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Compliance while resisting: a shear-thickening fluid controller for physical human-robot interaction

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arXiv 2025年

作者： Chen, Lu Chen, Lipeng Chen, Xiangchi Lu, Haojian Zheng, Yu Wu, Jun Wang, Yue Zhang, Zhengyou Xiong, Rong State Key Laboratory of Industrial Control Technology Zhejiang University Zhejiang Hangzhou China Tencent Robotics X Lab Shenzhen China

Physical human-robot interaction (pHRI) is widely needed in many fields, such as industrial manipulation, home services, and medical rehabilitation, and puts higher demands on the safety of robots. Due to the uncertainty of the working environment, the pHRI may receive unexpected impact interference, which affects the safety and smoothness of the task execution. The commonly used linear admittance control (L-AC) can cope well with high-frequency small-amplitude noise, but for medium-frequency high-intensity impact, the effect is not as good. Inspired by the solid-liquid phase change nature of shear-thickening fluid, we propose a Shear-thickening Fluid Control (SFC) that can achieve both an easy human-robot collaboration and resistance to impact interference. The SFC’s stability, passivity, and phase trajectory are analyzed in detail, the frequency and time domain properties are quantified, and parameter constraints in discrete control and coupled stability conditions are provided. We conducted simulations to compare the frequency and time domain characteristics of L-AC, nonlinear admittance controller (N-AC), and SFC, and validated their dynamic properties. In real-world experiments, we compared the performance of L-AC, N-AC, and SFC in both fixed and mobile manipulators. L-AC exhibits weak resistance to impact. N-AC can resist moderate impacts but not high-intensity ones, and may exhibit self-excited oscillations. In contrast, SFC demonstrated superior impact resistance and maintained stable collaboration, enhancing comfort in cooperative water delivery tasks. Additionally, a case study was conducted in a factory setting, further affirming the SFC’s capability in facilitating human-robot collaborative manipulation and underscoring its potential in industrial applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Industrial robots

Contextual Multi-Armed Bandit-Based Dynamic Cooperative Link Configuration for AUV in UWASNs With Energy Harvesting

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Dai, Jun Li, Xinbin Han, Song Yu, Junzhi Liu, Zhixin Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Yanshan University Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Qinhuangdao066004 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Beijing100871 China

This paper investigates the cooperative link configuration problem for Autonomous Underwater Vehicle (AUV) in Underwater Acoustic (UWA) sensor networks with Energy Harvesting (EH), which aims to maximize long-term cumulative capacity by jointly optimizing cooperation relay, AUV transmission power, and relay transmission power. Subject to unknown time-varying Channel state Information (CSI), unpredictable stochastic EH, and variable communication topology caused by AUV mobility, the proposed problem is an unknown dynamic combination decision optimization problem. To resist the change of the dynamic UWA communication topology with unknown time-varying CSI and EH, a novel collaborative contextual multi-armed bandit learning framework is proposed, which allows relay nodes to learn cooperatively and enriches the learning information of relays. Consequently, the proposed learning framework can resist the change of the dynamic UWA communication topology efficiently, thereby achieving the superior link configuration quickly. Besides, a learning-rate adjustment rule for the dynamic UWA communication topology is proposed to adaptively balance the exploration-exploitation, thereby avoiding missing the real superior joint relay-power configuration. Finally, simulation results show the significantly superiority of the proposed scheme. © 2015 IEEE.

关键词： Cooperative communication

Improving the Sparse Structure Learning of Spiking Neural Networks from the View of Compression Efficiency

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arXiv 2025年

作者： Shen, Jiangrong Xu, Qi Pan, Gang Chen, Badong Faculty of Electronic and Information Engineering Xi’an Jiaotong University China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China State Key Lab of Brain-Machine Intelligence Zhejiang University China National Key Lab of Human-Machine Hybrid Augmented Intelligence Xi’an Jiaotong University China School of Computer Science Dalian University of Technology China

The human brain utilizes spikes for information transmission and dynamically reorganizes its network structure to boost energy efficiency and cognitive capabilities throughout its lifespan. Drawing inspiration from this spike-based computation, Spiking Neural Networks (SNNs) have been developed to construct event-driven models that emulate this efficiency. Despite these advances, deep SNNs continue to suffer from over-parameterization during training and inference, a stark contrast to the brain’s ability to self-organize. Furthermore, existing sparse SNNs are challenged by maintaining optimal pruning levels due to a static pruning ratio, resulting in either under or over-pruning. In this paper, we propose a novel two-stage dynamic structure learning approach for deep SNNs, aimed at maintaining effective sparse training from scratch while optimizing compression efficiency. The first stage evaluates the compressibility of existing sparse subnetworks within SNNs using the PQ index, which facilitates an adaptive determination of the rewiring ratio for synaptic connections based on data compression insights. In the second stage, this rewiring ratio critically informs the dynamic synaptic connection rewiring process, including both pruning and regrowth. This approach significantly improves the exploration of sparse structures training in deep SNNs, adapting sparsity dynamically from the point view of compression efficiency. Our experiments demonstrate that this sparse training approach not only aligns with the performance of current deep SNNs models but also significantly improves the efficiency of compressing sparse SNNs. Crucially, it preserves the advantages of initiating training with sparse models and offers a promising solution for implementing Edge AI on neuromorphic hardware. © 2025, CC BY-NC-ND.

