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IEEE international Conference on Robotics and Automation (ICRA)

作者： Song, Ziwu Yu, Ran Zhang, Xuan Sou, Kit Wa Mu, Shilong Peng, Dengfeng Zhang, Xiao-Ping Ding, Wenbo Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China Shenzhen Univ Shenzhen Peoples R China RISC V Int Open Source Lab Shenzhen 518055 Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON M5B 2K3 Canada

ISBN: (纸本)9798350384581;9798350384574

Most vision-based tactile sensors use elastomer deformation to infer tactile information, which can not sense some modalities, like temperature. As an important part of human tactile perception, temperature sensing can help robots better interact with the environment. In this work, we propose a novel multi-modal vision-based tactile sensor, SATac, which can simultaneously perceive information on temperature, pressure, and shear. SATac utilizes the thermoluminescence of strontium aluminate to sense a wide range of temperatures with exceptional resolution. Additionally, the pressure and shear can also be perceived by analyzing the voronoi diagram. A series of experiments are conducted to verify the performance of our proposed sensor. We also discuss the possible application scenarios and demonstrate how SATac could benefit robot perception capabilities.

关键词： Shear flow

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Construction of a ferroelectric material BiOIO3 photoelectrocatalytic anode by triboelectric-photo coupling effect for degradation of antibiotics

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NANO ENERGY 2025年 139卷

作者： Qin, Yuanchu Xu, Qinghao Gong, Junhao Huang, Jie Wang, Wen-Long Lyu, Chuqiao Ding, Wenbo Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Guangdong Prov Engn Res Ctr Urban Water Recycling Key Lab Microorganism Applicat & Risk Control Shen Shenzhen 518055 Peoples R China Tsinghua Univ Inst Environm & Ecol Tsinghua Shenzhen Int Grad Sch Shenzhen Key Lab Ecol Remediat & Carbon Sequestrat Shenzhen 518055 Peoples R China Tsinghua Univ Inst Data & Informat Shenzhen Int Grad Sch Shenzhen Ubiquitous Data Enabling Key Lab Shenzhen 518055 Peoples R China Peking Univ Sch Environm & Energy Shenzhen Grad Sch Shenzhen 518055 Peoples R China RISC V Int Open Source Lab Shenzhen 518055 Peoples R China

Antibiotic, particularly from tetracycline derivatives, poses severe environmental and health challenges due to their stability, bioaccumulation, and resistance to traditional wastewater treatment methods. Advanced oxidation processes, including photocatalysis, offer potential solutions but are hindered by limitations like rapid recombination of photogenerated electron-hole pairs. This study introduces an innovative photoelectrocatalytic system combining BiOIO3 (BIO) with a triboelectric nanogenerator (TENG). BIO, with its unique ferroelectric properties and nanosheet morphology, enhances charge carrier separation and reduces recombination, achieving significantly improved degradation rates compared to conventional photocatalysts. TENG provides a sustainable power source by converting mechanical energy into electrical energy, modulating charge dynamics in BIO through band bending and alignment with its intrinsic electric field. The integration of TENG and BIO creates a synergistic effect, enhancing the generation of reactive species (RSs) and achieving degradation efficiencies 1.59 times higher than photocatalysis alone. The system demonstrates robust, scalable, and environmentally friendly performance, offering a promising solution for efficient antibiotic removal in water treatment applications.

关键词： Photoelectrocatalysis Triboelectric nanogenerator (TENG) Triboelectric-photo coupling effect Electric field modulates Antibiotics

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FireHunter: Toward Proactive and Adaptive Wildfire Suppression via Multi-UAv Collaborative Scheduling

FireHunter: Toward Proactive and Adaptive Wildfire Suppressi...

