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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Robinroy Peter Lavanya Ratnabala Demetros Aschu Aleksey Fedoseev Dzmitry Tsetserukou Intelligent Space Robotics Laboratory CDE Skolkovo Institute of Science and Technology Moscow Russia

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

Autonomous drone landing on dynamic platforms presents formidable challenges due to unpredictable velocities and aerodynamic disturbances. This study introduces an advanced Deep Reinforcement Learning (DRL) agent, ***, designed to navigate and land on 3D moving platforms in the presence of aerodynamic disturbance and sudden velocity changes, thereby enhancing drone autonomy and safety. *** is trained in the gym-pybullet-drone simulation, an environment that mirrors real-world complexities, including wind disturbance, to ensure the agent's robustness and adaptability. The agent's capabilities were empirically validated with Crazyflie 2.1 drones across various test scenarios, encompassing both simulated environments and real-world conditions. The experimental results showcased ***'s high-precision landing and its ability to adapt to moving platforms, even under wind-induced disturbances. Furthermore, our system performance was bench-marked against a baseline Proportional-integral-derivative (PID) controller augmented with an Extended Kalman Filter (EKF), illustrating significant improvements in landing precision and error recovery. This research not only advances drone landing technologies, essential for inspection and emergency applications, but also highlights the potential of DRL in addressing intricate aerodynamic challenges.

关键词： Three-dimensional displays Navigation System performance Aerodynamics Robustness Safety PD control

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Musinger: Communication of Music over a Distance with Wearable Haptic Display and Touch Sensitive Surface

Musinger: Communication of Music over a Distance with Wearab...

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IEEE International Conference on Robotics and Biomimetics

作者： Miguel Altamirano Cabrera Muhammad Haris Khan Ali Alabbas Luis Moreno Issatay Tokmurziyev Dzmitry Tsetserukou Intelligent Space Robotics Laboratory Skolkovo Institute of Science and Technology (Skoltech) Moscow Russia

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

This study explores the integration of auditory and tactile experiences in musical haptics, focusing on enhancing sensory dimensions of music through touch. Addressing the gap in translating auditory signals to meaningful tactile feedback, our research introduces a novel method involving a touch-sensitive recorder and a wearable haptic display that captures musical interactions via Force Sensitive Resistor (FSR) and converts these into tactile sensations. Previous studies have shown the potential of haptic feedback to enhance musical expressivity, yet challenges remain in conveying complex musical nuances. Our method aims to expand music accessibility for individuals with hearing impairments and deepen digital musical interactions. Experimental results reveal high user melodies recognition through tactile feedback (93% without noise cancellation and 98% without noise cancelation), demonstrating effective transmission and perception of musical information. The findings highlight the potential of haptic technology to bridge sensory gaps, offering significant implications for music therapy, education, and remote musical collaboration, advancing the field of musical haptics and multi-sensory technology applications.

关键词： Resistors Bridges Force Music Tactile sensors Medical treatment Focusing Collaboration Noise cancellation Haptic interfaces

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FlightAR: AR Flight Assistance Interface with Multiple Video Streams and Object Detection Aimed at Immersive Drone control

FlightAR: AR Flight Assistance Interface with Multiple Video...

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IEEE International Conference on Robotics and Biomimetics

作者： Oleg Sautenkov Selamawit Asfaw Yasheerah Yaqoot Muhammad Ahsan Mustafa Aleksey Fedoseev Daria Trinitatova Dzmitry Tsetserukou Intelligent Space Robotics Laboratory Center for Digital Engineering Skolkovo Institute of Science and Technology

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

The swift advancement of unmanned aerial vehicle (UAV) technologies necessitates new standards for developing human-drone interaction (HDI) interfaces. Most interfaces for HDI, especially first-person view (FPV) goggles, limit the operator's ability to obtain information from the environment. This paper presents a novel interface, FlightAR, that integrates augmented reality (AR) overlays of UAV first-person view (FPV) and bottom camera feeds with head-mounted display (HMD) to enhance the pilot's situational awareness. Using FlightAR, the system provides pilots not only with a video stream from several UAV cameras simultaneously, but also the ability to observe their surroundings in real time. User evaluation with NASA-TLX and UEQ surveys showed low physical demand $(\mu=1.8,SD=0.8)$ and good performance $(\mu=3.4,\ SD=0.8)$ , proving better user assessments in comparison with baseline FPV goggles. Participants also rated the system highly for stimulation $(\mu=2.35,\ SD=0.9)$ , novelty $(\mu=2.1,\ SD=0.9)$ and attractiveness $(\mu=1.97,\ SD=\ 1)$ , indicating positive user experiences. These results demonstrate the potential of the system to improve UAV piloting experience through enhanced situational awareness and intuitive control. The code is available here: https://***/SautenichlFlightAR

关键词： Surveys Resists Object detection Streaming media Autonomous aerial vehicles Cameras Real-time systems Eye protection Standards Robots

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AirNeRF: 3D Reconstruction of Human with Drone and NeRF for Future Communication Systems

AirNeRF: 3D Reconstruction of Human with Drone and NeRF for ...

