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30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

作者： Li, Jiahao Wang, Huandong Chen, Xinlei Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China Tsinghua Univ Pengcheng Lab Shenzhen Int Grad Sch RISC V Int Open Source Lab Shenzhen Peoples R China

ISBN: (纸本)9798400704901

Urban mobility undergoes a profound decline in the aftermath of a disaster, subsequently exhibiting a complex recovery trajectory. Effectively capturing and predicting this dynamic recovery process holds paramount importance for devising more efficient post-disaster recovery strategies, such as resource allocation to areas with protracted recovery periods. Existing models for post-disaster mobility recovery predominantly employ basic mathematical methods, which are strongly based on simplifying assumptions, and their limited parameters restrict their capacity to fully capture the mobility recovery patterns. In response to this gap, we introduce the Coupled Dynamic Graph ODE Network (CDGON) to model the intricate dynamics of post-disaster mobility recovery. Our model seamlessly integrates existing physical knowledge pertaining to post-disaster mobility recovery and incorporates the nuanced interactions between intra-regional and inter-regional population flows. Extensive experimental results demonstrate the efficiency of our model in capturing the dynamic recovery patterns of urban population mobility in post-disaster scenarios, surpassing the capabilities of current dynamic graph prediction models.

关键词： Physics-informed Graph Neural ODE Urban Mobility Recovery Post-Disaster

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M³Tac: A Multispectral Multimodal Visuotactile Sensor With Beyond-Human Sensory Capabilities

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IEEE TRANSACTIONS ON ROBOTICS 2024年 40卷 4506-4525页

作者： Li, Shoujie Yu, Haixin Pan, Guoping Tang, Huaze Zhang, Jiawei Ye, Linqi Zhang, Xiao-Ping Ding, Wenbo Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Key Lab Ubiquitous Data Enabling Shenzhen 518055 Peoples R China Shanghai Univ Inst Artificial Intelligence Collaborat Innovat Ctr Marine Artificial Intellige Shanghai 200444 Peoples R China Tsinghua Berkeley Shenzhen Inst RISC V Int Open Source Lab Shenzhen 518055 Peoples R China

To realize the exquisite interaction and precise manipulation for the robot, in this article, we propose a multispectral multimodal visuotactile sensor named M(3)Tac, which combines visible, near-infrared, and mid-infrared imaging technologies for the first time and can exceed the sensing ability of human skin in terms of resolution (719 pixels/cm(2)), temperature sensing range (-20-130degree celsius), etc. The M(3)Tac cannot only realize high-quality sensing of deformation, texture, force, stickiness, and temperature comparable to human skin but also can realize proximity sensing that is lacking for human skin. To achieve this, we not only design a multispectral imaging system with an elastic film whose light penetrability can be regulated by the brightness of the light, but also develop corresponding algorithms, including the pixel-level force sensing with finite element method (accuracy: +/- 0.023N), the proximity perception (accuracy: +/- 3.8 mm), the 3-D reconstruction (accuracy: 0.33 mm), the super-resolution temperature sensing (accuracy: +/- 0.3degree celsius), the multimodal fusion classification (accuracy: 98%), and the stickiness recognition (accuracy: 98%). Finally, we conduct experiments to verify the effectiveness and application potential of our research.

关键词： Multimodal sensing multispectral imaging visuotactile sensing Multimodal sensing multispectral imaging visuotactile sensing

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SolareSkin: Self-powered Visible Light Sensing Through a Solar Cell E-Skin

SolareSkin: Self-powered Visible Light Sensing Through a Sol...

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ACM international Joint Conference on Pervasive and Ubiquitous Computing / ACM international Symposium on Wearable Computing (UbiComp/ISWC)

作者： Li, Jiarong Ge, Changshuo Tao, Jun Wang, Jingyang Xu, Xiaomin Chen, Xinlei Gui, Weihua Liang, Xiaojun Ding, Wenbo Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Peng Cheng Lab RISC V Int Open Source Lab Shenzhen Peoples R China Cent South Univ Changsha Peoples R China