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IEEE Conference on Computer Communications (IEEE INFOCOM)

作者： Chen, Xuecheng Xiao, Zijian Cheng, Yuhan Hsia, Chen-Chun Wang, Haoyang Dang, Fan Xu, Jingao 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 Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Key Lab Ubiquitous Data Enabling Beijing Peoples R China Tsinghua Univ Global Innovat Exchange Beijing Peoples R China RISC V Int Open Source Lab Beijing Peoples R China Pengcheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350384482;9798350384475

Multi-robot systems are adept at handling complex tasks in large-scale, dynamic, and cold-start scenarios such as wildfire control. This paper introduces FireHunter to tackle the challenge of coordinating fire monitoring and suppression tasks simultaneously in unpredictable environments. FireHunter utilizes a confidence-aware assessment method to identify optimal locations and a priority graph-based algorithm to coordinate robots efficiently. It effectively manages the dynamic planning inclinations for sensing and operational tasks, ensuring real-time information collection and timely environmental intervention. Experimental results from simulation show that FireHunter reduces fire expansion ratio by 59% compared to state-of-the-art solutions.

关键词： Multi-robot System Collaborative Sensing

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QUEST: Quality-informed Multi-agent Dispatching System for Optimal Mobile Crowdsensing

QUEST: Quality-informed Multi-agent Dispatching System for O...

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IEEE Conference on Computer Communications (IEEE INFOCOM)

作者： Li, Zuxin Man, Fanhang Chen, Xuecheng Xu, Susu Dang, Fan Zhang, Xiao-Ping Chen, Xinlei Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Beijing Peoples R China Tsinghua Univ Global Innovat Exchange Beijing Peoples R China Johns Hopkins Univ Dept Civil & Syst Engn Baltimore MD USA Pengcheng Lablatory Shenzhen Peoples R China RISC V Int Open Source Lab Shenzhen Peoples R China

ISBN: (纸本)9798350383515;9798350383508

We address the challenges in achieving optimal Quality of Information (QoI) for non-dedicated vehicular Mobile Crowdsensing (MCS) systems, by utilizing vehicles not originally designed for sensing purposes to provide real-time data while moving around the city. These challenges include the coupled sensing coverage and sensing reliability, as well as the uncertainty and time-varying vehicle status. To tackle these issues, we propose QUEST, a QUality-informed multi-agEnt diSpaTching system, that ensures high sensing coverage and sensing reliability in non-dedicated vehicular MCS. QUEST optimizes QoI by introducing a novel metric called ASQ (aggregated sensing quality), which considers both sensing coverage and sensing reliability jointly. Additionally, we design a mutual-aided truth discovery dispatching method to estimate sensing reliability and improve ASQ under uncertain vehicle statuses. Real-world data from our deployed MCS system in a metropolis is used for evaluation, demonstrating that QUEST achieves up to 26% higher ASQ improvement, leading to a reduction of reconstruction map errors by 32-65% for different reconstruction algorithms.

关键词： internet of Things Mobile sensing and applications Mobile Crowdsensing Data Quality

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SOScheduler: Toward Proactive and Adaptive Wildfire Suppression via Multi-UAv Collaborative Scheduling

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IEEE intERNET OF THINGS JOURNAL 2024年第14期11卷 24858-24871页

作者： Chen, Xuecheng Xiao, Zijian Cheng, Yuhan Hsia, Chen-Chun Wang, Haoyang Xu, Jingao Xu, Susu Dang, Fan Zhang, Xiao-Ping Liu, Yunhao Chen, Xinlei Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen 518000 Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Shenzhen 518000 Peoples R China Tsinghua Univ Sch Software Beijing 100084 Peoples R China Johns Hopkins Univ Civil & Syst Engn Baltimore MD 21218 USA Tsinghua Univ Global Innovat Exchange Beijing 100084 Peoples R China Int Open Source Lab RISC V Shenzhen 518000 Guangdong Peoples R China Pengcheng Lab Shenzhen 518066 Guangdong Peoples R China

