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IEEE International Conference on robotics and Biomimetics

作者： Yang Zhou Shiqiang Zhu Huang Huang Yuehua Li Jason Gu Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Beijing Institute of Control Engineering Beijing China Beijing Research Institute of Zhejiang Laboratory Beijing China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

ISBN: (纸本)9781665481106

Dense map that contains the surrounding geometry and vision information of a robot is widely used for path planning, navigation, obstacle avoidance and other applications. Considering the performance of the processing unit mounted on the robot is limited, mapping algorithm has to make compromise by sacrificing speed and precision. It will be more challenging when the dense mapping scene is very large because the memory consumption will be greatly increased and the map is difficult to be extended if beyonding the initial map. To suppress the negative impact from the increased map scale, we proposed a novel block mapping approach to generate the dense map in large scale of scene. In this work, the elevation map is selected as the base dense map. The entire elevation map is segmented into numerous block maps of which size is much smaller than that of the entire map. The present moment of lidar and vision measurements are used to generate the local elevation map. The local elevation map is used to update block maps which are adaptively generated along the motion trajectory. A memory-disk interaction mechanism, which the block maps will be loaded to memory or saved to local disk when needed, is introduced. Our block mapping approach is tested on the KITTI datasets, and the results demonstrate that the mapping approach can stably operate in a large scale of scene with a much smaller consumption of memory.

关键词： Geometry Laser radar Navigation Motion segmentation Memory management Robot sensing systems Trajectory

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Leveraging convergence behavior to balance conflicting tasks in multi-task learning

arXiv

引用

arXiv 2022年

作者： Nakamura, Angelica Tiemi Mizuno Grassi, Valdir Wolf, Denis Fernando Mobile Robotics Laboratory Institute of Mathematics and Computer Sciences University of São Paulo São Carlos Brazil Department of Electrical and Computer Engineering São Carlos School of Engineering University of São Paulo São Carlos Brazil

Multi-Task Learning is a learning paradigm that uses correlated tasks to improve performance generalization. A common way to learn multiple tasks is through the hard parameter sharing approach, in which a single architecture is used to share the same subset of parameters, creating an inductive bias between them during the training process. Due to its simplicity, potential to improve generalization, and reduce computational cost, it has gained the attention of the scientific and industrial communities. However, tasks often conflict with each other, which makes it challenging to define how the gradients of multiple tasks should be combined to allow simultaneous learning. To address this problem, we use the idea of multi-objective optimization to propose a method that takes into account temporal behaviour of the gradients to create a dynamic bias that adjust the importance of each task during the backpropagation. The result of this method is to give more attention to the tasks that are diverging or that are not being benefited during the last iterations, allowing to ensure that the simultaneous learning is heading to the performance maximization of all tasks. As a result, we empirically show that the proposed method outperforms the state-of-art approaches on learning conflicting tasks. Unlike the adopted baselines, our method ensures that all tasks reach good generalization performances. © 2022, CC BY.

关键词： Multiobjective optimization

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EI-Drive: A Platform for Cooperative Perception with Realistic Communication Models

arXiv

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arXiv 2024年

作者： Zhou, Hanchu Xie, Edward Shao, Wei Gao, Dechen Dong, Michelle Zhang, Junshan Davis AI Robotics and Edge Laboratory University of California DavisCA95616 United States Electrical and Computer Engineering Department of Johns Hopkins University BaltimoreMD21218 United States data61 CSIRO ClaytonVIC Australia Monta Vista High School CupertinoCA95014 United States

The growing interest in autonomous driving calls for realistic simulation platforms capable of accurately simulating cooperative perception process in realistic traffic scenarios. Existing studies for cooperative perception often have not accounted for transmission latency and errors in real-world environments. To address this gap, we introduce EI-Drive, an edge-AI based autonomous driving simulation platform that integrates advanced cooperative perception with more realistic communication models. Built on the CARLA framework, EI-Drive features new modules for cooperative perception while taking into account transmission latency and errors, providing a more realistic platform for evaluating cooperative perception algorithms. In particular, the platform enables vehicles to fuse data from multiple sources, improving situational awareness and safety in complex environments. With its modular design, EI-Drive allows for detailed exploration of sensing, perception, planning, and control in various cooperative driving scenarios. Experiments using EI-Drive demonstrate significant improvements in vehicle safety and performance, particularly in scenarios with complex traffic flow and network conditions. All code and documents are accessible on our GitHub page: https://***/***/. © 2024, CC BY-NC-SA.

