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NBV-HRR: Next Best View Planning Network for Highly Reflective Region Restoration in Robotic 3-D Scanning

IEEE/ASME Transactions on Mechatronics 2024年

作者： Ouyang, Jun Liu, Daohui Jia, Peng Liu, Xinyu Liu, Xingjian Sun, Yu Dalian University of Technology Institute of Robotics and Intelligent Systems the State Key Laboratory of High-Performance Precision Manufacturing Dalian116024 China University of Toronto Department of Mechanical and Industrial Engineering TorontoONM5S 3G8 Canada

Three-dimensional scanning of highly reflective surfaces is a challenge faced in industrial metrology. The high dynamic range (HDR) technique offers a solution by fusing images taken at various exposures. However, its effectiveness is limited in regions where reflectivity exceeds the camera's dynamic range. To address this issue, this article introduces a next best view (NBV) planning network for the restoration of highly reflective regions, named NBV-HRR. Unlike traditional NBV networks focused on complete shape reconstruction utilizing global features, NBV-HRR targets the restoration of localized regions lost due to reflection, utilizing both global and local features. To accurately evaluate each view, a cross-attention-based module is devised to fuse features of varying dimensions. For network training, a dataset containing 70 400 training samples was collected from over 1000 highly reflective industrial parts in both simulation and real scenes. Experimental results demonstrated that the proposed NBV-HRR network achieved an average restoration rate of 99.3% within three NBV predictions, significantly outperforming the HDR technique (83.7%). © 2024 IEEE.

关键词： Restoration

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A Bipedal Wheel-Legged Robot with High-frequency Force Control by Qausi-Direct Drive: Design and Experiments

A Bipedal Wheel-Legged Robot with High-frequency Force Contr...

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IEEE International Conference on robotics and Biomimetics

作者： Yuntian Zhao Shiyuan Lin Zheng Zhu Zhenzhong Jia Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology (SUSTech) Shenzhen China

ISBN: (纸本)9781665481106

Under-actuated systems like bipedal wheel-legged robots have dynamics coupled with force control, which cannot be separated or integrated out. Joint force control is necessary for these robots to realize model based controllers. In this paper, we design a bipedal wheel-legged robot prototype with proprioceptive quasi-direct drive (QDD) actuators to realize high-frequency force control and good back-drivability to deal with unexpected impacts. We compare the control bandwidth, back-drivability (impact mitigation capability) of three commonly used force-control actuation paradigms including geareddown motor with integrated torque sensor, QDD, and SEA (series elastic actuation) in details. Detailed actuator calibration reveals good current-torque linearity, thereby simplifying the control problem. We also use EtherCAT-based communication network and real-time main controller to ensure high control frequency. We derive the robot dynamics model, establish the simulation model, and design a LQR controller, in order to demonstrate our proposed design. Moreover, in the real-world experiments, we use a velocity-based balance controller together with position joint controller to show preliminary mobility, including balancing, rotating, and forward motion.

关键词： Actuators Torque Dynamics Prototypes Propioception Linearity Real-time systems

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Mixed Motivation Driven Social Multi-Agent Reinforcement Learning for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2025年第6期12卷 1272-1282页

作者： Long Chen Peng Deng Lingxi Li Xuemin Hu State Key Laboratory of Multimodal Artificial Intelligence Systems and the State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing WAYTOUS Inc. Beijing Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China School of Computer Science and Information Engineering Hubei University Wuhan China Purdue School of Engineering and Technology Indiana University-Purdue University Indianapolis Indianapolis IN USA School of Artificial Intelligence Hubei University Wuhan Key Laboratory of Intelligent Sensing System and Security (Hubei University) Ministry of Education Wuhan China

Despite great achievement has been made in autonomous driving technologies, autonomous vehicles (AVs) still exhibit limitations in intelligence and lack social coordination, which is primarily attributed to their reliance on single-agent technologies, neglecting inter-AV interactions. Current research on multi-agent autonomous driving (MAAD) predominantly focuses on either distributed individual learning or centralized cooperative learning, ignoring the mixed-motive nature of MAAD systems, where each agent is not only self-interested in reaching its own destination but also needs to coordinate with other traffic participants to enhance efficiency and safety. Inspired by the mixed motivation of human driving behavior and their learning process, we propose a novel mixed motivation driven social multi-agent reinforcement learning method for autonomous driving. In our method, a multi-agent reinforcement learning (MARL) algorithm, called Social Learning Policy Optimization (SoLPO), which takes advantage of both the individual and social learning paradigms, is proposed to empower agents to rapidly acquire self-interested policies and effectively learn socially coordinated behavior. Based on the proposed SoLPO, we further develop a mixed-motive MARL method for autonomous driving combined with a social reward integration module that can model the mixed-motive nature of MAAD systems by integrating individual and neighbor rewards into a social learning objective for improved learning speed and effectiveness. Experiments conducted on the MetaDrive simulator show that our proposed method outperforms existing state-of-the-art MARL approaches in metrics including the success rate, safety, and efficiency. More-over, the AVs trained by our method form coordinated social norms and exhibit human-like driving behavior, demonstrating a high degree of social coordination.

