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arXiv 2025年

作者： Hou, Xiaolu Li, Mingcheng Yang, Dingkang Chen, Jiawei Qian, Ziyun Zhao, Xiao Jiang, Yue Wei, Jinjie Xu, Qingyao Zhang, Lihua Academy for Engineering and Technology Fudan University China Institute of Metaverse & Intelligent Medicine Fudan University China Engineering Research Center of AI and Robotics Ministry of Education China Jilin Provincial Key Laboratory of Intelligence Science and Engineering China Artificial Intelligence and Unmanned Systems Engineering Research Center of Jilin Province China ByteDance Inc China

With the widespread use of virtual reality applications, 3D scene generation has become a new challenging research frontier. 3D scenes have highly complex structures and need to ensure that the output is dense, coherent, and contains all necessary structures. Many current 3D scene generation methods rely on pre-trained text-to-image diffusion models and monocular depth estimators. However, the generated scenes occupy large amounts of storage space and often lack effective regularisation methods, leading to geometric distortions. To this end, we propose BloomScene, a lightweight structured 3D Gaussian splatting for crossmodal scene generation, which creates diverse and high-quality 3D scenes from text or image inputs. Specifically, a crossmodal progressive scene generation framework is proposed to generate coherent scenes utilizing incremental point cloud reconstruction and 3D Gaussian splatting. Additionally, we propose a hierarchical depth prior-based regularization mechanism that utilizes multi-level constraints on depth accuracy and smoothness to enhance the realism and continuity of the generated scenes. Ultimately, we propose a structured context-guided compression mechanism that exploits structured hash grids to model the context of unorganized anchor attributes, which significantly eliminates structural redundancy and reduces storage overhead. Comprehensive experiments across multiple scenes demonstrate the significant potential and advantages of our framework compared with several baselines. Copyright © 2025, The Authors. All rights reserved.

关键词： Virtual reality

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Parameter-Sharing-Based Average-Consensus Time Synchronization in IoT Networks

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IEEE Internet of Things Journal 2023年第9期10卷 8215-8227页

作者： Shi, Fanrong Yang, Simon X. Mukherjee, Mithun Jiang, Hong Da Costa, Daniel Benevides Wong, Wing-Kwong Southwest University of Science and Technology Robot Technology Used for Special Environment Key Laboratory of Sichuan Province School of Information Engineering Mianyang621010 China School of Engineering University of Guelph Advanced Robotics and Intelligent Systems Laboratory GuelphONN1G 2W1 Canada Nanjing University of Information Science and Technology School of Artificial Intelligence School of Future Technology Nanjing210044 China Technology Innovation Institute Abu Dhabi United Arab Emirates National Yunlin University of Science and Technology Future Technology Research Center Douliu64002 Taiwan

Average-consensus protocol is one of the ways to develop distributed time-synchronization algorithms in Internet of Things (IoT) networks. However, the large number of iteration leads to a common time notion issue in nodes. This poses a critical challenge in the convergence of the time-synchronization algorithm and resulting asymptotic convergence in the average consensus protocol. In this article, a parameter-sharing-based average-consensus time-synchronization (PACTS) algorithm is proposed. For fast convergence, the proposed PACTS quickly forwards the time information to multihop nodes and employs multihop average-consensus instead of single-hop average consensus. Specifically, a node asynchronously and periodically broadcasts the relative clock offset estimation of neighbors with its local time information. Meanwhile, the relative clock offset estimation of the multihop node is calculated and used to estimate the average value. Consequently, an average consensus among local multihop nodes is obtained. As a result, the iteration number and convergence time are significantly reduced over the network. Finally, the experimental results indicate that the proposed PACTS algorithm has low complexity, high accuracy, and quick convergence. © 2023 IEEE.

关键词： Topology

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A Battery Control Strategy for Sequential Utilization of Energy Storage systems in Power Batteries

A Battery Control Strategy for Sequential Utilization of Ene...

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IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Bowen Li Shijie Cui Lizhong Zhu College of Automation and Electrical Engineering Shenyang Ligong University Liaoning 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 Liaoning China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

With the widespread adoption of energy storage systems utilizing power batteries, battery lifespan degradation has become a primary constraint on system performance. To address this issue, this study proposes a lifespan-optimized battery control strategy. Firstly, a battery lifespan calculation model for the energy storage system is established, taking into detailed consideration key parameters such as battery State of Charge (SOC). Secondly, a control strategy based on SOC for charging and discharging is designed, aiming to minimize battery lifespan consumption to the greatest extent possible. Finally, experimental results demonstrate that the proposed control strategy significantly improves the lifespan of the energy storage system compared to existing strategies. This research provides an effective means of control for the long-term stable operation of power battery systems.

