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arXiv 2025年

作者： Wu, Ruixin Li, Zihan Wang, Jin Xu, Xiangyu Yu, Huan Zheng, Zhi Huang, Kaixiang Lu, Guodong The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310058 China Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Zhejiang University Hangzhou310058 China Robotics Research Center of Yuyao City Ningbo315400 China State Key Laboratory of Robotics and Systems Department of Mechatronics Engineering Harbin Institute of Technology Harbin150001 China

Millimeter-wave (mmWave) radar has attracted significant attention in robotics and autonomous driving. However, despite the perception stability in harsh environments, the point cloud generated by mmWave radar is relatively sparse while containing significant noise, which limits its further development. Traditional mmWave radar enhancement approaches often struggle to leverage the effectiveness of diffusion models in super-resolution, largely due to the unnatural range-azimuth heatmap (RAH) or bird’s eye view (BEV) representation. To overcome this limitation, we propose a novel method that pioneers the application of fusing range images with image diffusion models, achieving accurate and dense mmWave radar point clouds that are similar to LiDAR. Benefitting from the projection that aligns with human observation, the range image representation of mmWave radar is close to natural images, allowing the knowledge from pre-trained image diffusion models to be effectively transferred, significantly improving the overall performance. Extensive evaluations on both public datasets and self-constructed datasets demonstrate that our approach provides substantial improvements, establishing a new state-of-the-art performance in generating truly three-dimensional LiDAR-like point clouds via mmWave radar. Copyright © 2025, The Authors. All rights reserved.

关键词： Radar imaging

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Feasibility Evaluation of Quadratic Programs for Constrained Control

arXiv

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arXiv 2025年

作者： Rousseas, Panagiotis Panagou, Dimitra Control Systems Laboratory School of Mechanical Engineering National Technical University of Athens Greece Department of Robotics Department of Aerospace Engineering University of Michigan MI United States

This paper presents a computationally-efficient method for evaluating the feasibility of Quadratic Programs (QPs) for online constrained control. Based on the duality principle, we first show that the feasibility of a QP can be determined by the solution of a properly-defined Linear Program (LP). Our analysis yields a LP that can be solved more efficiently compared to the original QP problem, and more importantly, is simpler in form and can be solved more efficiently compared to existing methods that assess feasibility via LPs. The computational efficiency of the proposed method compared to existing methods for feasibility evaluation is demonstrated in comparative case studies as well as a feasible-constraint selection problem, indicating its promise for online feasibility evaluation of optimization-based controllers. © 2025, CC BY.

关键词： Quadratic programming

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CrazyKhoreia, a robotic perception system for UAV path planning from digital images

TechRxiv

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TechRxiv 2023年

作者： Restrepo-García, Santiago Romero Cano, Victor Robotics and Autonomous Systems Laboratory Faculty of Engineering Universidad Autónoma de Occidente Cali Colombia

Micro air vehicles can be used for a wide range of applications, which include drone light shows for cultural or marketing purposes, or as a replacement of fireworks. However, widespread use of this technology is precluded by a usability gap between choreography design and deployment on the target drones. In order to achieve seamless deployment of micro air vehicle choreographies, computational interfaces that obtain drone trajectories from a more conventional media such as digital images are needed. In this paper, we propose CrazyKhoreia, a low-cost, computationally efficient approach to Unmanned Aerial Vehicles (in short, UAVs) choreography design, which consists of a robotic perception system that takes a digital image, applies boundary tracing to it and obtains a safely, traversable and apparently accurate waypoint matrix as a result. We validate our trajectory generation system through two modes of operation, light painting, where the UAV can either fly through all of the waypoints or multiple UAVs may be arranged to mimic the original image. The generated trajectories on Light Painting mode by physical robots are compared against the ground truth using a data association tool by the Computer Vision Group at TUM and achieved an average RMSE error of 0.4531 metres, while on Multi-Drone Formation the observed positions are estimated from photographs on actual formations, this way the system reached a mean RMSE error of 0.136 metres. © 2023, CC BY.

关键词： Micro air vehicle (MAV)

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GenDepth: Generalizing Monocular Depth Estimation for Arbitrary Camera Parameters via Ground Plane Embedding

arXiv

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arXiv 2023年

作者： Koledić, Karlo Petrović, Luka Petrović, Ivan Marković, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Zagreb Croatia

