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arXiv 2024年

作者： Jiao, Jianhao Geng, Ruoyu Li, Yuanhang Xin, Ren Yang, Bowen Wu, Jin Wang, Lujia Liu, Ming Fan, Rui Kanoulas, Dimitrios Department of Computer Science University College London LondonWC1E 6BT United Kingdom Nansha Guangdong Guangzhou511400 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Nansha Guangdong Guangzhou511400 China College of Electronics and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems The Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China

The creation of a metric-semantic map, which encodes human-prior knowledge, represents a high-level abstraction of environments. However, constructing such a map poses challenges related to the fusion of multi-modal sensor data, the attainment of real-time mapping performance, and the preservation of structural and semantic information consistency. In this paper, we introduce an online metric-semantic mapping system that utilizes LiDAR-Visual-Inertial sensing to generate a global metric-semantic mesh map of large-scale outdoor environments. Leveraging GPU acceleration, our mapping process achieves exceptional speed, with frame processing taking less than 7ms, regardless of scenario scale. Furthermore, we seamlessly integrate the resultant map into a real-world navigation system, enabling metric-semantic-based terrain assessment and autonomous point-to-point navigation within a campus environment. Through extensive experiments conducted on both publicly available and self-collected datasets comprising 24 sequences, we demonstrate the effectiveness of our mapping and navigation methodologies. Code has been publicly released: https://***/gogojjh/cobra. Note to Practitioners—This paper tackles the challenge of autonomous navigation for mobile robots in complex, unstructured environments with rich semantic elements. Traditional navigation relies on geometric analysis and manual annotations, struggling to differentiate similar structures like roads and sidewalks. We propose an online mapping system that creates a global metric-semantic mesh map for large-scale outdoor environments, utilizing GPU acceleration for speed and overcoming the limitations of existing real-time semantic mapping methods, which are generally confined to indoor settings. Our map integrates into a real-world navigation system, proven effective in localization and terrain assessment through experiments with both public and proprietary datasets. Future work will focus on integrating kernel-based

关键词： Mesh generation

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Multi-view Inspection of Flare Stacks Operation Using a Vision-controlled autonomous UAV

Multi-view Inspection of Flare Stacks Operation Using a Visi...

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Annual Conference of Industrial Electronics Society

作者： Muaz Al Radi Pengfei Li Hamad Karki Naoufel Werghi Sajid Javed Jorge Dias Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE Center for Autonomous Robotic Systems Khalifa University Abu Dhabi UAE Department of Mechanical Engineering Khalifa University Abu Dhabi UAE

Flare stacks are crucial safety control components in petrochemical plants that required efficient monitoring and inspection. In this work, an Unmanned Aerial Vehicle (UAV)-based multi-view operation inspection system for monitoring and assessing the operation of flare stacks is proposed. Image-Based Visual Servoing (IBVS) control is used to guide the autonomous UAV for multi-view visual data collection. Afterwards, the collected visual data is analyzed using a new Multi-View Convolutional Neural Network (MV-CNN) deep learning model to obtain useful conclusions on the system's operation and classify the current state of the observed system. The proposed system's performance was validated in a simulated petrochemical plant environment with operational flare stacks and the results showed superior performance of the proposed MV-CNN model compared to a conventional single-view CNN model.

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Shortest Trajectory of a Dubins Vehicle with a controllable Laser

arXiv

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arXiv 2024年

作者： Bajaj, Shivam Jha, Bhargav Bopardikar, Shaunak D. Von Moll, Alexander Casbeer, David W. The Electrical and Computer Engineering Department Purdue University West LafayetteIN47906 United States The Electrical and Computer Engineering Department Michigan State University East LansingMI48823 United States Control Science Center Air Force Research Laboratory Ohio United States

We formulate a novel planar motion planning problem for a Dubins-Laser system that consists of a Dubins vehicle with an attached controllable laser. The vehicle moves with unit speed and the laser, having a finite range, can rotate in a clockwise or anti-clockwise direction with a bounded angular rate. From an arbitrary initial position and orientation, the objective is to steer the system so that a given static target is within the range of the laser and the laser is oriented at it in minimum time. We characterize multiple properties of the optimal trajectory and establish that the optimal trajectory for the Dubins-laser system is one out of a total of 16 candidates. Finally, we provide numerical insights that illustrate the properties characterized in this work. © 2024, CC BY-NC-SA.