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Modeling a Phased Array of Parametric Array Loudspeakers Using the Spherical Wave Expansion

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IEEE Transactions on Audio, Speech and Language Processing

IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 309-318页

作者： Tao Zhuang Jia-Xin Zhong Guilin Ma Feng Niu Jing Lu Key Laboratory of Modern Acoustics Nanjing University Nanjing China NJU-Horizon Intelligent Audio Lab Horizon Robotics Beijing China Graduate Program in Acoustics Pennsylvania State University University Park PA USA Audfly Technology Suzhou Company Ltd. Suzhou China Division of Mechanics and Acoustics National Institute of Metrology Beijing China

Parametric array loudspeakers (PALs) that utilize the phased array technique are receiving considerable attention because of their ability to electronically control and manipulate highly directional audio beams with great flexibility. However, accurately modeling the phased array PAL poses significant challenges due to the complex nature of the nonlinear processes involved, which results in the demodulated audio sound generated by the array not simply being a superposition of that generated by each individual array element. In addition, the nonlinear interaction between the ultrasound generated by any two distinct elements creates a coupled audio sound component that also contributes to the total audio sound field. Based on the quasilinear solution of the Westervelt equation as well as the spherical wave expansion method, this paper proposed a computationally efficient and accurate method to include the coupled audio sound component. Both simulations and experiments for a uniform linear PAL array are conducted to validate the method. The proposed method paved the way for simulating and designing phased array PAL systems.

关键词： Ultrasonic imaging Phased arrays Mathematical models Loudspeakers Harmonic analysis Field emitter arrays Acoustic beams Speech processing Computational modeling Transducers

Experimentally Probing Non-Hermitian Spectral Transition and Eigenstate Skewness

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arXiv 2025年

作者： Zhong, Jia-Xin Kim, Jeewoo Chen, Kai Lu, Jing Ding, Kun Jing, Yun Graduate Program in Acoustics The Pennsylvania State University University ParkPA16802 United States Key Laboratory of Modern Acoustics Institute of Acoustics Nanjing University Nanjing210093 China Fudan University Shanghai200438 China NJU-Horizon Intelligent Audio Lab Horizon Robotics Beijing100094 China

Non-Hermitian (NH) systems exhibit intricate spectral topology arising from complex-valued eigenenergies, with positive/negative imaginary parts representing gain/loss. Unlike the orthogonal eigenstates of Hermitian systems, NH systems feature left and right eigenstates that form a biorthogonal basis and can differ significantly, showcasing pronounced skewness between them. These characteristics give rise to unique properties absent in Hermitian systems, such as the NH skin effect and ultra spectral sensitivity. However, conventional experimental techniques are inadequate for directly measuring the complex-valued spectra and left and right eigenstates—key elements for enhancing our knowledge of NH physics. This challenge is particularly acute in higher-dimensional NH systems, where the spectra and eigenstates are highly sensitive to macroscopic shapes, lattice geometry, and boundary conditions, posing greater experimental demands compared to one-dimensional systems. Here, we present a Green’s function-based method that enables the direct measurement and characterization of both complex-valued energy spectra and the left and right eigenstates in arbitrary NH lattices. Using active acoustic crystals as the experimental platform, we observe spectral transitions and eigenstate skewness in two-dimensional NH lattices under both nonreciprocal and reciprocal conditions, with varied geometries and boundary conditions. Our approach renders complex spectral topology and left eigenstates experimentally accessible and practically meaningful, providing new insights into these quantities. The results not only confirm recent theoretical predictions of higher-dimensional NH systems but also establish a universal and versatile framework for investigating complex spectral properties and NH dynamics across a wide range of physical platforms. Copyright © 2025, The Authors. All rights reserved.

关键词： Topology

Leveraging Semantic Graphs for Efficient and Robust LiDAR SLAM

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arXiv 2025年

作者： Wang, Neng Lu, Huimin Zheng, Zhiqiang Wang, Hesheng Liu, Yun-Hui Chen, Xieyuanli College of Intelligence Science and Technology National Key Laboratory of Equipment State Sensing and Smart Support National University of Defense Technology China IRMV Lab Department of Automation Shanghai Jiao Tong University China T Stone Robotics Institute Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong