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Determination of the Characteristics of Gears of Robot-Like systems by Analytical Description of their Structure

Determination of the Characteristics of Gears of Robot-Like ...

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

作者： Stefan Landler Raúl Molina Blanco Michael Otto Birgit Vogel-Heuser Markus Zimmermann Karsten Stahl Technical University of Munich Germany TUM School of Engineering and Design Department of Mechanical Engineering Munich Institute of Robotics and Machine Intelligence (MIRMI) Institute of Machine Elements Gear Research Center (FZG) Institute of Automation and Information Systems (AIS) Laboratory for Product Development and Lightweight Design (LPL)

The axes of robots and robot-like systems (RLS) usually include e-motor-gearbox-arrangements for optimal connection of the elements. The characteristics of the drive system and thus also of the robot depend strongly on the gears. Different gearbox designs are available which differ in stiffness, efficiency and further properties. For an application-optimal design of RLS a uniform documentation and a comparability of gearbox concepts is a decisive factor. The application-optimal design is supported by an interdisciplinary approach between mechanical engineering and software design, guided by adequate product development methodology. The quite heterogeneous characterization of gearboxes for RLS which is currently the state of the art is a relevant obstacle in the flexible and optimal design of RLS. The paper shows the analysis of the gear structure with unified symbols for specific machine elements and contact types. The introduced method gives insight into the mechanical structure of the gearboxes. Similarities between gear types can thus be revealed. This also enables the classification of new developments in the state of the art. Moreover, the developed method for analyzing the gear structure can be used to determine the characteristics of gears. Examples for these characteristics are backlash, efficiency or stiffness. Specifically, the stiffness of gears can be synthesized by the force action of individual contacts and the individual phenomena that occur with them. The representation by individual phenomena also makes it possible to extend the calculation to include influencing parameters such as temperature that have not been sufficiently taken into account so far.

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A Novel Signals and Features Fusion Method Based on Fisher Vector for Severity Assessment in Parkinson's Disease 8

A Novel Signals and Features Fusion Method Based on Fisher V...

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8th IEEE International Conference on Advanced robotics and Mechatronics, ICARM 2023

作者： Sun, Yubo Cheng, Yuanyuan Zhu, Zhizhong Liu, Jinrui Guo, Zhenhui Yu, Ningbo College of Artificial Intelligence Nankai University Tianjin300350 China Tianjin Huanhu Hospital Department of Rehabilitation Medicine Tianjin300350 China Nankai University Engineering Research Center of Trusted Behavior Intelligence Ministry of Education Tianjin300350 China Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin300350 China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen518083 China

ISBN: (纸本)9798350300178

Parkinson's disease (PD) is a progressive neurode-generative disorder that requires accurate severity assessment to facilitate proper medication and interventions. This study proposes a novel method for PD severity assessment by fusing bilateral surface electromyography (sEMG) signals and features using Fisher vector. The sEMG signals were measured from the tibialis anterior (TA) and lateral gastrocnemius (GL) muscles in 28 PD patients. Preprocessing was performed to remove industrial frequency noise and low-frequency trend, followed by feature extraction using a customized feature generator that included time-domain and frequency-domain features. The extracted features and preprocessed sEMG signals were then vectorized using the Fisher vector and fused as input to the classifier. Our results demonstrated that considering bilateral features is more favorable than unilateral features for the PD severity assessment. Moreover, the proposed method significantly enhanced the classification accuracy, with a remarkable classification accuracy of 96.00% achieved through the combination of our proposed method with a decision tree classifier. © 2023 IEEE.

关键词： Time domain analysis

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Multi-contact Stochastic Predictive Control for Legged Robots with Contact Locations Uncertainty

arXiv

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arXiv 2023年

作者： Gazar, Ahmad Khadiv, Majid Del Prete, Andrea Righetti, Ludovic Max Planck Institute for Intelligent Systems Tuebingen Germany Munich Institute of Robotics and Machine Intelligence Technical University of Munich Germany Industrial Engineering Department University of Trento Italy Tandon School of Engineering New York University New York United States

Trajectory optimization under uncertainties is a challenging problem for robots in contact with the environment. Such uncertainties are inevitable due to estimation errors, control imperfections, and model mismatches between planning models used for control and the real robot dynamics. This induces control policies that could violate the contact location constraints by making contact at unintended locations, leading to unsafe motion plans. This work addresses the problem of robust kino-dynamic whole-body trajectory optimization using stochastic nonlinear model predictive control (SNMPC) by considering additive uncertainties on the model dynamics subject to contact location chance constraints as a function of the robot’s full kinematics. We demonstrate the benefit of using SNMPC over classic nonlinear MPC (NMPC) for whole-body trajectory optimization in terms of contact location constraint satisfaction (safety). We run extensive Monte-Carlo simulations for a quadruped robot performing agile trotting and bounding motions over small stepping stones, where contact location satisfaction becomes critical. Our results show that SNMPC can perform all motions safely with 100% success rate, while NMPC failed 48.3% of all motions. © 2023, CC BY.