关键词： Measurement Reinforcement learning Safety Driver behavior Autonomous vehicles Optimization

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Techno-Economic Assessment of a Large-Scale Electrolysis Plant in Hydrogen and Ancillary Service Markets

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IFAC-PapersOnLine 2024年第13期58卷 140-145页

作者： Yassine Ennassiri Giulio Ferro Michela Robba Gilney Damm Edemar O. Prado DIBRIS—Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova 16145 Italy IMSE Laboratory Gustave Eiffel University Marne-la-Vallée France Power Electronics and Control Research Group GEPOC Federal University of Santa Maria SantaMaria 97105-900 RS Brazil

Hydrogen electrolysis systems have seen considerable interest from energy communities as a potential solution for shifting from fossil-based energy resources to clean energy alternatives. The system consists of transforming electricity into hydrogen via water electrolysis. As it is expected to have a large-scale connection with the grid to compensate the fluctuation of renewables, it is important to understand the system’s technical and economic feasibility. Therefore, this work aims to provide a high-level techno-economic assessment for the participation of a large-scale electrolysis plant in hydrogen and grid ancillary service markets. The plant generates revenue from two sources: hydrogen and frequency regulation. Based on market prices of electricity and hydrogen, a linear programming (LP) optimization has shown that a 50MW electrolysis plant can generate a 33% increase in profit if the plant includes the grid support market in the business model. Additionally, simulations under unstable grid frequency have shown high reactivity of the system to maintain grid stability.

关键词： optimization hydrogen production frequency regulation linear programming

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A Distributed Fixed-Time Neurodynamic Algorithm and Its Application in Multi-Autonomous Underwater Vehicle Collaborative Escorting

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IEEE Transactions on Network Science and engineering 2025年

作者： Zhang, Peng He, Xing Yu, Junzhi Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China Peking University Nanchang Innovation Institute Nanchang330224 China

In this paper, a distributed fixed-time neurodynamic algorithm (DFxTNA) is designed for solving distributed optimization problem with time-varying (TV) objective function and constraints. The DFxTNA consists of consensus part based on sliding-mode control technique and optimization part based on Hessian matrix and penalty function. In contrast to existing distributed TV algorithms, DFxTNA based on fixed-time sliding mode control can effectively improve the convergence performance of the algorithm and has better stability with the addition of TV disturbance. In addition, the consistency and fixed-time convergence of DFxTNA are proved by Lyapunov theory and fixed-time stability, respectively. The effectiveness of DFxTNA is verified by numerical experiments. Finally, a distributed TV optimization problem model is established for a multi-autonomous underwater vehicle (AUV) cooperative escort scenario, and DFxTNA is applied to solve the problem. Simulation results confirm the effectiveness and stability of DFxTNA in target tracking, formation control, and dynamic obstacle avoidance. © 2013 IEEE.

关键词： Lyapunov functions

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Lidar-Inertial and Downward Vision Based Mobile Robot SLAM in Digital Workshops

Lidar-Inertial and Downward Vision Based Mobile Robot SLAM i...

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Data Driven Control and Learning systems (DDCLS)

作者： Yanchao Zhang Hao Geng Bin Liu Yinlong Zhang Xudong Yuan Sichao Zhang School of Information Science and Engineering Shenyang University of Technology Shenyang China Guangzhou Institute of Industrial Intelligence Guangzhou China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In order to solve the problems of long-term cumulative error and the absence of absolute pose in the existing Lidar-Inertial Measurement Unit (IMU) fusion methods for Automated Guided Vehicle (AGV) Simultaneous Localization and Mapping (SLAM), we propose a new SLAM method for mobile robot, integrating Lidar, IMU, and a downward vision camera. Firstly, we tightly couple Lidar-IMU and downward vision camera using a multimodal fusion framework. Downward visual measurements are integrated as QR factors into the factor graph, providing absolute pose information to correct cumulative error in the Lidar-IMU system and enable the joint pose estimation of the AGV. Secondly, an enhanced Iterative Closest Point (ICP) point cloud matching algorithm has been developed to correct the incorrect pose. This approach achieves Scan-to-Map alignment by employing a coarse-to-fine matching strategy, thereby enhancing the accuracy of point cloud alignment. Finally, the extended Kalman filter is used to optimize the Lidar pose drift, as well as the Lidar mapping effectively by using the Lidar-IMU as the measurements and the downward visual measurements as the updates. The algorithm has been extensively evaluated on the developed AGV experimental platform. The experimental results show that the position error is less than 0.02 meters and the orientation error is less than 1°, which solves the problems of long-term error accumulation and the lack of absolute pose information for AGV navigation.

