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

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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Poster Abstract: TENG-enabled Self-powered Human-machine interfaces for the Metaverse 22

Poster Abstract: TENG-enabled Self-powered Human-machine Int...

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22nd ACM/IEEE international Conference on Information Processing in Sensor Networks (IPSN)

作者： Wang, Haoyang Man, Fanhang Wang, Zihan Liu, Yuxuan Chen, Xinlei Dingt, Wenbo Tsinghua Univ Shenzhen Int Grad Sch RISC V Int Open Source Lab Peng Cheng Lab Shenzhen Peoples R China Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen Peoples R China

ISBN: (纸本)9798400701184

Human-machine interface (HMI) of high degrees of freedom (DoF) is one of the most critical bases of the metaverse. The ideal HMI for the metaverse should be cheap, robust, customizable, and ergonomically friendly. In light of this, we propose a triboelectric nanogenerator (TENG)-based sensing system. We developed a low-cost, soft, light, and customizable TENG sensor to collect data from the human body. We then used an artificial neural network (ANN) to obtain the corresponding human motion from collected sensory data. The effectiveness of the proposed system is demonstrated with experiments of a working prototype.

关键词： Triboelectric nanogenerator Human-machine interface Metaverse

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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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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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A Bio-Inspired Event-Driven Mechanoluminescent Visuotactile Sensor for intelligent interactions

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ADVANCED FUNCTIONAL MATERIALS 2025年第20期35卷

作者： Sou, Kit-Wa Chan, Wang-Sing Lei, Kai-Chong Wang, Zihan Li, Shoujie Peng, Dengfeng Ding, Wenbo Tsinghua Univ Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Univ Calif Berkeley Dept Mech Engn Berkeley CA 94709 USA Shenzhen Univ Coll Phys & Optoelect Engn Shenzhen Key Lab Intelligent Opt Measurement & Det Key Lab Optoelect Devices & SystMinist Educ & Gua Shenzhen 518060 Peoples R China RISC V Int Open Source Lab Shenzhen 518055 Peoples R China

Event-driven sensors are essential for real-time applications, yet the integration of current technologies faces limitations such as high cost, complex signal processing, and vulnerability to noise. This work introduces a bio-inspired mechanoluminescence visuotactile sensor that enables standard frame-based cameras to perform event-driven sensing by emitting light only under mechanical stress, effectively acting as an event trigger. Drawing inspiration from the biomechanics of canine teeth, the sensor utilizes a rod-patterned array to enhance mechanoluminescent signal sensitivity and expand the contact surface area. In addition, a machine learning-enabled algorithm is designed to accurately analyze the interaction-triggered mechanoluminescence signal in real-time. The sensor is integrated into a quadruped robot's mouth interface, demonstrating enhanced interactive capabilities. The system successfully classifies eight interactive activities with an average accuracy of 92.68%. Comprehensive tests validate the sensor's efficacy in capturing dynamic tactile signals and broadening the application scope of robots in interaction with the environment.

关键词： event-driven sensing human-machine interfaces mechanoluminescence quadruped robots visuotactile sensing

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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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Scheduling UAV Swarm with Attention-based Graph Reinforcement Learning for Ground-to-air Heterogeneous Data Communication

Scheduling UAV Swarm with Attention-based Graph Reinforcemen...

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

作者： Ren, Jiyuan Xu, Yanggang Li, Zuxin Hong, Chaopeng Zhang, Xiao-Ping Chen, Xinlei Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen Peoples R China Tsinghua Univ Shenzhen Key Lab Ecol Remediat & Carbon Sequestra Inst Environm & Ecol Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China Tsinghua Univ Tsinghua Shenzhen Int Grad Sch RISC V Int Open Source Lab Shenzhen Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON Canada Tsinghua Univ Shenzhen Int Grad Sch Pengcheng Lab RISC V Int Open Source Lab Shenzhen Peoples R China

ISBN: (纸本)9798400702006

In disaster scenarios, unmanned aerial vehicles (UAVs) can serve as mobile base stations because of their maneuverability and synergy. However, due to constrained UAV communication capabilities and limited battery life, UAV base stations resource allocation for mobile sensors in a data-heterogeneous environment is a significant challenge when optimizing communication quality. To address this, we propose AGUZero, an attention-based graph reinforcement learning (RL) framework. Inspired by MuZero [27], AGUZero is designed to handle dynamic and uncontrollable environments based on Monte Carlo Tree Search (MCTS). Additionally, to tackle data heterogeneity, AGUZero represents the states using heterogeneous sub-graphs and employs an attention-based model to capture relationships among UAVs and sensors. The experimental results show that AGUZero outperforms other baseline models consistently when either the number of UAVs or the number of sensors is varying. AGUZero improves the data transmission ratio by 11.03% and 10.35% in the two cases respectively.

