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Multi-Vehicle Cooperative Persistent Coverage for Random Target Search

IEEE robotics and Automation Letters 2025年第7期10卷 6680-6687页

作者： Li, Zhuo Li, Guangzheng Sadeghi, Alireza Sun, Jian Wang, Gang Wang, Jialin Beijing Institute of Technology National Key Lab of Autonomous Intelligent Unmanned Systems School of Automation Beijing100084 China Chongqing Innovation Center Beijing Institute of Technology Chongqing401120 China University of Minnesota Department of Electrical and Computer Engineering MinneapolisMN55455 United States Intelligent Game and Decision Laboratory China Academy of Launch Vehicle Technology Beijing100071 China

This letter investigates the target search problem for a network of autonomous vehicles, aiming to maximize the detection of randomly appearing targets within a given area. Considering no prior knowledge of the targets is available, we propose a multi-vehicle cooperative persistent coverage scheme under the framework of multi-agent reinforcement learning, in contrast to heuristic and model-based optimization methods in existing works. We model the persistent coverage problem as a partially observable Markov decision process (POMDP) due to the vehicles' limited observation ranges, and introduce a knowability map to characterize their knowledge of the target area. Each vehicle employs a distributed estimator, leveraging its own observations and shared information from neighboring vehicles, to construct a globally estimated knowability map - thereby mitigating partial observability. The persistent coverage policies are learned with the architecture of centralized training and distributed execution, enabling cooperative and efficient target search by fully exploiting shared information. Moreover, we propose an adaptive partition method for the target area to ensure a fixed dimension of the state space in the POMDP, which can improve scalability of the learned policy to target areas with various sizes. Simulations validate effectiveness and scalability of the proposed cooperative scheme. © 2025 IEEE.

关键词： Reinforcement learning

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Statistical Characterization of RIS-Assisted UAV Communications in Terrestrial and Non-Terrestrial Networks Under Channel Aging

Statistical Characterization of RIS-Assisted UAV Communicati...

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IEEE International Conference on Communications (ICC)

作者： Thanh Luan Nguyen Georges Kaddoum Tri Nhu Do Zygmunt J. Haas Department of Electrical Engineering École de Technologie Supérieure (ETS) Montréal QC Canada Artificial Intelligence & Cyber Systems Research Center Lebanese American University Electrical Engineering Department Polytechnique Montréal Montréal QC Canada Department of Computer Science University of Texas at Dallas TX USA School of Electrical and Computer Engineering Cornell University Ithaca NY USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

This paper studies the statistical characterization of ground-to-air (G2A) and reconfigurable intelligent surface (RIS)-assisted air-to-ground (A2G) communications with unmanned aerial vehicles (UAVs) in terrestrial and non-terrestrial networks under the impact of channel aging. We first model the G2A and A2G signal-to-noise ratios (SNRs) as non-central complex Gaussian quadratic random variables (RVs) and derive their exact probability density functions, offering a unique characterization for the A2G SNR as the product of two scaled non-central chi-square RVs. Moreover, we also find that, for a large number of RIS elements, the RIS-assisted A2G channel can be characterized as a single Rician fading channel. Our results reveal the presence of channel hardening in A2G communication under low UAV speeds, where we derive the maximum target spectral efficiency (SE) for a system to maintain a consistent required outage level. Meanwhile, high UAV speeds, exceeding 50 m/s, lead to a significant performance degradation, which cannot be mitigated by increasing the number of RIS elements.

关键词： Spectral efficiency Rician channels Reconfigurable intelligent surfaces Aging Probability density function Autonomous aerial vehicles Air to ground communication

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Adhesive bioelectronics for closed-loop therapy

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Med-X 2025年第1期3卷 1-32页

作者： Jung, Hyunjin Lee, Hyelim Shin, Mikyung Son, Donghee Department of Electrical and Computer Engineering Sungkyunkwan University (SKKU) Suwon Republic of Korea Center for Neuroscience Imaging Research Institute for Basic Science Suwon Republic of Korea Department of Intelligent Precision Healthcare Convergence Sungkyunkwan University (SKKU) Suwon Republic of Korea Department of Biomedical Engineering Sungkyunkwan University (SKKU) Suwon Republic of Korea Department of Artificial Intelligence System Engineering Sungkyunkwan University (SKKU) Suwon Republic of Korea

Wearable and implantable devices mark a major advancement in biomedical engineering, enabling continuous bio-signal monitoring and treatment based on precise feedback. However, traditional rigid bioelectronic devices face challenges in seamlessly interfacing with soft organs, often leading to tissue damage and inflammation. To address these issues, soft bioelectronics that better conform to the body while maintaining performance accuracy have been developed. Despite these advancements, the dynamic nature of body movements and the complex chemical environment in the body can compromise the stability of devices over time. Therefore, recent innovations have focused on ultrathin and stretchable designs that enhance conformal contact through van der Waals forces, as well as the incorporation of adhesive hydrogels, to improve adhesion, even in the presence of biofluids. This review provides a comprehensive overview of the advancements in wearable and implantable bioelectronics aimed at achieving conformal tissue adhesion and long-term stability, covering structural modifications to bilayer designs. Finally, the integration of bioelectronics with closed-loop therapies to significantly enhance treatment efficacy is explored. • Materials strategies and device fabrication/integration processes optimally tailored to various organs are addressed. • Mechanical and electrical characterizations of wearable and implantable adhesive bioelectronics are described, while emphasizing challenges and opportunities in long-term stable tissue interfacing. • Various demonstrations of closed-loop bio-integrated systems using soft stretchable adhesive materials, and their futuristic perspectives are introduced.

