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1st IEEE Conference on Telepresence, Telepresence 2024

作者： Singh, Harsimran Marcilla, Luis Perez Banerjee, Premankur Rothammer, Michael Hulin, Thomas The Institute of Robotics and Mechatronics Munich Germany Dresden51147 Germany University of Southern California Department of Computer Science United States Hapticlabs Germany

ISBN: (纸本)9798331528249

This paper presents the proof of concept of the modular nature of ferrofluid based tactile device, FerroVibe. The modularity of FerroVibe allows for customization in degrees of freedom, force feedback, and vibrational cues by strategically positioning magnetic actuators around the central assembly containing a neodymium magnet and ferrofluid. Three distinct configurations of FerroVibe are introduced, each offering unique actuation mechanisms and physical characteristics that could be tailored to different application requirements. The paper showcases the proof of concept and details the working principle of each configuration, highlighting the strengths and limitations of each design. Initial comparative analysis is conducted to evaluate the trade-offs in terms of power consumption, speed, size, weight, and feedback fidelity. The results demonstrate that FerroVibe's potential modular architecture provides a flexible and effective solution for a wide range of tactile feedback applications, making it a promising candidate for future advancements in haptic technology. © 2024 IEEE.

关键词： Magnetic actuators

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Versatile Tasks on Integrated Aerial Platforms Using Only Onboard Sensors: Control, Estimation, and Validation

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IEEE Transactions on robotics 2025年 41卷 3518-3538页

作者： Wang, Kaidi Lai, Ganghua Yu, Yushu Du, Jianrui Sun, Jiali Xu, Bin Franchi, Antonio Sun, Fuchun Beijing Institute of Technology School of Mechatronical Engineering Beijing100081 China Beijing Institute of Technology School of Mechanical Engineering Beijing100081 China University of Twente Robotics and Mechatronics Lab Faculty of Electrical Engineering Mathematics and Computer Science Enschede7500 AE Netherlands Tsinghua University Department of Computer Science and Technology China

Connecting multiple aerial vehicles to a rigid central platform through passive spherical joints holds the potential to construct a fully-actuated aerial platform. The integration of multiple vehicles enhances efficiency in tasks like mapping and object reconnaissance. This paper proposes a control and state estimation framework for the Integrated Aerial Platform (IAP), enabling it to perform versatile tasks like object reconnaissance and physical interactive tasks with only onboard sensors. In the framework, the 6D motion control serves as the low-level controller, while the high-level controller comprises a 6D admittance filter and a perception-aware attitude correction module. The 6D admittance filter, serving as the interaction controller, is adaptable for aerial interaction tasks. The perception-aware attitude correction algorithm is carefully designed by adopting a geometric Model Predictive Controller (MPC). This algorithm, incorporating both offline and online calculations, proves to be well-suited for the intricate dynamics of an IAP. A 6D direct wrench controller is also developed for the IAP. Notably, both the interaction controller and the direct wrench controller operate without reliance on force/torque sensors. Instead, a wrench observer algorithm is devised, considering external disturbances. Additionally, based on the kinematics constraints of the multiple aerials in the platform, a fusion algorithm for multiple Visual-Inertial Odometry (VIO) and kinematics constraints is developed, providing more accurate localization. A prototype of the IAP is constructed, and its capabilities are demonstrated through experiments including perception-aware object reconnaissance, aerial mapping, aerial peg-in-hole task, and 6D contact wrench generation. All experiments are conducted exclusively with onboard sensors. These tasks exemplify the merits of the proposed IAP and validate the effectiveness of the proposed control framework and fusion algorithm. © 2004-2012

关键词： State estimation

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Multi-Label Classification for Low-Resource Issue Tickets with BERT

Multi-Label Classification for Low-Resource Issue Tickets wi...