Accurate and robust simultaneous localization and mapping (SLAM) is crucial for autonomous mobile systems, typically achieved by leveraging the geometric features of the environment. Incorporating semantics provides a richer scene representation that not only enhances localization accuracy in SLAM but also enables advanced cognitive functionalities for downstream navigation and planning tasks. Existing point-wise semantic LiDAR SLAM methods often suffer from poor efficiency and generalization, making them less robust in diverse real-world scenarios. In this paper, we propose a semantic graph-enhanced SLAM framework, named SG-SLAM, which effectively leverages the geometric, semantic, and topological characteristics inherent in environmental structures. The semantic graph serves as a fundamental component that facilitates critical functionalities of SLAM, including robust relocalization during odometry failures, accurate loop closing, and semantic graph map construction. Our method employs a dual-threaded architecture, with one thread dedicated to online odometry and relocalization, while the other handles loop closure, pose graph optimization, and map update. This design enables our method to operate in real time and generate globally consistent semantic graph maps and point cloud maps. We extensively evaluate our method across the KITTI, MulRAN, and Apollo datasets, and the results demonstrate its superiority compared to state-of-the-art methods. Our method has been released at https://***/nubot-nudt/SG-SLAM. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph theory

Development of a Bionic Hexapod Robot for Walking on Unstructured Terrain

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Journal of Bionic Engineering 2014年第2期11卷 176-187页

作者： He Zhang Yubin Liu Jie Zhao Jie Chen Jihong Yan State Key Lab of Robotics and System Harbin Institute of Technology Harbin 150080 P. R. China

This paper reports the design methodology and control strategy in the development of a novel hexapod robot HITCR-II that is suitable for walking on unstructured terrain. First, the entire sensor system is designed to equip the robot with the perception of external environment and its internal states. The structure parameters are optimized for improving the dexterity of the robot. Second, a foot-force distribution model and a compensation model are built to achieve posture control. The two models are capable of effectively improving the stability of hexapod walking on unstructured terrain. Finally, the Posture Control strategy based on Force Distribution and Compensation （PCFDC） is applied to the HITCR-II hexapod robot. The experimental results show that the robot can effectively restrain the vibration of trunk and keep stable while walking and crossing over the un- structured terrains.

关键词： hexapod robot structure optimization posture control force distribution force compensation

Comments on “Sidewinding with Minimal Slip: Snake and Robot Ascent of Sandy Slopes”

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机器人 2015年第2期37卷 254-256页

作者： LIU Jinguo GAO Yang WANG Yuechao MA Shugen LUO Yifan State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences STAR Lab Surrey Space Centre University of Surrey Department of Robotics College of Science and Engineering Ritsumeikan University

Marvi et al(Science,2014,vol.346,p.224)concluded a sidewinder rattlesnake increases the body contact length with the sand when granular incline angle *** also claimed the same principle should work on robotic snake *** have evidence to prove that this conclusion is only partial in describing the snake body-sand *** should be three phases that fully represent the snake locomotion behaviors during ascent of sandy slopes,namely lifting,descending,and *** snake body-sand interaction during the descending and ceasing phases helps with the climbing while such interaction during the lifting phase in fact contributes resistance.

关键词：机器人设计工业机器人应用

Anti-Degeneracy Scheme for Lidar SLAM based on Particle Filter in Geometry Feature-Less Environments

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arXiv 2025年

作者： Li, Yanbin Zhang, Wei Zhang, Zhiguo Shi, Xiaogang Li, Ziruo Zhang, Mingming Xie, Hongping Chi, Wenzheng School of Electronics Engineering Beijing University of Posts and Telecommunications Beijing100876 China Key Lab of Smart Agriculture Systems Ministry of Education China Agricultural University Beijing100083 China School of Integrated Circuit Science and Engineering Beihang University Beijing100191 China Co.Ltd Construction Branch State Grid Jiangsu Electric Power Jiangsu226000 China Robotics and Microsystems Center School of Mechanical and Electric Engineering Soochow University Suzhou215021 China

Simultaneous localization and mapping (SLAM) based on particle filtering has been extensively employed in indoor scenarios due to its high efficiency. However, in geometry feature-less scenes, the accuracy is severely reduced due to lack of constraints. In this article, we propose an anti-degeneracy system based on deep learning. Firstly, we design a scale-invariant linear mapping to convert coordinates in continuous space into discrete indexes, in which a data augmentation method based on Gaussian model is proposed to ensure the model performance by effectively mitigating the impact of changes in the number of particles on the feature distribution. Secondly, we develop a degeneracy detection model using residual neural networks (ResNet) and transformer which is able to identify degeneracy by scrutinizing the distribution of the particle population. Thirdly, an adaptive anti-degeneracy strategy is designed, which first performs fusion and perturbation on the resample process to provide rich and accurate initial values for the pose optimization, and use a hierarchical pose optimization combining coarse and fine matching, which is able to adaptively adjust the optimization frequency and the sensor trustworthiness according to the degree of degeneracy, in order to enhance the ability of searching the global optimal pose. Finally, we demonstrate the optimality of the model, as well as the improvement of the image matrix method and GPU on the computation time through ablation experiments, and verify the performance of the anti-degeneracy system in different scenarios through simulation experiments and real experiments. Copyright © 2025, The Authors. All rights reserved.

关键词： Mapping