关键词： Stochastic systems

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A memory-based BCI approach for training of executive function in ASD

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Brain Stimulation 2023年第1期16卷 264-265页

作者： Dias, Camila Sousa, Teresa Cruz, Aniana Costa, Diana Castelhano, João Pires, Gabriel Castelo-Branco, Miguel CIBIT – Coimbra Institute for Biomedical Imaging and Translational Research University of Coimbra Portugal ICNAS - Institute for Nuclear Sciences Applied to Health University of Coimbra Portugal FMUC - Faculty of Medicine University of Coimbra Portugal ISR - Institute of Systems and Robotics University of Coimbra Portugal Polytechnic Institute of Tomar Department of Engineering Portugal

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Industrial Wireless Network Access Selection Based on Multilevel Fuzzy Neural Network

Industrial Wireless Network Access Selection Based on Multil...

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Chinese Control and Decision Conference, CCDC

作者： Yan Xu Zhijun Gao Sichao Zhang School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang 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)9798350387780

ISBN: (纸本)9798350387797

In order to solve the issues in the heterogeneous industrial wireless network access selection such as delay and reliability, this paper present an industrial wireless network access selection based on multilevel fuzzy neural network. The algorithm divides the input attributes into three categories: network attributes, terminal attributes, and environment attributes. It synchronously inputs them into the fuzzy neural network to obtain the candidate network scores, which effectively reduces the number of fuzzy rules and reduces the algorithm complexity. Furthermore, this paper also consider the influence of the industrial environment on network selection and quantitatively reduce the number of switching and blocking rate. The simulation experimental results show that this algorithm is able to achieve fast network access selection in noisy and occluded industrial heterogeneous wireless networks, and the blocking rate can remain low in the presence of dense terminals.

关键词： Wireless networks Neural networks Switches Mean square error methods Interference Fuzzy neural networks Delays

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UAV Path Planning Based on Simultaneous Optimization of Monitoring Frequency and Security

UAV Path Planning Based on Simultaneous Optimization of Moni...

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第34届中国控制与决策会议

作者： Yifei Shu Yang Chen Mian Hu Huaiyu Wu Xingang Zhao Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences

UAVs have great potential when applied to persistent monitoring,but there are still problems such as difficulty in ensuring the monitoring frequency and easy leakage of monitoring path ***,it is necessary to increase the UAV monitoring frequency of targets and the randomness of monitoring paths as much as possible on the premise of covering all monitoring *** response to the above problems,this paper studies the UAV path planning problem of simultaneous optimization of monitoring frequency and path *** the evaluation of monitoring frequency and path security based on monitoring overdue time and path entropy,a mathematical model for UAV path planning is *** improved ant colony algorithm based on the monitoring overdue time(Overdue-aware Ant Colony Optimization,OACO) is designed,and finally the UAV flight trajectory with high monitoring frequency and the high monitoring path security is *** simulation results show that the method proposed in this paper can effectively improve the monitoring frequency of each monitoring node to be accessed,improve the security of the UAV monitoring path,which is of great significance for enhancing monitoring security and preventing intrusion.

关键词： UAV persistent monitoring path planning monitoring frequency overdue time security

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Terrain Classification Using Inside-Wheel Cameras Based on Wheel-Terrain Interaction Characteristics

Terrain Classification Using Inside-Wheel Cameras Based on W...

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IEEE International Conference on robotics and Biomimetics

作者： Longteng Hu Feng Xue Chen Yao Yunzhou Li Jin Wei Peichen Wang Zheng Zhu Zhenzhong Jia Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology (SUSTech) Shenzhen China Harbin of Institute of Technology (HIT) Harbin China

The wheeled rover is widely used in off-road planetary exploration tasks nowadays. However, complex operating environment like soft deformable terrains on the planet surface demands the high performance of rover. In order to reduce danger, environmental perception technologies need to be used on the rover. Terrain classification is a critical method in environmental perception. In this research, a wheel-vision-based terrain classification method is proposed. This method uses wheel-terrain interaction parameters obtained from inside-wheel cameras which are strongly related to the terrain as input. We use single-wheel testbed to collect and process data to form a dataset. The data is obtained under different slip ratios and different terrains. Then, we train four terrain classification models using machine learning algorithms and analyze their performance. From the results, all performance criteria of the models achieve over 95%, with DT being the optimal method. The feasibility of our method in the case of fewer training samples and the rationality of dataset selection are also verified through the results of different training set sizes and the ablation experiment.

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