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Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds

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arXiv 2022年

作者： Raji, Ayoub Liniger, Alexander Giove, Andrea Toschi, Alessandro Musiu, Nicola Morra, Daniele Verucchi, Micaela Caporale, Danilo Bertogna, Marko University of Modena and Reggio Emilia Italy University of Parma Italy Computer Vision Lab ETH Zurich Switzerland University of Pisa Italy Technology Innovation Institute Autonomous Robotics Research Center United Arab Emirates

This paper presents a multi-layer motion planning and control architecture for autonomous racing, capable of avoiding static obstacles, performing active overtakes, and reaching velocities above 75 m/s. The used offline global trajectory generation and the online model predictive controller are highly based on optimization and dynamic models of the vehicle, where the tires and camber effects are represented in an extended version of the basic Pacejka Magic Formula. The proposed single-track model is identified and validated using multi-body motorsport libraries which allow simulating the vehicle dynamics properly, especially useful when real experimental data are missing. The fundamental regularization terms and constraints of the controller are tuned to reduce the rate of change of the inputs while assuring an acceptable velocity and path tracking. The motion planning strategy consists of a Frenét-Frame-based planner which considers a forecast of the opponent produced by a Kalman filter. The planner chooses the collision-free path and velocity profile to be tracked on a 3 seconds horizon to realize different goals such as following and overtaking. The proposed solution has been applied on a Dallara AV-21 racecar and tested at oval race tracks achieving lateral accelerations up to 25 m/s2 Copyright © 2022, The Authors. All rights reserved.

关键词： Motion planning

Platform-portable reinforcement learning methods to localize underwater targets

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Platform-portable reinforcement learning methods to localize...

OCEANS

作者： Ivan Masmitja Mario Martin Tom O’Reilly Narcis Palomeras Kakani Katija Joan Navarro Institut de Ciències del Mar (ICM) CSIC Barcelona Spain KEMLG Research Group Universitat Politècnica de Catalunya Barcelona Tech. Barcelona Spain Monterey Bay Aquarium Research Institute Moss Landing USA Computer Vision and Robotics Institute Universitat de Girona Girona Spain Bioinspiration Lab Monterey Bay Aquarium Research Institute Moss Landing USA

Subsea residential autonomous systems will reduce the cost and CO 2 footprint of future subsea operations. This concept includes subsea docking stations (SDS) and Autonomous Underwater Vehicles (AUVs). To ensure the AUVs are capable of interfacing with the SDS, a driftless and accurate close-range navigation schema is required. Thus, this paper proposes a visual method for navigation utilizing a defined set of ArUco markers printed on the SDS. To increase the accuracy of the system, the set of ArUco markers is defined to a common frame of reference, introducing more points in the camera frame for a more accurate solution to the Perspective-n-Point problem. This makes the system robust against partial occlusion. The paper has also implemented a modified Line of Sight (LOS) schema for the guidance of the vehicle. The results show stable pose estimates at up to 6 meters above the SDS, and successful autonomous docking of an underwater vehicle. The navigational estimates provided by the system get more stable when the vehicle is closer to the SDS.

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Multiparametric benthic landers for monitoring fishing-impacted deep-sea ecosystems

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Multiparametric benthic landers for monitoring fishing-impac...

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作者： Daniel Mihai Toma Jacopo Aguzzi Matias Carandell Marc Nogueras Enoc Martínez Marco Francescangeli Damianos Chatzievangelou Nixon Bahamon Joan Baptista Company Jordi Grinyo Marc Carreras Sascha Flögel Joaquín del Río SARTI Research Group Universitat Politècnica de Catalunya Vilanova i la Geltrú Spain Instituto de Ciencias del Mar Consejo Superior de Investigaciones Científicas Barcelona Spain Department of Ocean Systems NIOZ Royal Netherlands Institute for Sea Research Den Burg The Netherlands Computer Vision and Robotics Institute University of Girona Girona Spain GEOMAR Helmholtz Centre for Ocean Research Kiel Kiel Germany

To assess conservation efforts and follow the effects of anthropogenic pressures, it is crucial to monitor the ecological status of benthic habitats. Monitoring requires measurements that are made on-site, reproduced over a vast region, and capable of capturing their spatial evolution over time. This calls for the expensively efficient development of tools and protocols that deliver reliable, high-quality data. This article introduces the PLOME multiparametric benthic landers, a new tool and protocol for non-extractively assessing the status of fishes and benthic habitats. By combining independent and wirelessly connected benthic stations, AUVs, and USVs, PLOME creates a spatially adjustable, non-invasive, modular platform. Lander stations will be able to continuously deliver high-rate data while AUVs can provide comprehensive spatial measurements.