Learning-based monocular depth estimation leverages geometric priors present in the training data to enable metric depth perception from a single image, a traditionally ill-posed problem. However, these priors are often specific to a particular domain, leading to limited generalization performance on unseen data. Apart from the well studied environmental domain gap, monocular depth estimation is also sensitive to the domain gap induced by varying camera parameters, an aspect that is often overlooked in current state-of-the-art approaches. This issue is particularly evident in autonomous driving scenarios, where datasets are typically collected with a single vehicle-camera setup, leading to a bias in the training data due to a fixed perspective geometry. In this paper, we challenge this trend and introduce GenDepth, a novel model capable of performing metric depth estimation for arbitrary vehicle-camera setups. To address the lack of data with sufficiently diverse camera parameters, we first create a bespoke synthetic dataset collected with different vehicle-camera systems. Then, we design GenDepth to simultaneously optimize two objectives: (i) equivariance to the camera parameter variations on synthetic data, (ii) transferring the learned equivariance to real-world environmental features using a single real-world dataset with a fixed vehicle-camera system. To achieve this, we propose a novel embedding of camera parameters as the ground plane depth and present a novel architecture that integrates these embeddings with adversarial domain alignment. We validate GenDepth on several autonomous driving datasets, demonstrating its state-of-the-art generalization capability for different vehicle-camera systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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Magnetic Flux Servoing for Precise Localization Based on Gradient Tensor Contractions

Magnetic Flux Servoing for Precise Localization Based on Gra...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Dean Martinović Matko Orsag Faculty of Electrical Engineering and Computing Laboratory for Robotics and Intelligent Control Systems University of Zagreb Zagreb Croatia

This paper focuses on bridge inspections performed by intelligent unmanned aerial vehicles (UAV). For this, small data loggers are placed by the UAVs at the bridge, which have to be removed later. In our previous work we presented the magnetic localization method SRIOD, which not only detects the center of the data logger but also its orientation. In this work we proof SRIOD in a real application and let a robotic arm locate a magnetic target. The hardware involved is presented including the control strategy for the robot. The motions of the robot and the magnetic target are captured with 12 Optitrack cameras in order to validate the localization quality.

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Domain-Adaptive Power Profiling Analysis Strategy for the Metaverse

Domain-Adaptive Power Profiling Analysis Strategy for the Me...

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作者： Li, Xiang Yang, Ning Liu, Weifeng Chen, Aidong Zhang, Yanlong Wang, Shuo Zhou, Jing Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing China Institute of Semiconductors Chinese Academy of Sciences Beijing China College of Robotics Beijing Union University Beijing China Research Center for Multi-Intelligent Systems Beijing Union University Beijing China Information System Laboratory Beijing Microelectronics Technology Institution Beijing China

In the surge of the digital era, the metaverse, as a groundbreaking concept, has become a focal point in the technology sector. It is reshaping human work and life patterns, carving out a new realm of virtual and real interaction. However, the rapid development of the metaverse brings along novel challenges in security and privacy. In this multifaceted and complex technological environment, data protection is of paramount importance. The innovative capabilities of high-end devices and functions in the metaverse, owing to advanced integrated circuit technology, face unique threats from side-channel analysis (SCA), potentially leading to breaches in user privacy. Addressing the issue of domain differences caused by different hardware devices, which impact the generalizability of the analysis model and the accuracy of analysis, this paper proposes a strategy of portability power profiling analysis (PPPA). Combining domain adaptation and deep learning techniques, it models and calibrates the domain differences between the profiling and target devices, enhancing the model's adaptability in different device environments. Experiments show that our method can recover the correct key with as few as 389 power traces, effectively recovering keys across different devices. This paper underscores the effectiveness of cross-device SCA, focusing on the adaptability and robustness of analysis models in different hardware environments, thereby enhancing the security of user data privacy in the metaverse environment. © 2024 John Wiley & Sons Ltd.

关键词： Data privacy

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MOFT: Monocular odometry based on deep depth and careful feature selection and tracking

MOFT: Monocular odometry based on deep depth and careful fea...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Karlo Koledić Igor Cvišić Ivan Marković Ivan Petrović University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Zagreb Croatia

Autonomous localization in unknown environments is a fundamental problem in many emerging fields and the monocular visual approach offers many advantages, due to being a rich source of information and avoiding comparatively more complicated setups and multisensor calibration. Deep learning opened new venues for monocular odometry yielding not only end-to-end approaches but also hybrid methods combining the well studied geometry with specific deep components. In this paper we propose a monocular odometry that leverages deep depth within a feature based geometrical framework yielding a lightweight frame-to-frame approach with metrically scaled trajectories and state-of-the-art accuracy. The front-end is based on a multihypothesis matcher with perspective correction coupled with deep depth predictions that enables careful feature selection and tracking; especially of ground plane features that are suitable for translation estimation. The back-end is based on point-to-epipolar line minimization for rotation and unit translation estimation, followed by deep depth aided reprojection error minimization for metrically correct translation estimation. Furthermore, we also present a domain shift adaptation approach that allows for generalization over different camera intrinsic and extrinsic setups. The proposed approach is evaluated on the KITTI and KITTI-360 datasets, showing competitive results and in most cases outperforming other state-of-the-art stereo and monocular methods.

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Development of a Tomato Harvesting Robot: Peduncle Recognition and Approaching 3

Development of a Tomato Harvesting Robot: Peduncle Recogniti...