ISBN: (纸本)9798400702006

SolareSkin is a self-powered and ubiquitous electronic skin equipped with ultraflexible organic solar cells for visible light sensing and energy harvesting. This dual-functional system captures light signals, transforms them into electrical impulses and enables multi-class gesture and activity recognition. Its design employs a photocurrent model that allows solar cells to serve as energy harvesters and visible light sensors simultaneously. The solar cells demonstrate a decent conversion rate of incident light into electricity, supporting an efficient, sustainable operation. Additionally, the system incorporates advanced system integration with a low-powered data collection board embedded with wireless transmission modules and an intuitive user interface. An algorithm is employed for signal analysis with data pre-processing methods and several machine learning models. The data pre-processing methods comprise filtering, scaling, normalization, segmentation, and downsampling of raw sensor data to reduce noise and increase prediction accuracy. The machine learning model evaluation focuses on three algorithms: Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Random Forest, due to their efficiency with high-dimensional, nonlinear data. The experimental results suggest the excellent performance of SVM in recognizing 7-class finger gestures and 7-class body activities, with accuracies of 97.3% and 96.7%, respectively. This advancement in electronic skin technology is promising for ubiquitous human-centric sensing, enabling various applications such as healthcare monitoring, human-computer interaction, and smart homes.

关键词： Electronic Skin Visible light sensing Human activity recognition Energy harvesting

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TacTID: High-Performance Visuo-Tactile Sensor-Based Terrain Identification for Legged Robots

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IEEE SENSORS JOURNAL 2024年第16期24卷 26487-26495页

作者： Song, Ziwu Li, Chenchang Quan, Zhentan Mu, Shilong Li, Xiaosa Zhao, Ziyi Jin, Wanxin Wu, Chenye Ding, Wenbo Zhang, Xiao-Ping Tsinghua Univ Shenzhen Int Grad Sch Shenzhen 518071 Peoples R China Arizona State Univ Sch Engn Matter Transport & Energy Tempe AZ 85287 USA Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Peoples R China RISC V Int Open Source Lab Shenzhen 518055 Peoples R China

Adapting to various terrains and improving locomotion performance have always been challenging for legged robots. Inspired by the animal somatosensory system in walking and balancing, the robot can benefit from the foot sensors as well when interacting with the terrain and gain better performance. In this article, we propose a visuo-tactile foot sensor with embedded markers, named TacTID, for legged robot terrain identification. By introducing elastic embedded markers on an elastic hemispherical shell, TacTID could detect 3-D pressure distribution directly, based on which the terrain coefficient of sliding friction (COSF) and terrain effective stiffness can be estimated precisely. The results of the COSF prediction experiment demonstrate that TacTID exhibits an average error of 0.036, which is notably lower than that of TacTip. For the effective stiffness estimation experiment, TacTID is tested on ten silicone cubes with different Young's moduli, and the absolute average estimation error is less than 0.1 MPa. Besides, a quadruped robot is equipped with TacTID for real-time terrain identification, and the possibility to estimate COSF and the terrain effective stiffness in various terrains is demonstrated. This research highlights the potential of TacTID for robotic applications requiring precise contact force and terrain identification in real-time.

关键词： Robot sensing systems Sensors Robots Legged locomotion Foot Real-time systems Force Legged robot tactile sensing terrain identification

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Cooperative Multi-Type Multi-Agent Deep Reinforcement Learning for Resource Management in Space-Air-Ground integrated Networks

Cooperative Multi-Type Multi-Agent Deep Reinforcement Learni...

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ACM international Joint Conference on Pervasive and Ubiquitous Computing / ACM international Symposium on Wearable Computing (UbiComp/ISWC)

作者： Zhang, Hengxi Tang, Huaze Ding, Wenbo Zhang, Xiao-Ping Tsinghua Berkeley Shenzhen Inst Shenzhen Guangdong Peoples R China RISC V Int Open Source Lab Shenzhen Guangdong Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON Canada

ISBN: (纸本)9798400702006

The Space-Air-Ground integrated Network (SAGIN), integrating heterogeneous devices including low earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and ground users (GUs), holds significant promise for the advancing smart city applications. However, resource management of the SAGIN is a challenge requiring urgent study in that inappropriate resource management will cause poor data transmission, and hence affect the services in smart cities. In this paper, we develop a comprehensive SAGIN system that encompasses five distinct communication links and propose an efficient cooperative multi-type multi-agent deep reinforcement learning (CMT-MARL) method to address the resource management issue. The experimental results highlight the efficacy of proposed CMT-MARL, as evidenced by key performance indicators such as the overall transmission rate and transmission success rate. These results underscore the potential value and feasibility of future implementation of the SAGIN.