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Continuity Scaling: A Rigorous Framework for Detecting and Quantifying Causality Accurately

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research 2022年第4期2022卷 137-146页

作者： Xiong Ying Si-Yang Leng Huan-Fei MaD Qing Nie Ying-Cheng Lai Wei Lin School of Mathematical Sciences SCMSand SCAMFudan UniversityShanghai 200433China Research Institute for Inteligent Complex Systems CCSBand LCNBIFudan UniversityShanghai 200433China State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Institutes of Brain ScienceFudan UniversityShanghai 200032China Institute of AI and Robotics Academy for Engineering and TechnologyFudan UniversityShanghai 200433China School of Mathematical Sciences Soochow UniversitySuzhou 215006China Department of Mathematics Department of Developmental and Cll BiologyAnd NSF-Simons Center for Multiscale Cell Fate ResearchUniversity of CaliforniaIrvineCA 92697-3875USA School of Electrical ComputerAnd Energy EngineeringArizona State UniversityTempeArizona 85287-5706USA Shanghai Artificial Intelligence Laboratory Shanghai 200232China

Data-based detection and quantification of causation in complex,nonlinear dynamical systems is of paramount importance to science,engineering,and *** by the widely used methodology in recent years,the cross-map-based techniques,we develop a general framework to advance towards a comprehensive understanding of dynamical causal mechanisms,which is consistent with the natural interpretation of *** particular,instead of measuring the smoothness of the cross-map as conventionally implemented,we define causation through measuring the scaling law for the continuity of the investigated dynamical system *** uncovered scaling law enables accurate,reliable,and eficient detection of causation and assessment of its strength in general complex dynamical systems,outperforming those existing representative *** continuity scaling-based framework is rigorously established and demonstrated using datasets from model complex systems and the real world.

关键词： dynamical scaling continuity

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Advances in artificial joint bio-friction and wear simulation assessment technology

Advances in artificial joint bio-friction and wear simulatio...

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IEEE International Conference on robotics and Biomimetics

作者： Yanyan Zhang Yiming Liang Shiqiang Zhu Menglong Ding Yunhe Bai Haoyuan Du Jason Gu Tiefeng Li Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada Center for X-mechanics Zhejiang University Hangzhou China Department of Engineering Mechanics Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Zhejiang University Hangzhou China

ISBN: (纸本)9781665481106

Natural joints have excellent low-friction properties that positively inspire the frictional wear properties of artificial joints. Replacing damaged biological joints with artificial ones can restore bio-function and reduce the pain of arthritic patients. Improvements in the frictional wear properties of artificial joints will effectively increase the life of the prosthesis, especially for young arthritic patients. This paper reviews the lubrication mechanism of natural joints before systematically introducing the development of lubrication and wear simulation techniques for artificial joints, offering references and insights for further studies of artificial joints and the optimization of tribological properties of commercial artificial joint prostheses.

关键词： Pain Friction Biological system modeling Lubrication Surface texture Optimization Surface treatment

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Conceptual Design and Simulation of a BCF Swimming Soft Robotic Fish for Deep-Sea*

Conceptual Design and Simulation of a BCF Swimming Soft Robo...

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IEEE International Conference on robotics and Biomimetics

作者： Yanyan Zhang Menglong Ding Shiqiang Zhu Yiming Liang Yunhe Bai Keren Xie Minhong Wan Jason Gu Tiefeng Li Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada Center for X-mechanics Zhejiang University Hangzhou China Department of Engineering Mechanics Zhejiang University Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Hangzhou China

ISBN: (纸本)9781665481106

This work provides a design of a deep-sea soft robotic fish propelled by the body and/or caudal fin (BCF) mode. The robotic fish consists of a bionic fish head and a BCF propulsion body. To enhance the swimming performance of the first soft robot fish in the deep sea, the design combines the advantages of high efficiency in BCF propulsion with the large deformation angle of dielectric elastomers (DE) flexible joints. The connection of the bionic fish head and BCF is cast in one piece of silicone to achieve deep-sea pressure self-adaptation of the soft robotic fish. The swimming performance was simulated using the CFD method, and the BCF propulsion behaviors were analyzed to evaluate the design proposed. The design goal was verified through numerical calculation, and a further enhanced strategy for the thrust ability of the fish was presented as well. This work can provide a low-cost bionic soft robot fish solution for deep-sea exploration.