关键词： Disaster Response Path Planning Graph Neural Network Model-based Reinforcement Learning

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SmoothLander: A Quadrotor Landing Control System with Smooth Trajectory Guarantee Based on Reinforcement Learning

SmoothLander: A Quadrotor Landing Control System with Smooth...

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

作者： Zhao, Chenyu Wang, Haoyang Li, Jiaqi Man, Fanhang Mu, Shilong Ding, Wenbo Zhang, Xiaoping Chen, Xinlei Tsinghua Univ Shenzhen Int Grad Sch Beijing Peoples R China Pengcheng Lab RISC V Int Open Source Lab Shenzhen Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Shenzhen Peoples R China

ISBN: (纸本)9798400702006

The landing process of the quadrotors can be affected by the disturbance from the ground effect when approaching the landing surface. Such a disturbance significantly increases the chances of collision and jittering of the quadrotors, thereby posing threats to the safety of both the quadrotors and the mounted equipment. In light of this, we propose SmoothLander, an aerodynamics and reinforcement learning-based control system to stabilize the quadrotors under the influence of the ground effect and control noise. First, we design a landing trajectory for the quadrotor in accordance with aerodynamics. Then we design a reinforcement learning-based command generator to effectively optimize the quadrotor's landing behavior. We evaluate our control system through physical feature-based simulation and in-field experiments. The results show that our method can enable the quadrotor to land more smoothly and stably against control noise than the baseline.

关键词： Quadrotor Ground Effect Reinforcement Learning

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TransformLoc: Transforming MAVs into Mobile Localization Infrastructures in Heterogeneous Swarms

TransformLoc: Transforming MAVs into Mobile Localization Inf...

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IEEE Conference on Computer Communications (IEEE INFOCOM)

作者： Wang, Haoyang Xu, Jingao Zhao, Chenyu Lu, Zihong Cheng, Yuhan Chen, Xuecheng Zhang, Xiao-Ping Liu, Yunhao Chen, Xinlei Tsinghua Univ Shenzhen Int Grad Sch Beijing Peoples R China Tsinghua Univ Sch Software Beijing Peoples R China Harbin Inst Technol Harbin Peoples R China Pengcheng Lab Shenzhen Peoples R China RISC V Int Open Source Lab Shenzhen Peoples R China

ISBN: (纸本)9798350383515;9798350383508

A heterogeneous micro aerial vehicles (MAV) swarm consists of resource-intensive but expensive advanced MAVs (AMAVs) and resource-limited but cost-effective basic MAVs (BMAVs), offering opportunities in diverse fields. Accurate and real-time localization is crucial for MAV swarms, but current practices lack a low-cost, high-precision, and real-time solution, especially for lightweight BMAVs. We find an opportunity to accomplish the task by transforming AMAVs into mobile localization infrastructures for BMAVs. However, turning this insight into a practical system is non-trivial due to challenges in location estimation with BMAVs' unknown and diverse localization errors and resource allocation of AMAVs given coupled influential factors. This study proposes TransformLoc, a new framework that transforms AMAVs into mobile localization infrastructures, specifically designed for low-cost and resource-constrained BMAVs. We first design an error-aware joint location estimation model to perform intermittent joint location estimation for BMAVs and then design a proximity-driven adaptive grouping-scheduling strategy to allocate resources of AMAVs dynamically. TransformLoc achieves a collaborative, adaptive, and cost-effective localization system suitable for large-scale heterogeneous MAV swarms. We implement TransformLoc on industrial drones and validate its performance. Results show that TransformLoc outperforms baselines including SOTA up to 68% in localization performance, motivating up to 60% navigation success rate improvement.

关键词： Micro air vehicle (MAV)

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