关键词： SAGIN resource management multi-agent reinforcement learning

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BEANet: An Energy-efficient BLE Solution for High-capacity Equipment Area Network

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ACM TRANSACTIONS ON SENSOR NETWORKS 2024年第3期20卷 1-23页

作者： Xu, Yifan Dang, Fan Liu, Kebin Zhu, Zhui Chen, Xinlei Wang, Xu Miao, Xin Zhao, Haitian Tsinghua Univ Sch Software Beijing Peoples R China Tsinghua Univ Global Innovat Exchange Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Beijing Peoples R China Pengcheng Lab Shenzhen Peoples R China RISC V Int Open Source Lab Shenzhen Peoples R China

The digital transformation of factories has greatly increased the number of peripherals that need to connect to a network for sensing or control, resulting in a growing demand for a new network category known as the Equipment Area Network (EAN). The EAN is characterized by its cable-free, high-capacity, low-latency, and low-power features. To meet these expectations, we present BEANet, a novel solution designed specifically for EAN that combines a two-stage synchronization mechanism with a time division protocol. We implemented the system using commercially available Bluetooth Low Energy (BLE) modules and evaluated its performance. Our results show that the network can support up to 150 peripherals with a packet reception rate of 95.4%, which is only 0.9% lower than collision-free BLE transmission. When the cycle time is set to 2 s, the average transmission latency for all peripherals is 0.1 s, while the power consumption is 18.9 mu W, which is only half that of systems using LLDN or TSCH. Simulation results also demonstrate that BEANet has the potential to accommodate over 30,000 peripherals under certain configurations.

关键词： Equipment Area Network Bluetooth Low Energy industrial internet

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Path Generation for Wheeled Robots Autonomous Navigation on Vegetated Terrain

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第2期9卷 1764-1771页

作者： Jian, Zhuozhu Liu, Zejia Shao, Haoyu Wang, Xueqian Chen, Xinlei Liang, Bin Tsinghua Univ Ctr Artificial Intelligence & Robot Shenzhen Int Grad Sch Shenzhen 518005 Peoples R China Harbin Inst Technol Sch Mech Engn & Automat Shenzhen 518055 Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Pengcheng Lab Shenzhen 518055 Peoples R China RISC V Int Open Source Lab Shenzhen 518055 Peoples R China

Wheeled robot navigation has been widely used in urban environments, but navigation in wild vegetation is still challenging. External sensors (LiDAR, camera etc.) are often used to construct point cloud map of the surrounding environment, however, the supporting rigid ground used for travelling cannot be detected due to the occlusion of vegetation. This often leads to unsafe or non-smooth paths during the planning process. To address the drawback, we propose the PE-RRT* algorithm, which effectively combines a novel support plane estimation method and sampling algorithm to generate real-time feasible and safe path in vegetation environments. In order to accurately estimate the support plane, we combine external perception and proprioception, and use Multivariate Gaussian Processe Regression (MV-GPR) to estimate the terrain at the sampling nodes. We build a physical experimental platform and conduct experiments in different outdoor environments. Experimental results show that our method has high safety, robustness, and generalization. The source code is released for the reference of the community.

关键词： Field robots motion and path planning collision avoidance

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LSync: A Universal Timeline-Synchronizing Solution for Live Streaming

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IEEE-ACM TRANSACTIONS ON NETWORKING 2024年第5期32卷 4144-4159页

作者： Dang, Fan Xu, Yifan Xu, Rongwu Chen, Xinlei Liu, Yunhao Tsinghua Univ Global Innovat Exchange Beijing 100084 Peoples R China Tsinghua Univ Sch Software Beijing 100084 Peoples R China Tsinghua Univ Inst Interdisciplinary Informat Sci Beijing Peoples R China Shenzhen Int Grad Sch Peng Cheng Lab Shenzhen Guangdong Peoples R China Shenzhen Int Grad Sch RISC V Int Open Source Lab Shenzhen Guangdong Peoples R China