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2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024

作者： Sungthong, Chawanee Ruangtanusak, Saksorn Songmuang, Pokpong Thammasat University Faculty Of Science And Technology Department Of Computer Science Pathum Thani Thailand Bedrock Analytics Ai And Robotics Ventures Applied Machine Learning Team Bangkok Thailand

ISBN: (纸本)9798350370096

This study focuses on the problem in the field of issue ticketing systems, where the need for human involvement in classifying work task in the ticket often results in insufficient classification. Our primary focus is on a problem associated with multi-label classification. A significant challenge in this study is the dependency of our dataset on low resources, which is different from the large datasets usually used in current multi-label text classification studies. Therefore, we propose a pre-training approach for our low-resource dataset. Our methodology applies several embedding techniques, including TF-IDF, Word2Vec, and BERT (WangchanBERTa and PhayaThaiBERT). The classification models, such as Logistic Regression, Support Vector Classifiers, Decision Trees, Neural Networks, and Deep Neural Networks, are experimented in this study to assess their performance in a low-resource environment. According to our experimental results, a combination of PhayaThaiBERT and Deep Neural Networks outperforms conventional methods. The combination yielded an F1 score of 0.769 on the test dataset. © 2024 IEEE.

关键词： Deep neural networks

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RoboMamba: Efficient Vision-Language-Action Model for Robotic Reasoning and Manipulation 38

RoboMamba: Efficient Vision-Language-Action Model for Roboti...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Liu, Jiaming Liu, Mengzhen Wang, Zhenyu An, Pengju Li, Xiaoqi Zhou, Kaichen Yang, Senqiao Zhang, Renrui Guo, Yandong Zhang, Shanghang State Key Laboratory of Multimedia Information Processing School of Computer Science Peking University China AI2Robotics China China

A fundamental objective in robot manipulation is to enable models to comprehend visual scenes and execute actions. Although existing Vision-Language-Action (VLA) models for robots can handle a range of basic tasks, they still face challenges in two areas: (1) insufficient reasoning ability to tackle complex tasks, and (2) high computational costs for VLA model fine-tuning and inference. The recently proposed state space model (SSM) known as Mamba demonstrates promising capabilities in non-trivial sequence modeling with linear inference complexity. Inspired by this, we introduce RoboMamba, an end-to-end robotic VLA model that leverages Mamba to deliver both robotic reasoning and action capabilities, while maintaining efficient fine-tuning and inference. Specifically, we first integrate the vision encoder with Mamba, aligning visual tokens with language embedding through co-training, empowering our model with visual common sense and robotic-related reasoning. To further equip RoboMamba with SE(3) pose prediction abilities, we explore an efficient fine-tuning strategy with a simple policy head. We find that once RoboMamba possesses sufficient reasoning capability, it can acquire manipulation skills with minimal fine-tuning parameters (0.1% of the model) and time. In experiments, RoboMamba demonstrates outstanding reasoning capabilities on general and robotic evaluation benchmarks. Meanwhile, our model showcases impressive pose prediction results in both simulation and real-world experiments, achieving inference speeds 3 times faster than existing VLA models. Our project web page: https://***/view/robomamba-web. © 2024 Neural information processing systems foundation. All rights reserved.

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Drone Swarm Coordination Using Reinforcement Learning for Efficient Wildfires Fighting

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SN computer science 2024年第3期5卷 314页

作者： Blais, Marc-Andrė Akhloufi, Moulay A. Dept. of Computer Science Université de Moncton Moncton NB Canada Perception Robotics and Intelligent Machines (PRIME) Université de Moncton Moncton NB Canada

