***,we introduce an LfD model named Takagi-Sugeno-Kang fuzzy system-based movement primitives(TSKFMPs),which exploits the advantages of the fuzzy theory for effective robotic imitation learning of human *** work formulates the TSK fuzzy system and gradient descent(GD) as imitation learning models,leveraging recent advancements in GD-based optimization for fuzzy *** study takes a two-step strategy.(ⅰ) The input-output relationships of the model are established using TSK fuzzy systems based on demonstration *** this way,the skill is encoded by the model parameter in the latent space.(ⅱ) GD is used to optimize the model parameter to increase the modeling accuracy and trajectory *** further explain how learned trajectories are adapted to new task scenarios through local *** conduct multiple tests using an open dataset to validate our method,and the results demonstrate performance comparable with those of other ***,we implement it in a real-world case study.

关键词： robot skill learning movement primitives TSK fuzzy systems gradient descent robotics

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A Novel Assistive Imaging Device for Colon Map Reconstruction

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IEEE robotics and Automation Letters 2024年第12期9卷 10780-10787页

作者： Dei, Neri Niccolo Bano, Sophia Vasconcelos, Francisco Consumi, Vanni Stoyanov, Danail Stilli, Agostino Ciuti, Gastone Scuola Superiore sant'Anna Bio Robotics Institute Department of Excellence in Robotics and AI Pisa Italy University College London Hawkes Institute Department of Computer Science London United Kingdom

Colonoscopy, recognised as the gold standard for diagnosing and treating colorectal cancer, faces limitations that may result in overlooking some colonic regions. This can lead to missed lesions and interval cancer, leading to incomplete treatment. Addressing this challenge, the authors present a novel Assistive Imaging Device for Colon Map Reconstruction (AID-CMR) to support clinicians in visualising the entire colonic mucosa and identifying areas that may have been missed during the procedure. The device is comprised of an overtube tooled with an imaging module on its distal end, which can be moved about a threaded silicone sleeve. The imaging module houses inflatable silicone chambers to distend the lumen, a side-viewing miniaturised full-HD camera with LEDs, a magnetic encoder, and an irrigation channel for camera cleansing. Before intervention, the device can be installed on standard colonoscopes or other tethered endoscopes. During the intervention, the device can be operated to (1) reconstruct assistive maps of the organ, (2) visualise the full anatomy, and (3) increase disease detection. Moreover, these maps may be employed for the quantification of missed regions during standard inspection. This paper introduces a proof of concept device and discusses locomotion and reconstruction tests conducted in two in-vitro conditions. These tests reveal overall procedural feasibility in terms of locomotion, exerted forces and colon map reconstruction, preserving polyp morphology. © 2024 IEEE.

关键词： Medical robotics

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Beyond NeRF Underwater: Learning Neural Reflectance Fields for True Color Correction of Marine Imagery

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IEEE robotics and Automation Letters 2023年第10期8卷 6467-6474页

作者： Zhang, Tianyi Johnson-Roberson, Matthew Carnegie Mellon University Robotics Institute School of Computer Science PittsburghPA15213 United States

Underwater imagery often exhibits distorted coloration as a result of light-water interactions, which complicates the study of benthic environments in marine biology and geography. In this research, we propose an algorithm to restore the true color (albedo) in underwater imagery by jointly learning the effects of the medium and neural scene representations. Our approach models water effects as a combination of light attenuation with distance and backscattered light. The proposed neural scene representation is based on a neural reflectance field model, which learns albedos, normals, and volume densities of the underwater environment. We introduce a logistic regression model to separate water from the scene and apply distinct light physics during training. Our method avoids the need to estimate complex backscatter effects in water by employing several approximations, enhancing sampling efficiency and numerical stability during training. The proposed technique integrates underwater light effects into a volume rendering framework with end-to-end differentiability. Experimental results on both synthetic and real-world data demonstrate that our method effectively restores true color from underwater imagery, outperforming existing approaches in terms of color consistency. © 2016 IEEE.