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Challenges of deep learning methods for COVID-19 detection using public datasets

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Informatics in Medicine Unlocked 2022年 30卷 100945页

作者： Hasan, Md. Kamrul Alam, Md. Ashraful Dahal, Lavsen Roy, Shidhartho Wahid, Sifat Redwan Elahi, Md. Toufick E. Martí, Robert Khanal, Bishesh Department of Electrical and Electronic Engineering (EEE) Khulna University of Engineering & Technology (KUET) Khulna 9203 Bangladesh Nepal Applied Mathematics and Informatics Institute for Research (NAAMII) Nepal Computer Vision and Robotics Institute University of Girona Spain

Since the COVID-19 pandemic, several research studies have proposed Deep Learning (DL)-based automated COVID-19 detection, reporting high cross-validation accuracy when classifying COVID-19 patients from normal or other common Pneumonia. Although the reported outcomes are very high in most cases, these results were obtained without an independent test set from a separate data source(s). DL models are likely to overfit training data distribution when independent test sets are not utilized or are prone to learn dataset-specific artifacts rather than the actual disease characteristics and underlying pathology. This study aims to assess the promise of such DL methods and datasets by investigating the key challenges and issues by examining the compositions of the available public image datasets and designing different experimental setups. A convolutional neural network-based network, called CVR-Net (COVID-19 Recognition Network), has been proposed for conducting comprehensive experiments to validate our hypothesis. The presented end-to-end CVR-Net is a multi-scale-multi-encoder ensemble model that aggregates the outputs from two different encoders and their different scales to convey the final prediction probability. Three different classification tasks, such as 2-, 3-, 4-classes, are designed where the train–test datasets are from the single, multiple, and independent sources. The obtained binary classification accuracy is 99.8% for a single train–test data source, where the accuracies fall to 98.4% and 88.7% when multiple and independent train–test data sources are utilized. Similar outcomes are noticed in multi-class categorization tasks for single, multiple, and independent data sources, highlighting the challenges in developing DL models with the existing public datasets without an independent test set from a separate dataset. Such a result concludes a requirement for a better-designed dataset for developing DL tools applicable in actual clinical settings. The datas

关键词： Chest computed tomography and X-ray Convolutional neural networks COVID-19 disease Ensemble classifier

The iToBoS dataset: skin region images extracted from 3D total body photographs for lesion detection

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arXiv 2025年

作者： Saha, Anup Adeola, Joseph Ferrera, Nuria Mothershaw, Adam Rezze, Gisele Gaborit, Séraphin D’Alessandro, Brian Hudson, James Szabó, Gyula Pataki, Balazs Rajani, Hayat Nazari, Sana Hayat, Hassan Primiero, Clare Soyer, H. Peter Malvehy, Josep Garcia, Rafael Computer Vision and Robotics Research Institute University of Girona Girona Spain Dermatology Department Hospital Clínic Barcelona Universitat de Barcelona Barcelona Spain Frazer Institute The University of Queensland Dermatology Research Center Brisbane Australia ISAHIT Paris France Canfield Scientific Inc. ParsippanyNJ United States V7 London United Kingdom HUN-REN Institute for Computer Science and Control Budapest Hungary CIBER de Enfermedades Raras Instituto de Salud Carlos III Barcelona Spain Dermatology Department Princess Alexandra Hospital BrisbaneQLD Australia Dermatology Department University of Trieste Trieste Italy

Artificial intelligence has significantly advanced skin cancer diagnosis by enabling rapid and accurate detection of malignant lesions. In this domain, most publicly available image datasets consist of single, isolated skin lesions positioned at the center of the image. While these lesion-centric datasets have been fundamental for developing diagnostic algorithms, they lack the context of the surrounding skin, which is critical for improving lesion detection. The iToBoS dataset was created to address this challenge. It includes 16,954 images of skin regions from 100 participants, captured using 3D total body photography. Each image roughly corresponds to a 7×9 cm section of skin with all suspicious lesions annotated using bounding boxes. Additionally, the dataset provides metadata such as anatomical location, age group, and sun damage score for each image. This dataset aims to facilitate training and benchmarking of algorithms, with the goal of enabling early detection of skin cancer and deployment of this technology in non-clinical environments. © 2025, CC BY-NC-SA.

关键词： Lung cancer

A Unified Framework for Event-based Frame Interpolation with Ad-hoc Deblurring in the Wild

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arXiv 2023年

作者： Sun, Lei Gehrig, Daniel Sakaridis, Christos Gehrig, Mathias Liang, Jingyun Sun, Peng Xu, Zhijie Wang, Kaiwei Van Gool, Luc Scaramuzza, Davide National Research Center for Optical Instrumentation Zhejiang University Hangzhou310027 China Robotics and Perception Group University of Zurich Zurich8050 Switzerland Computer Vision Lab ETH Zurich Zurich8092 Switzerland Centre for Visual and Immersive Computing Huddersfield University HD1 3DH United Kingdom INSAIT Sofia University "St. Kliment Ohridski" Bulgaria

Effective video frame interpolation hinges on the adept handling of motion in the input scene. Prior work acknowledges asynchronous event information for this, but often overlooks whether motion induces blur in the video, limiting its scope to sharp frame interpolation. We instead propose a unified framework for event-based frame interpolation that performs deblurring ad-hoc and thus works both on sharp and blurry input videos. Our model consists in a bidirectional recurrent network that incorporates the temporal dimension of interpolation and fuses information from the input frames and the events adaptively based on their temporal proximity. To enhance the generalization from synthetic data to real event cameras, we integrate self-supervised framework with the proposed model to enhance the generalization on real-world datasets in the wild. At the dataset level, we introduce a novel real-world high-resolution dataset with events and color videos named HighREV, which provides a challenging evaluation setting for the examined task. Extensive experiments show that our network consistently outperforms previous state-of-the-art methods on frame interpolation, single image deblurring, and the joint task of both. Experiments on domain transfer reveal that self-supervised training effectively mitigates the performance degradation observed when transitioning from synthetic data to real-world data. Code and datasets are available at https://***/AHupuJR/REFID. Copyright © 2023, The Authors. All rights reserved.