关键词： Vehicles

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An Image Acquisition Scheme for Visual Odometry based on Image Bracketing and Online Attribute control

An Image Acquisition Scheme for Visual Odometry based on Ima...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Shuyang Zhang Jinhao He Bohuan Xue Jin Wu Pengyu Yin Jianhao Jiao Ming Liu Department of Electronic and Computer Engineering the Hong Kong University of Science and Technology Hong Kong SAR China Thrust of Robotics and Autonomous Systems the Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Center for Advanced Robotics Technology Innovation (CARTIN) School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Computer Science University College London London UK

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Visual odometry (VO) system is challenged by complex illumination environments. Image quality and its consistency in the time domain directly determine feature detection and tracking performance, which further affect the robustness and accuracy of the entire system. In this paper, an image acquisition scheme with image bracketing patterns is proposed. Images with different exposure levels are continuously captured to sufficiently explore the scene under varying illumination. An attribute control method is designed to adjust image exposures within the brackets online. Gaussian process regression fits the relationship between image quality metric and exposure via image synthesis technique. The optimal exposures for the next bracket are obtained directly without attempts to ensure a quick response. Experiments show our acquisition system’s effectiveness and performance improvement for VO tasks in complex illumination scenes.

关键词： Image quality Measurement Image synthesis Lighting Gaussian processes Robustness Time-domain analysis

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Vision-Based Analytics of Flare Stacks Using Deep Learning Detection

Vision-Based Analytics of Flare Stacks Using Deep Learning D...

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International Conference on Advanced Robotics (ICAR)

作者： Muaz Al Radi Said Boumaraf Hamad Karki Jorge Dias Naoufel Werghi Sajid Javed Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE Center for Autonomous Robotic Systems Khalifa University Abu Dhabi UAE Department of Mechanical Engineering Khalifa University Abu Dhabi UAE

Flare stacks play a critical role in oil refineries and chemical plants, but monitoring their performance is a challenging task that often requires skilled operators. To address this challenge, we propose a novel approach that combines video capturing and machine learning techniques to automate the monitoring of flare stack operations in real-time. Our vision-based system analyzes captured video footage of the flare stack's scene and employs state-of-the-art deep learning detection models, including YOLOv5, YOLOv7, and the Detection Transformer (DETR), to detect and analyze combustion-related objects such as flame and smoke. Rigorous experiments show that the proposed technique was able to accurately detect flame and smoke objects in flare stacks scene and the best model showed encouraging performance metrics. By leveraging the power of recent deep detection models, our proposed system offers a promising alternative to labor-intensive manual inspection by keeping a continuous and automated watchable eye in combustion quality, facilitating more efficient and reliable flare stack operation analysis.

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Design and Deployment of a Simulation Platform: Case Study of an Agent-Based Model for Youth Suicide Prevention 22

Design and Deployment of a Simulation Platform: Case Study o...

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Proceedings of the Winter Simulation Conference

作者： Joshua Huddleston Michael C. Galgoczy Kareem A. Ghumrawi Philippe J. Giabbanelli Ketra L. Rice Nisha Nataraj Margaret M. Brown Christopher R. Harper Curtis S. Florence Department of Computer Science & Software Engineering Miami University Oxford OH USA National Center for Injury Prevention and Control Centers for Disease Control and Prevention (CDC) Atlanta GA USA

Research has examined the process of engaging end-users in co-designing a simulation model and using it within workplace settings. In contrast, few studies document the software development aspect of creating, packaging, and deploying a simulation platform that satisfies end-users' needs. In this case study, we detail these aspects regarding a platform involving Agent-Based Modeling for youth suicide prevention. Goals and constraints are detailed in three categories of needs, each showing how competing demands are addressed via software solutions: 1) data, encompassing data security (e.g., no direct access to individual-level data) and data updates (e.g., changing a model's initialization without coding expertise); 2) deployment, to satisfy security protocols (e.g., secure intranet communications) and the end-users' experience (e.g., ease of installation); 3) accessibility, ensuring that individuals with impairments are not excluded from using simulations. By presenting practical solutions to each category, our case study supports modelers in addressing their own deployment needs.

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Infrastructure-dependent ramp-metering control for METANET-s

Infrastructure-dependent ramp-metering control for METANET-s

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IEEE Conference on Decision and control

作者： Ayda Kamalifar Carlo Cenedese Michele Cucuzzella Antonella Ferrara Department of Electrical Computer Science and Biomedical Engineering University of Pavia Pavia Italy Department of Information Technology and Electrical Engineering ETH Zürich Zürich Switzerland Center for Systems and Control ENTEG Faculty of Science and Engineering (FSE) University of Groningen (UG) Nijenborgh 4 Groningen the Netherlands

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we propose a novel infrastructure-dependent ramp-metering control for the recently proposed METANET with service station (METANET-s) model, i.e., a second-order macroscopic traffic model that, compared to the classical METANET, incorporates the dynamics of service stations on highways. We study the effect of a ramp-metering control scheme on a highway stretch with a service station and show that it is capable of actively regulate internal traffic demand attempting to exit the service station via its on-ramp, on top of contributing to decrease the traffic congestion on the mainstream. In fact, the proposed control scheme effectively prevents the backlog of vehicles attempting to merge back onto the mainstream. This dynamic control mechanism is further endowed by a route guidance control strategy increasing the share of vehicles stopping at the service station during main-stream congestion periods, e.g. via incentives. The combined effect of our control schemes allows to take full advantage of the presence of service stations, reducing the overall traffic congestion. Simulation results demonstrate the effectiveness of the proposed control strategies.