Natural events, such as wildfires, pose a serious threat to the human population and cause significant environmental and economic damage. As climate change increases the frequency and intensity of extreme natural events, more efficient solutions are required to mitigate their impacts. One proposed solution is the usage of a swarm of drones and unmanned ground vehicles to extinguish wildfires. Controlling a swarm of drones is a challenging task that requires a complex control system capable of providing a good coordination between multiple vehicles. Artificial intelligence, particularly reinforcement learning, has been proposed as a solution for the autonomous control of a swarm. In this paper, we propose a conceptual system based on reinforcement learning and swarm robotics to efficiently combat wildfires. Our system consists of an overwatch drone and multiple payload drones which are responsible for carrying fire suppressants. The goal of our research is to guide a swarm of payload drones to a wildfire while preserving a tight swarm grouping. Each drone is individually controlled and aware of the position of the goal and the positions of other agents. To compare different algorithms, we tested a DQN, Rainbow DQN and FQF using a simulation and four AirSim scenarios. With our approach, we achieve impressive results in controlling a swarm of drones towards a common goal. Our research demonstrates the possibility of utilizing reinforcement learning to control a swarm of drones for wildfire fighting. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Drones Intelligent system Reinforcement learning Swarm Wildfires

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Hand Gestures and Machine Learning: A Novel Path to Number Prediction

Hand Gestures and Machine Learning: A Novel Path to Number P...

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2023 International Conference on Recent Advances in science and Engineering Technology, ICRASET 2023

作者： Karegoudra, Shilpa Veeresha, R.K. Department of Computer Science and Engineering Udupi574110 India Department of Robotics and Artificial Intelligence Udupi574110 India

ISBN: (纸本)9798350306927

The gesture-based communication system proves highly valuable for individuals who are almost deaf and discourse disabled. It allows them to convey messages effectively through gestures. The objective of this application is to provide a platform that accurately interprets these signs, enabling regular individuals to understand the gestures. The system detects and recognizes the gestures and displays the corresponding characters. This technology, which utilizes the PCA method, achieves comparable accuracy to other existing systems and methods. The system we have developed plays a crucial role in enabling individuals to understand and communicate like normal people using static gestures. While various proposals exist for gesture systems with specific languages like ASL, ISL, etc., we introduce a new set of static gestures using MATLAB, building upon existing systems. The system involves capturing input through a camera, after which the system applies preprocessing techniques to enhance the captured image. Next, a set of features is extracted from the preprocessed image using PCA (Principal Component Analysis). To identify the gestures, the system compares these features with those in the training sets, using the Euclidean Distance method. Our hand gesture system has been designed to recognize and interpret numbers from 0 to 5. Once a gesture is identified, the system can produce the output in the form of text ensuring effective communication for the users. © 2023 IEEE.

关键词： Principal component analysis

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Graph-Based Feature Learning for Cross-Project Software Defect Prediction

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computers, Materials & Continua 2023年第10期77卷 161-180页

作者： Ahmed Abdu Zhengjun Zhai Hakim A.Abdo Redhwan Algabri Sungon Lee School of Software Northwestern Polytechnical UniversityXi’anChina School of Computer Science Northwestern Polytechnical UniversityXi’anChina Department of Computer Science Hodeidah UniversityPO Box 3114Al-HudaydahYemen Research Institute of Engineering and Technology Hanyang UniversityAnsanKorea Department of Robotics Hanyang UniversityAnsanKorea

Cross-project software defect prediction(CPDP)aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source *** existing CPDP approaches rely on static metrics or dynamic syntactic features,which have shown limited effectiveness in CPDP due to their inability to capture higher-level system properties,such as complex design patterns,relationships between multiple functions,and dependencies in different software projects,that are important for *** paper introduces a novel approach,a graph-based feature learning model for CPDP(GB-CPDP),that utilizes NetworkX to extract features and learn representations of program entities from control flow graphs(CFGs)and data dependency graphs(DDGs).These graphs capture the structural and data dependencies within the source *** proposed approach employs Node2Vec to transform CFGs and DDGs into numerical vectors and leverages Long Short-Term Memory(LSTM)networks to learn predictive *** process involves graph construction,feature learning through graph embedding and LSTM,and defect *** evaluation using nine open-source Java projects from the PROMISE dataset demonstrates that GB-CPDP outperforms state-of-the-art CPDP methods in terms of F1-measure and Area Under the Curve(AUC).The results showcase the effectiveness of GB-CPDP in improving the performance of cross-project defect prediction.