引用

3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2021

作者： Kounalakis, Nikolaos Kalykakis, Emmanouil Pettas, Manos Makris, Alexandros Kavoussanos, Manolis M. Sfakiotakis, Michael Fasoulas, John Hellenic Mediterranean University Control Systems and Robotics Laboratory School of Engineering Heraklion71410 Greece

ISBN: (纸本)9781665440585

Over the last decade there is a growing effort to incorporate robotic technologies in agriculture to reduce costs and increase yields and quality. In this paper we present the methods and tools developed for automated tomato harvesting by a greenhouse robot. The system is comprised of a 6-dof manipulator arm, a custom gripping/cutting end-effector, and a depth camera with dedicated vision processor. Deep learning algorithms are employed to locate ripe tomatoes and their peduncles, exploiting depth information from the acquired images to guide the manipulator arm towards the identified cutting points. Through extensive experiments in a realistic setting, the overall success rate of the detection and approach procedure was found to be 65%, with 92.6% accuracy of the vision processing in locating the correct cutting point. Suggestions for further improvement of the system's performance are also provided. © 2021 IEEE.

关键词： Deep learning

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Multistable Soft Actuator for Physical Human-robot Interaction

Multistable Soft Actuator for Physical Human-robot Interacti...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Juncai Long Jituo Li Xiaojie Diao Chengdi Zhou Guodong Lu Yixiong Feng Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou China Zhejiang University Robotics Institute China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Collaboration with robots through physical contact offers a more intuitive, natural, and engaging operational experience, showcasing vast potential in the field of human-robot interaction. However, current physical interaction devices, such as collaborative robots and haptic feedback mechanisms, are limited by their singular modes of motion and feedback, hindering enhancements in interaction experiences. Herein, we present a multistable soft actuator capable of driving multimodal shape changes and passively conforming to user touch. This actuator can memorize and maintains any deformation with zero power consumption. Its structural mechanical properties can be dynamically adjusted to produce rich haptic feedback for the user, including changes in shape, elasticity, stiffness, and even sensations of rupture and weightlessness. Structurally, the mechanism consists of a network of pneumatic bistable units in series and parallel configurations, which can switch states under air pressure or external force, achieving extension, contraction, and omnidirectional bending. The input of air pressure can either impede or assist deformation, altering structural stiffness and resulting in varied loading curves. With its high safety in physical interactions, robust operability, and rich mechanical tactile feedback, the multistable soft actuator promises new design directions for physical human-robot interaction devices.

关键词： Training Actuators Shape Deformation System performance Force Human-robot interaction Tactile sensors Soft robotics Haptic interfaces

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Compensating for the Effect of Absence of Joint Movements in EMG-based Continuous Movement Decoding with a Deep Learning Approach 30

Compensating for the Effect of Absence of Joint Movements in...

引用

30th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2024

作者： Pan, Lizhi Ding, Zhongyi Li, Chengjun Zhao, Haifeng Mu, Ruinan Li, Jianmin Li, Jinhua Tianjin University Key Laboratory of Mechanism Theory and Equipment Design Ministry of Education School of Mechanical Engineering Tianjin300350 China Tianjin University Institute of Medical Robotics and Intelligent Systems Tianjin300350 China Chinese Academy of Sciences Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Beijing100094 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9798350391916

In recent decades, human-machine interfaces (HMIs) based on electromyography (EMG) signals have become research hotspots in the fields of rehabilitation engineering, aerospace, and human factors engineering. However, the absence of joint movements can influence the performance of HMIs in the continuous movement decoding. Therefore, it has become an urgent problem to improve the quality of EMG signals with the absence of joint movements. In this study, a deep learning (DL) method is proposed based on the convolutional neural network (CNN) to compensate for the absence of joint movements in EMG-based continuous movement decoding. Ten able-bodied subjects were recruited to complete a series of movements with the metacarpophalangeal (MCP) and wrist joints unconstrained (MWJU) and constrained (MWJC). Half of the EMG signals for the constrained joint movements were compensated with the proposed CNN-based method (CNN-EMG), which were then compared with the other half of the constrained EMG signals (MWJC-EMG) and the signals without joint constraints (MWJU-EMG). The Pearson's correlation coefficient (r) and the normalized root mean square error (NRMSE) were employed as the indexes to quantify the performance of continuous movement decoding with three groups of EMG signals. Compared with the performance of MWJC-EMG, the average r values for wrist and MCP flexion/extension of CNN-EMG signals increased from 0.8 to 0.84 and from 0.49 to 0.66, respectively. Compared with the performance of MWJC-EMG signals, the average NRMSE values for wrist and MCP flexion/extension of CNN-EMG signals decreased from 0.21 to 0.19 and 0.31 to 0.26, respectively. The analysis results demonstrated that the proposed DL method can significantly improve the continuous movement decoding performance of HMIs through compensating the effect of absence of joint movements. © 2024 IEEE.

关键词： Convolutional neural networks

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