The widespread use of intelligent devices and the development of mobile networks have led to the increasing popularity of live-streaming services worldwide. In addition to video and audio transmissions, a wide range of media content is also sent to audiences, such as player statistics for sports streams and subtitles for live news. However, due to the diverse transmission process between live streams and other media content, synchronizing them has become a significant challenge. Unfortunately, existing commercial solutions are not universal, requiring specific server cloud services or CDNs and limiting users' free choices of web infrastructures. To address this issue, we propose a lightweight and universal solution called LSync, which inserts a series of audio signals containing metadata into the original audio stream. Based on the embedded metadata, a well-designed timeline-synchronizing solution helps to synchronize the information stream to the live stream. It brings no modifications to the original live broadcast process and thus fits prevalent live broadcast infrastructures. Evaluations show that the proposed solution reduces the signal processing delay to around 5% of an audio buffer length in mobile phones and ensures real-time signal processing. It achieves a channel utilization of more than 150 bps/kHz in a specific configuration, greatly outperforming recent works. Furthermore, the proposed synchronization mechanism reaches a precision of 24.84 ms on average, which matches people's viewing habits.

关键词： Streams Synchronization Metadata Delays Chirp Acoustics Streaming media Live streaming synchronization chirp signal

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Autonomous Swarm Robot Coordination via Mean-Field Control Embedding Multi-Agent Reinforcement Learning

Autonomous Swarm Robot Coordination via Mean-Field Control E...

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

作者： Tang, Huaze Zhang, Hengxi Shi, Zhenpeng Chen, Xinlei Ding, Wenbo Zhang, Xiao-Ping Tsinghua Berkeley Shenzhen Inst Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China RISC Int Open Source Lab Shenzhen 518055 Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON Canada

ISBN: (纸本)9781665491907

The learning approaches of designing a controller to guide the collective behavior of swarm robots have gained significant attention in recent years. However, the scalability of swarm robots and their inherent stochasticity complicate the control problem due to increasing complexity, unpredictability, and non-linearity. Despite considerable progress made in swarm robotics, addressing these challenges remains a significant issue. In this work, we model the stochastic dynamics of a swarm robot system and then propose a novel control framework based on a mean-field control (MFC) embedding multi-agent reinforcement learning (MARL) approach named MF-MARL to deal with these challenges. While MARL is able to deal with stochasticity statistically, we integrate MFC, allowing MF-MARL to cope with large-scale robots. Moreover, we apply statistical moments of robots' state and control action to discretize continuous input and enable MF-MARL to be applied in continuous scenarios. To demonstrate the effectiveness of MF-MARL, we evaluate the performance of the robots on a specific swarm simulation platform. The experimental results show that our algorithm outperforms the traditional algorithms both in navigation and manipulation tasks. Finally, we demonstrate the adaptability of the proposed algorithm through the component failure test.

关键词： Stochastic systems

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A Wireless integrated System with Hybrid Embedded Sensing for the Continuous Monitoring of Bird Flight

A Wireless Integrated System with Hybrid Embedded Sensing fo...

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ACM international Joint Conference on Pervasive and Ubiquitous Computing / ACM international Symposium on Wearable Computing (UbiComp/ISWC)

作者： Mu, Shilong Chow, Ho Ngai Zhou, Mi Zhao, Runze Lei, Kai Chong Geng, Zihan Han, Yuxing Ding, Wenbo Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen Int Grad Sch Shenzhen Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China Tsinghua Univ Shenzhen Int Grad Sch RISC V Int Open Source Lab Shenzhen Peoples R China

ISBN: (纸本)9798400702006

The intricate flight patterns exhibited by birds have sparked considerable interest in many fields. However, traditional external visual observation has significant limitations, including susceptibility to line-of-sight occlusion, uneven lighting effects, and lack of continuous detection. In this paper, we design a hybrid embedded system that can perform bird flight postures recognition by integrating inertial measurement unit (IMU) with flexible strain sensor. The proposed system leverages a strain sensor based on laser-induced graphene (LIG), strategically positioned on the bird's wing joints, in conjunction with a high-precision IMU deployed on the bird's torso. Through a learning architecture, we achieve an impressive accuracy of 99.48% in identifying eight commonly observed bird flight postures. The integration of the proposed system offers exciting possibilities and serves as a powerful tool for advancing our understanding of the mechanisms underlying creature flight.

关键词： Flexible Strain Sensor Multimodal Deep Learning Flight Posture Recognition intelligent System.

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