关键词： Simulation Merging Numerical models Vehicle dynamics Traffic congestion

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Locate n' Rotate: Two-stage Openable Part Detection with Foundation Model Priors

arXiv

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arXiv 2024年

作者： Li, Siqi Chen, Xiaoxue Cheng, Haoyu Zhou, Guyue Zhao, Hao Tian, Guanzhong Ningbo innovation Center Zhejiang University China College of Control Science and Engineering Zhejiang University China Institute for AI Industry Research Tsinghua University China Department of Computer Science and Technology Tsinghua University China

Detecting the openable parts of articulated objects is crucial for downstream applications in intelligent robotics, such as pulling a drawer. This task poses a multitasking challenge due to the necessity of understanding object categories and motion. Most existing methods are either category-specific or trained on specific datasets, lacking generalization to unseen environments and objects. In this paper, we propose a Transformer-based Openable Part Detection (OPD) framework named Multi-feature Openable Part Detection (MOPD) that incorporates perceptual grouping and geometric priors, outperforming previous methods in performance. In the first stage of the framework, we introduce a perceptual grouping feature model that provides perceptual grouping feature priors for openable part detection, enhancing detection results through a cross-attention mechanism. In the second stage, a geometric understanding feature model offers geometric feature priors for predicting motion parameters. Compared to existing methods, our proposed approach shows better performance in both detection and motion parameter prediction. Codes and models are publicly available at https://***/lisiqizju/MOPD. © 2024, CC BY.

关键词： Intelligent robots

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Sensor-based Multi-agent Coverage control with Spatial Separation in Unstructured Environments

arXiv

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arXiv 2024年

作者： Wang, Xinyi Xu, Jiwen Gao, Chuanxiang Chen, Yizhou Zhang, Jihan Wang, Chenggang Ding, Yulong Chen, Ben M. Department of Mechanical and Automation Engineering The Chinese University of Hong Kong N.T. Shatin Hong Kong Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai200240 China Department of Control Science and Engineering Tongji University Shanghai China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education China

Multi-robot systems have increasingly become instrumental in tackling coverage problems. However, the challenge of optimizing task efficiency without compromising task success still persists, particularly in expansive, unstructured scenarios with dense obstacles. This paper presents an innovative, decentralized Voronoi-based coverage control approach to reactively navigate these complexities while guaranteeing safety. This approach leverages the active sensing capabilities of multi-robot systems to supplement GIS (Geographic Information System), offering a more comprehensive and real-time understanding of environments like post-disaster. Based on point cloud data, which is inherently non-convex and unstructured, this method efficiently generates collision-free Voronoi regions using only local sensing information through spatial decomposition and spherical mirroring techniques. Then, deadlock-aware guided map integrated with a gradient-optimized, centroid Voronoi-based coverage control policy, is constructed to improve efficiency by avoiding exhaustive searches and local sensing pitfalls. The effectiveness of our algorithm has been validated through extensive numerical simulations in high-fidelity environments, demonstrating significant improvements in task success rate, coverage ratio, and task execution time compared with others. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots

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Linear Encryption Techniques for Counteracting Information-based Stealthy Attacks

Linear Encryption Techniques for Counteracting Information-b...

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Jun Shang Hanwen Zhang Weixiong Rao Yiguang Hong Department of Control Science and Engineering National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Sci-ence and Technology Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China School of Software Engineering Tongji University Shanghai China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

This study explores linear encryption techniques to protect against information-based stealthy attacks on re-mote state estimation. Utilizing smart sensors equipped with local Kalman filters, the system transmits innovations rather than raw measurements via wireless networks. However, this transmission is susceptible to malicious data interception and manipulation by attackers. To safeguard against these stealthy threats, encryption and decryption modules are integrated into the system. This research aims to assess the effectiveness of the encryption strategy when faced with information-based stealthy attacks. A key contribution of this paper is the adoption of the most comprehensive attack models, moving away from the conventional reliance on innovation-based linear attack models. Our results demonstrate that the proposed linear encryption approach effectively mitigates stealthy attacks under certain mild conditions. The efficacy of the encryption is further validated through numerical examples, corroborating the theoretical advancements presented in this paper.

关键词： Technological innovation Automation Wireless networks Encryption Numerical models Kalman filters State estimation Robots Intelligent sensors

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