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IEEE International Conference on Automation science and Engineering (CASE)

作者： Alexey Kotcov Maria Dronova Vladislav Cheremnykh Sausar Karaf Dzmitry Tsetserukou Intelligent Space Robotics Laboratory CDE Skolkovo Institute of Science and Technology Moscow Russia

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In the rapidly evolving landscape of digital content creation, the demand for fast, convenient, and autonomous methods of crafting detailed 3D reconstructions of humans has grown significantly. Addressing this pressing need, our AirNeRF system presents an innovative pathway to the creation of a realistic 3D human avatar. Our approach leverages Neural Radiance Fields (NeRF) with an automated drone-based video capturing method. The acquired data provides a swift and precise way to create high-quality human body reconstructions following several stages of our system. The rigged mesh derived from our system proves to be an excellent foundation for freeview synthesis of dynamic humans, particularly well-suited for the immersive experiences within gaming and virtual reality.

关键词： Three-dimensional displays Computer aided software engineering Automation Communication systems Avatars Pressing Neural radiance field Drones

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Non-fragile Joint State and Fault Estimation for Complex Networks with Missing Measurements and Switching Topology 41

Non-fragile Joint State and Fault Estimation for Complex Net...

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第41届中国控制会议

作者： Pingping Gao Jun Hu Chaoqing Jia Zhihui Wu Department of Mathematics Harbin University of Science and Technology School of Automation Harbin University of Science and Technology Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,a non-fragile joint state and fault estimation problem is investigated for time-varying complex networks with switching topology and randomly occurring nonlinearities under the missing *** phenomena of the switching topology,randomly occurring nonlinearities and missing measurements are described by three mutually independent Bernoulli random variables,where the uncertain occurrence probabilities are *** addition,the parameter perturbations of the estimator gain matrix are characterized by a set of zero-mean multiplicative noises.A novel time-varying estimation method is designed and an upper bound of estimation error covariance matrix is obtained by solving two recursive matrix ***,the desired estimator gain matrix is parameterized by minimizing the trace of the obtained upper ***,a sufficient condition is given to ensure the boundedness of the upper bound matrix by using the mathematical induction ***,a simulation study is given to verify the feasibility of the proposed estimation scheme.

关键词： Time-varying complex networks switching topology randomly occurring nonlinearities missing measurements uncertain occurrence probabilities joint estimation

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FlyNeRF: NeRF-Based Aerial Mapping for High-Quality 3D Scene Reconstruction

FlyNeRF: NeRF-Based Aerial Mapping for High-Quality 3D Scene...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Maria Dronova Vladislav Cheremnykh Alexey Kotcov Aleksey Fedoseev Dzmitry Tsetserukou Intelligent Space Robotics Laboratory CDE Skolkovo Institute of Science and Technology Moscow Russia

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

Current methods for 3D reconstruction and environmental mapping frequently face challenges in achieving high precision, highlighting the need for practical and effective solutions. In response to this issue, our study introduces FlyNeRF, a system integrating Neural Radiance Fields (NeRF) with drone-based data acquisition for high-quality 3D reconstruction. Utilizing unmanned aerial vehicles (UAV) for capturing images and corresponding spatial coordinates, the obtained data is subsequently used for the initial NeRF-based 3D reconstruction of the environment. Further evaluation of the reconstruction render quality is accomplished by the image evaluation neural network developed within the scope of our system. Depending on the results of the image evaluation module, our algorithm determines the position for additional image capture, thereby improving the reconstruction quality. The neural network introduced for render quality assessment demonstrates an accuracy of 97%. Furthermore, our adaptive methodology enhances the overall reconstruction quality, resulting in an average improvement of 2.5 dB in Peak Signal-to-Noise Ratio (PSNR) for the 10% quantile. The FlyNeRF demonstrates promising results, offering advancements in such fields as environmental monitoring, surveillance, and reconstruction of digital twins, where high-fidelity 3D reconstructions are crucial.

关键词： Three-dimensional displays PSNR Surveillance Neural networks Image capture Autonomous aerial vehicles Quality assessment

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Automatic skin lesion classification using a novel densely connected convolutional network integrated with an attention module

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 8943-8956页

作者： Shan, Pufang Chen, Jialei Fu, Chong Cao, Lin Tie, Ming Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China Science and Technology on Space Physics Laboratory Beijing100076 China School of Computer Science The University of Auckland Auckland New Zealand

Automated skin lesion classification in dermoscopy images remains challenging due to the existence of artefacts and intrinsic cutaneous features, diversity of lesion morphology, insufficiency of training data, and class imbalance problem. To address these challenges, we propose a new densely connected convolutional network termed AttDenseNet-121, which is obtained by integrating the convolutional block attention module (CBAM) into DenseNet-121. CBAM is a simple yet efficient attention module, which is further improved by adding a novel pooling layer (multiscale-pooling) to effectively enhance its attention capability. The optimized CBAM strengthens the representation power of DenseNet-121 by emphasizing meaningful features and suppressing unnecessary ones, thus significantly enhancing the classification performance. Besides, to handle the imbalanced dataset, we employ an improved focal loss function rather than the traditional cross-entropy loss function to train AttDenseNet-121. The improved focal loss is calculated from a different perspective compared with the original focal loss. It makes the distribution of positive samples and negative samples in each batch the same as that in the original data, successfully mitigating the negative influence of class imbalance on this multi-class classification task. We conduct extensive experiments on the public benchmark dataset (HAM10000) and the results indicate that our proposed method achieves superior performance compared with that of the baselines and state-of-the-art algorithms. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolution

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OmniRace: 6D Hand Pose Estimation for Intuitive Guidance of Racing Drone

OmniRace: 6D Hand Pose Estimation for Intuitive Guidance of ...