关键词： Reflection

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Non-rigid Relative Placement through 3D Dense Diffusion 8

Non-rigid Relative Placement through 3D Dense Diffusion

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8th Conference on Robot Learning, CoRL 2024

作者： Cai, Eric Donca, Octavian Eisner, Ben Held, David Robotics Institute School of Computer Science Carnegie Mellon University United States

The task of "relative placement" is to predict the placement of one object in relation to another, e.g. placing a mug onto a mug rack. Through explicit object-centric geometric reasoning, recent methods for relative placement have made tremendous progress towards data-efficient learning for robot manipulation while generalizing to unseen task variations. However, they have yet to represent deformable transformations, despite the ubiquity of non-rigid bodies in real world settings. As a first step towards bridging this gap, we propose "cross-displacement" - an extension of the principles of relative placement to geometric relationships between deformable objects - and present a novel vision-based method to learn cross-displacement through dense diffusion. To this end, we demonstrate our method's ability to generalize to unseen object instances, out-of-distribution scene configurations, and multimodal goals on multiple highly deformable tasks (both in simulation and in the real world) beyond the scope of prior works. Supplementary information and videos can be found at our website. © 2024 Proceedings of Machine Learning Research.

关键词： Geometry

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FlowBotHD: History-Aware Diffuser Handling Ambiguities in Articulated Objects Manipulation 8

FlowBotHD: History-Aware Diffuser Handling Ambiguities in Ar...

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8th Conference on Robot Learning, CoRL 2024

作者： Li, Yishu Leng, Wen Hui Fang, Yiming Eisner, Ben Held, David Robotics Institute School of Computer Science Carnegie Mellon University United States

We introduce a novel approach for manipulating articulated objects which are visually ambiguous, such doors which are symmetric or which are heavily occluded. These ambiguities can cause uncertainty over different possible articulation modes: for instance, when the articulation direction (e.g. push, pull, slide) or location (e.g. left side, right side) of a fully closed door are uncertain, or when distinguishing features like the plane of the door are occluded due to the viewing angle. To tackle these challenges, we propose a history-aware diffusion network that can model multi-modal distributions over articulation modes for articulated objects;our method further uses observation history to distinguish between modes and make stable predictions under occlusions. Experiments and analysis demonstrate that our method achieves state-of-art performance on articulated object manipulation and dramatically improves performance for articulated objects containing visual ambiguities. Our project website is available at https://***/. © 2024 Proceedings of Machine Learning Research.

关键词： Ambiguity Articulated Objects Diffusion

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Investigating the effects of embodiment on presence and perception in remote physician video consultations: a between-participants study comparing a tablet and a telepresence robot

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i-com 2025年第1期24卷 213-226页

作者： Schulz, Dominik Unbehaun, David Doernbach, Tobias Clausthal University of Technology Computer Science Institute Clausthal-Zellerfeld Germany Ostfalia University of Applied Sciences Human-Centered Robotics Lab Wolfenbuettel Germany

Increasing life expectancy and low birth rates have led to a larger aging population requiring more care, especially in rural areas. Information and communications technology may enhance older adults' quality of life and digital health services. Assistive robotic systems, especially telepresence robots, are increasingly used in healthcare. These robots represent a significantly higher embodiment level than tablets, as they can provide the remote person with an additional body. This work explores the effects of different embodiment levels in video consultation, focusing on Presence and Perception from a Human-computer Interaction (HCI) perspective. For this purpose, between-participants experiments were conducted in which 18 participants assumed the role of the patient and engaged in a video consultation with a remote, medically experienced person acting as the physician. A shortened medical check-up simulated the real assessment. Qualitative and quantitative data were collected during and after the experiment focusing on presence and interaction. Results show no significant influence of embodiment level in a video consultation from a HCI perspective. However, a trend towards more natural communication with stronger embodiment was observed, suggesting it may positively influence interaction. © 2025 the author(s), published by De Gruyter, Berlin/Boston.