关键词： Self-supervised learning

Robotic workcell for sole grasping in footwear manufacturing

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Robotic workcell for sole grasping in footwear manufacturing

International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Guillermo Oliver Pablo Gil Fernando Torres Automatics Robotics and Artificial Vision Lab (AUROVA). Computer Science Research Institute University of Alicante Spain

ISBN: (数字)9781728189567

ISBN: (纸本)9781728189574

The goal of this paper is to present a robotic workcell to automate several tasks of the cementing process in footwear manufacturing. Our cell's main applications are sole digitization of a wide variety of footwear, glue dispensing and sole grasping from conveyor belts. This cell is made up of a manipulator arm endowed with a gripper, a conveyor belt and a 3D scanner. We have integrated all the elements into a ROS simulation environment facilitating control and communication among them, also providing flexibility to support future extensions. We propose a novel method to grasp soles of different shape, size and material, exploiting the particular characteristics of these objects. Our method relies on object contour extraction using concave hulls. We evaluate it on point clouds of 16 digitized real soles in three different scenarios: concave hull, k-NNs extension and PCA correction. While we have tested this workcell in a simulated environment, the presented system's performance is scheduled to be tested on a real setup at INESCOP facilities in the upcoming months.

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Cross Domain Object Detection by Target-Perceived Dual Branch Distillation

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arXiv 2022年

作者： He, Mengzhe Wang, Yali Wu, Jiaxi Wang, Yiru Li, Hanqing Li, Bo Gan, Weihao Wu, Wei Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China SenseTime Research University of Chinese Academy of Science China Shanghai AI Laboratory Shanghai China Beihang University China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Cross domain object detection is a realistic and challenging task in the wild. It suffers from performance degradation due to large shift of data distributions and lack of instance-level annotations in the target domain. Existing approaches mainly focus on either of these two difficulties, even though they are closely coupled in cross domain object detection. To solve this problem, we propose a novel Target-perceived Dual-branch Distillation (TDD) framework. By integrating detection branches of both source and target domains in a unified teacher-student learning scheme, it can reduce domain shift and generate reliable supervision effectively. In particular, we first introduce a distinct Target Proposal Perceiver between two domains. It can adaptively enhance source detector to perceive objects in a target image, by leveraging target proposal contexts from iterative cross-attention. Afterwards, we design a concise Dual Branch Self Distillation strategy for model training, which can progressively integrate complementary object knowledge from different domains via self-distillation in two branches. Finally, we conduct extensive experiments on a number of widely-used scenarios in cross domain object detection. The results show that our TDD significantly outperforms the state-of-the-art methods on all the benchmarks. Our code and model will be available at here. Copyright © 2022, The Authors. All rights reserved.

关键词： Distillation

RDA: An Accelerated Collision Free Motion Planner for Autonomous Navigation in Cluttered Environments

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arXiv 2022年

作者： Han, Ruihua Wang, Shuai Wang, Shuaijun Zhang, Zeqing Zhang, Qianru Eldar, Yonina C. Hao, Qi Pan, Jia The Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Shenzhen China The Department of Computer Science The University of Hong Kong Hong Kong Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China The Department of Computer Science and Engineering Harbin Institute of Technology Guangdong Shenzhen China The Weizmann Institute of Science Rehovot Israel The Department of Computer Science and Engineering The Shenzhen Key Laboratory of Robotics and Computer Vision The Sifakis Research Institute for Trustworthy Autonomous Systems Southern University of Science and Technology Guangdong Shenzhen China

Autonomous motion planning is challenging in multi-obstacle environments due to nonconvex collision avoidance constraints. Directly applying numerical solvers to these nonconvex formulations fails to exploit the constraint structures, resulting in excessive computation time. In this paper, we present an accelerated collision-free motion planner, namely regularized dual alternating direction method of multipliers (RDADMM or RDA for short), for the model predictive control (MPC) based motion planning problem. The proposed RDA addresses nonconvex motion planning via solving a smooth biconvex reformulation via duality and allows the collision avoidance constraints to be computed in parallel for each obstacle to reduce computation time significantly. We validate the performance of the RDA planner through path-tracking experiments with car-like robots in both simulation and real-world settings. Experimental results show that the proposed method generates smooth collision-free trajectories with less computation time compared with other benchmarks and performs robustly in cluttered environments. The source code is available at https://***/hanruihua/ RDA_planner. Copyright © 2022, The Authors. All rights reserved.

关键词： Motion planning