关键词： Autonomous vehicles

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Reinforcement Learning with Large Language Model for Hybrid Disassembly Lines in Remanufacturing Contexts

Reinforcement Learning with Large Language Model for Hybrid ...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Peng Ji Xi Wang Guo Jiacun Wang Weitian Wang Shu Jin Qin Ying Tang Qi Kang College of Information and Control Engineering Liaoning Petrochemical University Fushun China Dept of Comp. Sci. and Soft. Eng. Monmouth University W. Long Branch NJ USA Collaborative Robotics and Smart Systems Laboratory (CRoSS Lab) Montclair State University Montclair NJ USA College of Economics and Management Shangqiu Normal University Shangqiu China Electrical and Computer Engineering Rowan University Glassboro New Jersey USA Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Large language models (LLM), ChatGPT is making substantial impact across various fields. This study for the first time presents a novel approach to the hybrid disassembly line balancing problem using LLM and reinforcement learning algorithms in remanufacturing contexts. The problem is divided into two sub-stages. LLM is innovatively used to capture a disassembly sequence well in the first stage, while reinforcement learning is utilized to address the problem in the second stage. Upon comparing the performance with and without LLM, the proposed approach significantly reduces the trial-and-error space and achieves faster convergence to achieve the desired solution. Future work of this study is also discussed.

关键词： computer aided software engineering Automation Large language models Reinforcement learning Chatbots Convergence

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Modelling with NGSI-LD: the VALLPASS project case study

Modelling with NGSI-LD: the VALLPASS project case study

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Tiago Ribeiro João Paulo Coelho Luísa Jorge Joaquim Sardão José Gonçalves Higor Rosse CeDRI - Research Center for Digitization and Intelligent Robotics SusTEC - Associate Laboratory for Sustainability and Technology in Mountains Regions Bragança Portugal Department of Electrical and Computer Engineering INESC Coimbra University of Coimbra Polo 2 Coimbra Portugal Valled Lda Bragança Portugal Intelligent Technologies Collaborative Laboratory Mountains of Research Bragança Portugal

The smart cities paradigm covers multiple domains which span from citizens’ accessibility and mobility to general infrastructures and services. Hence, smart cities can be seen as an excellent showcase of heterogeneity, namely at the data level. For this reason, they are a perfect candidate for linked data and semantic web concept applications. This powerful combination leads to interoperability at the data level which is one of the ultimate goals of the Internet of Things (IoT). In this reference frame, NGSI-LD is an open framework for context information processing consisting of both a semantic information model and a RESTful Application Programming Interface (API). This paper proposes a methodology for creating semantic data models in the context of IoT, namely to represent and describe data associated with digital twins. The methodology is presented in a practical way, through the process of creating an NGSI-LD semantic data model for the VALLPASS project, inserted in the traffic domain, which is one of the most popular in smart cities.

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STEV: Stretchable Triboelectric E-skin enabled Proprioceptive Vibration Sensing for Soft Robot

STEV: Stretchable Triboelectric E-skin enabled Proprioceptiv...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Zihan Wang Kai-Chong Lei Huaze Tang Shoujie Li Yuan Dai Wenbo Ding Xiao-Ping Zhang Tsinghua-Berkeley Shenzhen Institute Shenzhen International Graduate School Tsinghua University China Tencent Robotics X Lab Shenzhen China RISC-V International Open Source Laboratory Shenzhen China Department of Electrical Computer and Biomedical Engineering Ryerson University Toronto ON Canada

Vibration perception is essential for robotic sensing and dynamic control. Nevertheless, due to the rigorous demand for sensor conformability and stretchability, enabling soft robots with proprioceptive vibration sensing remains challenging. This paper proposes a novel liquid metal-based stretchable e-skin via a kirigami-inspired design to enable soft robot proprioceptive vibration sensing. The e-skin is fabricated into 0.1mm ultrathin thickness, ensuring its negligible influence on the overall stiffness of the soft robot. Moreover, the working mechanism of the e-skin is based on the ubiquitous triboelectrification effect, which transduces mechanical stimuli without external power supply. To demonstrate the practicability of the e-skin, we built a soft gripper consisting of three soft robotic fingers with proprioceptive vibration sensing. Our experiment shows that the gripper can accurately distinguish the grain category (six grains with the same mass, 99.9% accuracy) and the packaging quality (100% accuracy) by simply shaking the gripped bottle. In summary, a soft robotic proprioceptive vibration sensing solution is proposed; it helps soft robots to have a more comprehensive awareness of their self-state and may inspire further research on soft robotics.

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Automatic Search for Photoacoustic Marker Using Automated Transrectal Ultrasound

arXiv

引用

arXiv 2023年

作者： Wu, Zijian Moradi, Hamid Yang, Shuojue Song, Hyunwoo Boctor, Emad M. Salcudean, Septimiu E. Johns Hopkins University Laboratory for Computational Sensing and Robotics BaltimoreMD United States University of British Columbia Dept. of Electrical and Computer Engineering VancouverBC Canada University of Texas at Austin Walker Department of Mechanical Engineering AustinTX United States Johns Hopkins University Dept. of Computer Science BaltimoreMD United States

Real-time transrectal ultrasound (TRUS) image guidance during robot-assisted laparoscopic radical prostatectomy has the potential to enhance surgery outcomes. Whether conventional or photoacoustic TRUS is used, the robotic system and the TRUS must be registered to each other. Accurate registration can be performed using photoacoustic (PA markers). However, this requires a manual search by an assistant [19]. This paper introduces the first automatic search for PA markers using a transrectal ultrasound robot. This effectively reduces the challenges associated with the da Vinci-TRUS registration. This paper investigated the performance of three search algorithms in simulation and experiment: Weighted Average (WA), Golden Section Search (GSS), and Ternary Search (TS). For validation, a surgical prostate scenario was mimicked and various ex vivo tissues were tested. As a result, the WA algorithm can achieve 0.53o ± 0.30o average error after 9 data acquisitions, while the TS and GSS algorithm can achieve 0.29° ± 0.31° and 0.48o ± 0.32o average errors after 28 data acquisitions. © 2023, CC BY.