关键词： Cross-project defect prediction graphs features deep learning graph embedding

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Competition for roadside camera monocular 3D object detection

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National science Review 2023年第6期10卷 34-37页

作者： Jinrang Jia Yifeng Shi Yuli Qu Rui Wang Xing Xu Hai Zhang Baidu Inc. Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University College of Software Jilin University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION Accurate environment perception is a critical topic in autonomous driving and intelligent *** environmental perception methods mostly rely on on-board ***,limited by the installation height,thereare problems such as blind spots and unstable long-range perception in vehicle perceptual ***,with the rapid improvement of intelligent infrastructure,it has become possible to use roadside cameras for traffic environment *** from the increased height when compared with on-boardsensors,roadside cameras can obtain a larger perceptual field of view and realize long-range observation.

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LifeSaver: Predictive Load Limit Estimation for Transport Vehicles in Hilly Areas

LifeSaver: Predictive Load Limit Estimation for Transport Ve...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Chopra, Vaibhav Rao, Chanakya Soni, Moksh Mishra, Prashant Plaksha University Computer Science & Artificial Intelligence Mohali India Plaksha University Data Science Economics & Business Mohali India Plaksha University Robotics & Cyber-Physical Systems Mohali India

ISBN: (纸本)9798331529833

The transportation of essential goods in mountainous regions faces severe logistical challenges and frequent disruptions. To mitigate these difficulties, transport companies often overload trucks, which, though cost-saving, significantly heightens the risk of accidents and mechanical failures. This paper presents the development of a device that detects overloaded and insecurely fastened loads on trucks and commercial vehicles. Using advanced load sensors, the device offers real-time monitoring of cargo weight distribution, alerting drivers and authorities to unsafe conditions. The initial prototype utilised two basic load cells and an Arduino microcontroller. The second version was enhanced with four load cells and extended sensors. This version was tested by placing an electric golf cart onto the prototype. Various loads were then added to the cart in different orientations to assess whether the system could accurately detect improper or excessive load conditions. © 2025 IEEE.

关键词： Trucks

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Secrecy Performance Review for IoT-based Cognitive Farming System

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Recent Patents on Engineering 2024年第7期18卷 66-75页

作者： Chopra, Khyati Alam, M. Afshar Department of CSE Jamia Hamdard University New Dehli India University School of Automation and Robotics GGSIPU New Delhi110032 India Department of Computer Science and Engineering Jamia Hamdard University New Delhi110062 India

Smart farm management has complex challenges;therefore, there is a need to improve the overall adaptability, profitability, and environmental soundness of the farming systems. Agricultural production can be enhanced with the help of IoT-based cognitive tools. The rapid increase in IoT-based smart systems has raised critical concerns about efficiently utilizing the wireless spectrum. To drastically bring the improvement in the utilization of the wireless spectrum, cognitive radio (CR) has emerged as an effective solution. The inherent openness and broadcast nature of the wireless medium have given rise to many security issues in cooperative networks. Information-theoretic security or physical layer security (PLS) is evolved as an efficacious wireless prototype for reliable transmissions to avert the eavesdropper from expropriating transmittal data on the wireless connections. Security is the key factor in these networks as they are exposed and active in be-havior, thus, are susceptible to fraudulent activities. In this patent study, we investigated the performance of the CR system in terms of secrecy and also examined the system in terms of diversity receipt and selection of relay. The secrecy outcome of channel state information at the sender was also studied. The detrimental effects of fading in cooperative networks were also extensively discussed. Cooperative diversity was examined by deploying MRC and SC strategies at receptors. It has been deduced that CSI knowledge at the sender also has an eloquent concussion on the secrecy of the cognitive system. © 2024 Bentham science Publishers.

关键词： Cognitive radio

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