Multiunmanned aerial vehicle (UAv) systems have shown immense potential in handling complex tasks in large-scale, dynamic, and cold-start (i.e., limited prior knowledge) scenarios, such as wildfire suppression. Due to the dynamic and stochastic environmental conditions, the scheduling for sensing tasks (i.e., fire monitoring) and operation tasks (i.e., fire suppression) should be executed concurrently to enable real-time information collection and timely intervention of the environment. However, the planning inclinations of sensing and operation tasks are typically inconsistent and evolve over time, complicating the task of identifying the optimal strategy for each UAv. To solve this problem, this article proposes SOScheduler, a collaborative multi-UAv scheduling framework for integrated sensing and operation in large-scale and dynamic wildfire environments. We introduce a spatiotemporal confidence-aware assessment model to dynamically and directly pinpoint locations that can optimally enhance the understanding of environmental dynamics and operational effectiveness, as well as a priority graph-instructed scalable scheduler to coordinate multi-UAv in an efficient manner. Experiments on real multi-UAv testbeds and large-scale physical feature-based simulations show that our SOScheduler reduces the fire expansion ratio by 59% and enhances the fire coverage ratio by 190% compared to state-of-the-art (SOTA) solutions.

关键词： Wildfires Monitoring Dynamic scheduling Task analysis Sensors Autonomous aerial vehicles Wireless sensor networks multirobot systems robot sensing systems

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Theoretical modeling of contact-separation mode triboelectric nanogenerators from initial charge distribution

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ENERGY & ENvIRONMENTAL SCIENCE 2024年第6期17卷 2228-2247页

作者： Zhao, Hongfa Wang, Hao Yu, Hongyong Xu, Qinghao Li, Xiaosa Guo, Jing Shao, Jiajia Wang, Zhong Lin Xu, Minyi Ding, Wenbo Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Dalian Maritime Univ Marine Engn Coll Dalian 116026 Peoples R China Chinese Acad Sci Beijing Inst Nanoenergy & Nanosyst Beijing 100085 Peoples R China Georgia Inst Technol Sch Mat Sci & Engn Atlanta GA 30332 USA Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea RISC V Int Open Source Lab Shenzhen 518055 Peoples R China

Triboelectric nanogenerators (TENGs) have demonstrated remarkable capabilities in energy harvesting and self-powered sensing, while TENG theories have set the foundation for their further development. This study presents a systematic theoretical analysis for contact-separation mode TENGs, including theoretical models, analysis methods and validation approaches. Specifically, three infinite plate models are proposed to cover typical forms of contact-separation mode TENGs. According to the physics of charge conservation and electrostatic equilibrium, the distribution of initial charges on tribo-charge layers, and redistribution of all charges on the TENG are determined, based on which the electric field, charge transfer process, conduction current in the external circuit and displacement current inside TENGs are mathematically deduced. TENGs' characteristics and figure of merit can be directly obtained from the proposed theory, which are consistent with phenomena in practice. This validates the theoretical derivations, and better explains TENG performances in practice. Furthermore, applicability is discussed by comparing the finite plate models and infinite plate models, and more accurate schematic diagrams depicting the working principle of TENGs are created. Additionally, an electrodynamic model with a solving approach is proposed that can be applied for TENGs of any size. Extended analysis for different applications is performed, demonstrating the practicability of the proposed theory for TENGs. This work may provide a solid and practical theoretical foundation for TENG development. A systematic theoretical analysis for contact-separation mode TENGs is presented, including theoretical models, analysis methods, validation approaches, and extended analysis for applications.

关键词： Nanogenerators

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visual-Tactile Fusion for Transparent Object Grasping in Complex Backgrounds

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IEEE TRANSACTIONS ON ROBOTICS 2023年第5期39卷 3838-3856页

作者： Li, Shoujie Yu, Haixin Ding, Wenbo Liu, Houde Ye, Linqi Xia, Chongkun Wang, Xueqian Zhang, Xiao-Ping Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Tsinghua Berkeley Shenzhen Inst RISC V Int Open Source Lab Shenzhen 518055 Peoples R China Shanghai Univ Inst Artificial Intelligence Collaborat Innovat Ctr Marine Artificial Intellige Shanghai 200444 Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON M5B 2K3 Canada