关键词： Point cloud compression Visualization Laser radar Simultaneous localization and mapping Conferences Robot vision systems Measurement uncertainty

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Intention-Aware Planner for Robust and Safe Aerial Tracking

Intention-Aware Planner for Robust and Safe Aerial Tracking

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

作者： Qiuyu Ren Huan Yu Jiajun Dai Zhi Zheng Jun Meng Li Xu Chao Xu Fei Gao Yanjun Cao The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China The Robotics Institute of Zhejiang University Yuyao China Huzhou Institute Zhejiang University Huzhou China Huzhou Laboratory of Autonomous Intelligent Systems Huzhou China College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Autonomous target tracking with quadrotors has wide applications in many scenarios, such as cinematographic follow-up shooting or suspect chasing. Target motion prediction is necessary when designing the tracking planner. However, the widely used constant velocity or constant rotation assumption can not fully capture the dynamics of the target. The tracker may fail when the target happens to move aggressively, such as sudden turn or deceleration. In this paper, we propose an intention-aware planner by additionally considering the intention of the target to enhance safety and robustness in aerial tracking applications. Firstly, a designated intention prediction method is proposed, which combines a user-defined potential assessment function and a state observation function. A reachable region is generated to speci cally evaluate the turning intentions. Then we design an intention-driven hybrid A* method to predict the future possible positions for the target. Finally, an intention-aware optimization approach is designed to generate a spatial-temporal optimal trajectory, allowing the tracker to perceive unexpected situations from the target. Benchmark comparisons and real-world experiments are conducted to validate the performance of our method.

关键词： Target tracking Semantics Prediction methods Turning Prediction algorithms Robustness Safety Planning Trajectory optimization Quadrotors

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SAF: Local Shape-aware Face-based Garment Collision Handling via Neural SDFs

SAF: Local Shape-aware Face-based Garment Collision Handling...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Minzhe Tang Ruisheng Yuan Dongliang Kou Mingyang Sun Lihua Zhang Academy for Engineering and Technology Fudan University Shanghai China Institute of Metaverse & Intelligent Medicine Fudan University Shanghai China Jilin Provincial Key Laboratory of Intelligence Science and Engineering Changchun China Engineering Research Center of AI and Robotics Ministry of Education Shanghai China AI and Unmanned Systems Engineering Research Center of Jilin Province Changchun China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Learning-based garment prediction presents an appealing alternative to physics-based methods owing to its high efficiency. However, the predicted garments can exhibit noticeable penetrations into the body. Many collision-handling methods operate in the point domain, which has an inherent limitation in addressing the penetration issues with the mesh faces. In light of this, we propose a local Shape-Aware, Face-based collision handling approach (SAF) that can be applied to garment prediction networks to achieve real-time collision handling. Considering the nature of garments, we design a compact formulation to model the continuity of the garment surface, and utilize neural Signed Distance Fields (SDFs) to accomplish penetration resolution. Recognizing the significant impact of the body’s local shape on collision handling, we further propose using the angles between SDF gradients to characterize the sharpness of the body. Our approach can compensate for the inaccuracy of neural SDFs and preserve local smoothness and details. Extensive experiments demonstrate the outstanding performance and generalizability of our method.

关键词： Shape Face recognition Clothing Signal processing Real-time systems Acoustics Character recognition Speech processing Faces

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An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

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Computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

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A Stair-Climbing Robot with Star-wheel Configuration

A Stair-Climbing Robot with Star-wheel Configuration

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IEEE International Conference on robotics and Biomimetics

作者： Tongxin Cui Wenhui Wang Zheng Zhu Jing Wu Zhenzhong Jia Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology (SUSTech) Shenzhen China

ISBN: (纸本)9781665481106

This paper proposes a four-wheel mobile robot that uses passive star-wheel configuration to climb stairs. Without adding any control complexity, the robot can climb a standard indoor staircase of 15 x 28cm at a speed of 0.7 $s$ / step. For stairs of other sizes, it also has a certain adaptability, avoiding the problem of slipping when using active star-wheel to climb stairs. This paper analyzes the obstacle-surmounting conditions of the four-star-wheel robot during stair climbing, and uses statics to calculate the driving torque. We use simulation to validate the torque consumption study, and prove the stability of the robot's central trajectory during stair climbing. These results provide a basis to quantify the robot's stair-climbing capability under certain load. We build an robot prototype platform and conduct physical experimentation to validate the robot performance.

关键词： Torque Prototypes Stairs Stability analysis Trajectory Complexity theory Mobile robots

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