关键词： Urology

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MG-SLAM: Structure Gaussian Splatting SLAM with Manhattan World Hypothesis

arXiv

引用

arXiv 2024年

作者： Liu, Shuhong Deng, Tianchen Zhou, Heng Li, Liuzhuozheng Wang, Hongyu Wang, Danwei Li, Mingrui Department of Information Science and Technology Department of Complexity Science and Engineering The University of Tokyo Tokyo113-8654 Japan Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Department of Mechanical Engineering Columbia University New York10027 United States School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Computer Science Dalian University of Technology Dalian116024 China

Gaussian Splatting SLAMs have made significant advancements in improving the efficiency and fidelity of real-time reconstructions. However, these systems often encounter incomplete reconstructions in complex indoor environments, characterized by substantial holes due to unobserved geometry caused by obstacles or limited view angles. To address this challenge, we present Manhattan Gaussian SLAM, an RGB-D system that leverages the Manhattan World hypothesis to enhance geometric accuracy and completeness. By seamlessly integrating fused line segments derived from structured scenes, our method ensures robust tracking in textureless indoor areas. Moreover, The extracted lines and planar surface assumption allow strategic interpolation of new Gaussians in regions of missing geometry, enabling efficient scene completion. Extensive experiments conducted on both synthetic and real-world scenes demonstrate that these advancements enable our method to achieve state-of-the-art performance, marking a substantial improvement in the capabilities of Gaussian SLAM systems. © 2024, CC BY.

关键词： Gaussian distribution

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A Lightweight Blockchain and Fog-enabled Secure Remote Patient Monitoring System

arXiv

引用

arXiv 2023年

作者： Cheikhrouhou, Omar Mershad, Khaleel Jamil, Faisal Mahmud, Redowan Koubaa, Anis Moosavi, Sanaz Rahimi Ces Laboratory University of Sfax Tunisia Computer Science and Mathematics Department Lebanese American University Beirut Lebanon Department of Computer Engineering Jeju National University Korea Republic of School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia Robotics and Internet of Things Lab Prince Sultan University Riyadh Saudi Arabia United States Isima University of Monastir Mahdia Tunisia

IoT has enabled the rapid growth of smart remote healthcare applications. These IoT-based remote healthcare applications deliver fast and preventive medical services to patients at risk or with chronic diseases. However, ensuring data security and patient privacy while exchanging sensitive medical data among medical IoT devices is still a significant concern in remote healthcare applications. Altered or corrupted medical data may cause wrong treatment and create grave health issues for patients. Moreover, current remote medical applications' efficiency and response time need to be addressed and improved. Considering the need for secure and efficient patient care, this paper proposes a lightweight Blockchain-based and Fogenabled remote patient monitoring system that provides a high level of security and efficient response time. Simulation results and security analysis show that the proposed lightweight blockchain architecture fits the resource-constrained IoT devices well and is secure against attacks. Moreover, the augmentation of Fog computing improved the responsiveness of the remote patient monitoring system by 40%. © 2023, CC BY-NC-ND.

关键词： Fog computing

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Conceptualizing UAM: Technologies and Methods for Safe and Efficient Urban Air Transportation

引用

Green Energy and Intelligent Transportation 2025年

作者： Lidia Zewde Ioannis A. Raptis North Carolina A&T State University Department of Electrical and Computer Engineering Autonomous Robotics Systems Laboratory Greensboro USA

The effective integration of Urban Air Mobility (UAM) hinges upon the adoption of a comprehensive approach that harmoniously melds various network components, placing paramount importance on the pillars of safety, sustainability, and efficiency. Existing technologies are currently undergoing a transformative evolution to cater to the distinctive requirements of UAM, with an unwavering commitment to enhancing safety, sustainability, and efficiency. This paper meticulously elucidates the extant technologies and methodologies that pertain to the safe and efficient realm of air transportation while delving into the perspective of key UAM network components: (1) Aircraft classification, dimensions, and operational technology; (2) Airspace typology and structural intricacies; and (3) Air Traffic Management (ATM) services. In conclusion, this paper culminates by offering insights into prospective research directions in this burgeoning field.

关键词： Urban Aerial Mobility Electric Vertical Takeoff and Landing Urban Airspace Aircraft Traffic Management Aircraft Communication Navigation and Surveillance Ground Infrastructure Concept of Operations

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