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IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Valerii Serpiva Aleksey Fedoseev Sausar Karaf Ali Alridha Abdulkarim Dzmitry Tsetserukou Intelligent Space Robotics Laboratory Skolkovo Institute of Science and Technology Moscow Moscow Russia

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper presents the OmniRace approach to controlling a racing drone with 6-degree of freedom (DoF) hand pose estimation and gesture recognition. To our knowledge, this is the first technology enabling low-level control of high-speed drones through gestures. OmniRace employs a gesture interface based on computer vision and a deep neural network to estimate 6-DoF hand pose. The advanced machine learning algorithm robustly interprets human gestures, allowing users to control drone motion intuitively. Real-time control tests validate the system’s effectiveness and its potential to revolutionize drone racing and other applications. Experimental results conducted in simulation environment revealed that OmniRace allows the users to complite the UAV race track significantly (by 25.1%) faster and to decrease the length of the test drone path (from 102.9 to 83.7 m). Users preferred the gesture interface for attractiveness (1.57 UEQ score), hedonic quality (1.56 UEQ score), and lower perceived temporal demand (32.0 score in NASA-TLX), while noting the high efficiency (0.75 UEQ score) and low physical demand (19.0 score in NASA-TLX) of the baseline remote controller. The deep neural network attains an average accuracy of 99.75% when applied to both normalized datasets and raw datasets. OmniRace can potentially change the way humans interact with and navigate racing drones in dynamic and complex environments. The source code is available at https://***/SerValera/***.

关键词： Hands Machine learning algorithms Tracking Navigation Source coding Pose estimation Artificial neural networks Real-time systems intelligent robots Drones

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Adaptive fuzzy asymptotic control design for MIMO nonlinear systems with state constraints

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Journal of control and Decision 2023年第4期10卷 610-623页

作者： Yongchao Liu Bowen Zeng Qidan Zhu Lipeng Wang Institute of Complexity Science School of AutomationQingdao UniversityQingdaoPeople’s Republic of China Shandong Key Laboratory of Industrial Control Technology QingdaoPeople’s Republic of China College of Intelligent Systems Science and Engineering Harbin Engineering UniversityHarbinPeople’s Republic of China

This paper addresses the asymptotic control problem of uncertain multi-input and multi-output(MIMO)nonlinear *** considered MIMO systems contain unknown virtual control coefficients(UVCCs)and state *** Lyapunov function by associating with the lower bounds of UVCCs is presented to counteract the adverse effect deriving from *** state constraints are ensured by utilising the barrier Lyapunov ***,the asymptotic tracking controller is recursively constructed by combining the backstepping technique with fuzzy logic *** remarkable character of the designed controller is that the asymptotic tracking performance can be achieved by introducing some smooth functions into adaptive backstepping *** contrast to the existing results,the conditions on the UVCCs are ***,the new control design is illustrated by a practical example.

关键词： Asymptotic tracking control state constraints bound estimation method nonlinear systems

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Instance by Instance: An Iterative Framework for Multi-Instance 3D Registration

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IEEE/CAA Journal of Automatica Sinica 2025年第6期12卷 1117-1128页

作者： Yang, Jiaqi Cao, Xinyue Zhang, Xiyu Cheng, Yuxin Qi, Zhaoshuai Quan, Siwen School of Computer Science Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an710072 China School of Electronic and Control Engineering Chang'an University Xi'an710064 China Shenzhen University MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area Shenzhen518060 China

Multi-instance registration is a challenging problem in computer vision and robotics, where multiple instances of an object need to be registered in a standard coordinate system. Pioneers followed a non-extensible one-shot framework, which prioritizes the registration of simple and isolated instances, often struggling to accurately register challenging or occluded instances. To address these challenges, we propose the first iterative framework for multi-instance 3D registration (MI-3DReg) in this work, termed instance-by-instance (IBI). It successively registers instances while systematically reducing outliers, starting from the easiest and progressing to more challenging ones. This enhances the likelihood of effectively registering instances that may have been initially overlooked, allowing for successful registration in subsequent iterations. Under the IBI framework, we further propose a sparse-to-dense correspondence-based multi-instance registration method (IBI-S2DC) to enhance the robustness of MI-3DReg. Experiments on both synthetic and real datasets have demonstrated the effectiveness of IBI and suggested the new state-of-the-art performance with IBI-S2DC, e.g., our mean registration F1 score is 12.02%/12.35% higher than the existing state-of-the-art on the synthetic/real datasets. © 2014 Chinese Association of Automation.

关键词： 3D modeling

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