关键词： Electronic health record

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A smart green mashrabiyya-shutter design for residential applications in Egypt

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HBRC Journal 2023年第1期19卷 229-252页

作者： Abdel Gelil Mohamed, Nermine Hamid Abd El-Rahman, Eslam Sadek, Mohamed Giza Egypt Mechatronics and Robotics Department Cairo Higher Institute for Engineering Computer Science and Management Cairo Egypt

The revival of traditional latticework mashrabiyya is continually urged in the Middle East and North Africa to address conflicting needs since it optimally balances environmental, social, and aesthetic functions. However, its material and craftsmanship costs and inflexibility are significant barriers. This study aims to provide a preliminary design for a green, smart mashrabiyya-like shutter responsive to climatic conditions and privacy needs in Egypt, manufactured with palm midribs, a waste material available in the MENA region due to annual trimming. The simple, yet responsive and traditional design outlined in this study has a considerable potential for residential applications not only in Egypt, but also in similar socio-environmental contexts. The design generation is based on three steps: 1) constructing a static model that maintains the basic socio-environmental needs, manufactured with palm midrib patterns, 2) modeling dynamic functions, using Grasshopper/Ladybug, through standardizing slats and deciding upon visibility and adjustable parts, and 3) integrating smart controls based on comfort indexes and indoor climatic readings. Using a simple circuit fitted with sensors, actuators, and a microcontroller, the design of a latticework shutter responsive to climate and privacy was generated. The concept depends on computing indoor weather data and comparing them to thermal, daylight, and wind comfort indexes. Consequently, automated decisions could be made to regulate the shutter parts in two ways: 1) its opening angle and 2) openness between the slats. Ultimately, the operating concept of the smart shutter in a room and automated flowcharts that make decisions in daytime and nighttime modes were proposed. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Visibility

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Support vector machine with discriminative low-rank embedding

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CAAI Transactions on Intelligence Technology 2024年第5期9卷 1249-1262页

作者： Guangfei Liang Zhihui Lai Heng Kong Computer Vision Institute College of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina Shenzhen Institute of Artificial Intelligence and Robotics for Society ShenzhenChina Department of Breast and Thyroid Surgery BaoAn Central Hospital of ShenzhenShenzhenChina

Support vector machine(SVM)is a binary classifier widely used in machine ***,neglecting the latent data structure in previous SVM can limit the performance of SVM and its *** address this issue,the authors propose a novel SVM with discriminative low-rank embedding(LRSVM)that finds a discriminative latent low-rank subspace more suitable for SVM *** extension models of LRSVM are introduced by imposing different orthogonality constraints to prevent computational inaccuracies.A detailed derivation of the authors’iterative algorithms are given that is essentially for solving the SVM on the low-rank ***,some theorems and properties of the proposed models are presented by the *** is worth mentioning that the subproblems of the proposed algorithms are equivalent to the standard or the weighted linear discriminant analysis(LDA)*** indicates that the projection subspaces obtained by the authors’algorithms are more suitable for SVM classification compared to those from the LDA *** convergence analysis for the authors proposed algorithms are also ***,the authors conduct experiments on various machine learning data sets to evaluate the *** experiment results show that the authors’algorithms perform significantly better than other algorithms,which indicates their superior abilities on classification tasks.

关键词： iterative methods machine leaning support vector machunes

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Existing Approaches & A Proposal to Content Analysis of Pilots' Speech Activities

Existing Approaches & A Proposal to Content Analysis of Pilo...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Bogdanova, Diana Giniiatullin, Azamat Batth, Jaspreet Singh Ufa University of Science and Technology Institute of Computer Science Mathematics and Robotics Ufa Russia Chandigarh University Computer Science and Engineering Chandigarh India

ISBN: (纸本)9798350395044

Content analysis of pilot speech is a key tool for improving flight safety as it allows to identify potential problems in communication between pilots, dispatchers and crew. The development of efficient algorithms for analyzing pilot speech contributes to improving communication skills, preventing incidents and improving overall airline safety. The research focuses on the possibility of applying machine learning techniques and deep neural networks to analyze speech data, which opens new perspectives for information processing and interpretation. The results of the study emphasize the interdisciplinary nature of the topic and suggest new methodological approaches, including the use of fuzzy set theory and fuzzy logic systems to handle uncertainty in speech data, which in turn helps to improve communication performance in complex and stressful flight conditions. The finalization of the work includes the evaluation of the developed methods and approaches in terms of their applicability and impact on improving flight safety, incorporating model training results and addressing testing errors. © 2024 IEEE.

关键词： Deep neural networks

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