The grasping of transparent objects is challenging but of significance to robots. In this article, a visual-tactile fusion framework for transparent object grasping in complex backgrounds is proposed, which synergizes the advantages of vision and touch, and greatly improves the grasping efficiency of transparent objects. First, we propose a multiscene synthetic grasping dataset named SimTrans12 K together with a Gaussian-mask annotation method. Next, based on the TaTa gripper, we propose a grasping network named transparent object-grasping convolutional neural network for grasping position detection, which shows good performance in both synthetic and real scenes. Inspired by human grasping, a tactile calibration method and a visual-tactile fusion classification method are designed, which improve the grasping success rate by 36.7% compared with direct grasping and the classification accuracy by 39.1%. Furthermore, the tactile height sensing module and the tactile position exploration module are added to solve the problem of grasping transparent objects in irregular and visually undetectable scenes. The experimental results demonstrate the validity of the framework.

关键词： Complex backgrounds synthetic transparent object dataset tactile calibration transparent object grasping visual-tactile fusion

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CaliFormer: Leveraging Unlabeled Measurements to Calibrate Sensors with Self-supervised Learning

CaliFormer: Leveraging Unlabeled Measurements to Calibrate S...

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

作者： Wang, Haoyang Liu, Yuxuan Zhao, Chenyu He, Jiayou Ding, Wenbo Chen, Xinlei Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen Peoples R China Hong Kong Univ Sci & Technol Sch Engn Hong Kong Peoples R China RISC V Int Open Source Lab Pengcheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798400702006

Accurate calibration of low-cost sensors is critical for improving their potential in environmental monitoring. State-of-the-art (SOTA) methods based on supervised learning commonly calibrate sensor measurements with ground truth from the immediate past or future. However, these techniques rely heavily on labeled data which is challenging to obtain in real-world scenarios. Thus, this paper introduces CaliFormer, a novel representation learning model using self-supervised learning to extract time- and spatial-invariant knowledge from unlabeled measurements. Moreover, we propose pre-training enhancements and model architecture modifications to help train CaliFormer. We then fine-tune the calibration model with the learned representations, which is supervised by limited labeled data. Finally, we comprehensively evaluate our calibration model with a dataset collected by low-cost sensors. Results show that our model outperforms other SOTA calibration methods significantly.

关键词： Air Pollution Sensor calibration Self-supervised learning

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Estimating and modeling spontaneous mobility changes during the COvID-19 pandemic without stay-at-home orders

HUMANITIES & SOCIAL SCIENCES COMMUNICATIONS

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HUMANITIES & SOCIAL SCIENCES COMMUNICATIONS 2024年第1期11卷 1-15页

作者： Zhao, Baining Wang, Xuzhe Zhang, Tianyu Shi, Rongye Xu, Fengli Man, Fanhang Chen, Erbing Li, Yang Li, Yong Sun, Tao Chen, Xinlei Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Beihang Univ Inst Artificial Intelligence Beijing Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China Shenzhen Univ Dept Math Shenzhen Peoples R China RISC V Int Open Source Lab Shenzhen Peoples R China

Comprehending the complex interplay among urban mobility, human behavior, and the COvID-19 pandemic could deliver vital perspectives to steer forthcoming public health endeavors. In late 2022, China lifted its "Zero-COvID" policy and rapidly abandoned nearly all interventions. It provides a unique opportunity to observe spontaneous mobility changes without government restriction throughout such a pandemic with high infection. Based on 148 million travel data from the public bus, subway, and taxi systems in Shenzhen, China, our analysis reveals discernible spatial discrepancies within mobility patterns. This phenomenon can be ascribed to the heterogeneous responses of mobility behavior tailored to specific purposes and travel modes in reaction to the pandemic. Considering both the physiological effects of virus infection and subjective willingness to travel, a dynamic model is proposed and capable of fitting fine-grained urban mobility. The analysis and model can interpret mobility data and underlying population behavior to inform policymakers when evaluating public health strategies against future large-scale infectious diseases.

关键词：

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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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