关键词： Biological system modeling Kinematics Soft robotics Propulsion Fish Dielectric elastomers Numerical models

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Detecting and Localizing Objects on an Unmanned Aerial System (UAS) Integrated with a Mobile Device

Detecting and Localizing Objects on an Unmanned Aerial Syste...

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2021 International Conference on Unmanned Aircraft systems, ICUAS 2021

作者： Kim, Jinho Lin, Kevin Nogar, Stephen M. Larkin, Dominic Korpela, Christopher M. Robotics Research Center United States Military Academy Department of Electrical Engineering and Computer Science West PointNY10996 United States MD21005 United States

ISBN: (纸本)9780738131153

There is a strong need for Unmanned Aerial systems (UAS) that can navigate autonomously using visual information for GPS denied and other degraded environments. In this paper, a system architecture is proposed that utilizes visual-inertial odometry (VIO) and object recognition and localization for a quadrotor UAS. It also incorporates communication with an Android Team Awareness Kit (ATAK) device, allowing users to share situational awareness of the UAS in real-time. The system architecture is validated through experiments in which a human is detected and localized as the object of interest. © 2021 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Generalized Correspondence Matching via Flexible Hierarchical Refinement and Patch Descriptor Distillation

arXiv

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arXiv 2024年

作者： Han, Yu Long, Ziwei Zhang, Yanting Wu, Jin Fang, Zhijun Fan, Rui The School of Computer Science and Technology Donghua University Shanghai201620 China The Department of Electronics and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong SAR Hong Kong Hong Kong Group The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China

Correspondence matching plays a crucial role in numerous robotics applications. In comparison to conventional hand-crafted methods and recent data-driven approaches, there is significant interest in plug-and-play algorithms that make full use of pre-trained backbone networks for multi-scale feature extraction and leverage hierarchical refinement strategies to generate matched correspondences. The primary focus of this paper is to address the limitations of deep feature matching (DFM), a state-of-the-art (SoTA) plug-and-play correspondence matching approach. First, we eliminate the pre-defined threshold employed in the hierarchical refinement process of DFM by leveraging a more flexible nearest neighbor search strategy, thereby preventing the exclusion of repetitive yet valid matches during the early stages. Our second technical contribution is the integration of a patch descriptor, which extends the applicability of DFM to accommodate a wide range of backbone networks pre-trained across diverse computer vision tasks, including image classification, semantic segmentation, and stereo matching. Taking into account the practical applicability of our method in real-world robotics applications, we also propose a novel patch descriptor distillation strategy to further reduce the computational complexity of correspondence matching. Extensive experiments conducted on three public datasets demonstrate the superior performance of our proposed method. Specifically, it achieves an overall performance in terms of mean matching accuracy of 0.68, 0.92, and 0.95 with respect to the tolerances of 1, 3, and 5 pixels, respectively, on the HPatches dataset, outperforming all other SoTA algorithms. Our source code, demo video, and supplement are publicly available at ***/GCM. © 2024, CC BY.

关键词： Semantics

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Study of Helicopter Optimal Autorotation Landing Procedure in Tail Rotor Drive Failure

Study of Helicopter Optimal Autorotation Landing Procedure i...

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IEEE International Conference on robotics and Biomimetics

作者： Xufei Yan Renliang Chen Shiqiang Zhu Anhuan Xie Jason Gu Research Center for Intelligent Robotics Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China National Key Laboratory of Science and Technology on Rotorcraft Aeromechanics College of Aeronautics Nanjing University of Aeronautics and Astronautics Nanjing Jiangsu China Intelligent Robot Research Center Zhejiang Lab Hangzhou Zhejiang China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

ISBN: (纸本)9781665481106

This paper investigates the optimal landing trajectory and control procedure when a helicopter undergoes autorotation due to tail rotor drive failure (TRDF), in which an optimal control methodology is proposed. First, a helicopter flight dynamics model with TRDF was developed. Then, the autorotation in TRDF was converted to be a nonlinear optimal control problem, solved by direct node collocation method and sequential quadratic programming algorithm. Finally, a model helicopter (Z11) with single main rotor and tail rotor was used to demonstrate the proposed approach. An optimal autorotation landing procedure in TRDF was determined accordingly. Results indicate that the airframe will immediately respond to the excess torque generated by the main rotor via yawing, sideslip and rolling when TRDF occurs. The pilot is recommended to shut down the engine and perform a series of critical operations to stabilize the violent yaw and roll movements. In addition, flight test data were used to validate the numerical simulations of autorotation landing. The proposed optimal control approach provides a useful tool to investigate helicopter TRDF autorotation landing procedure.

关键词： Torque Biological system modeling Helicopters Rotors Optimal control Tail Numerical simulation

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Learning the continuous-time optimal decision law from discrete-time rewards

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National Science Open 2024年第5期3卷 130-147页

作者： Ci Chen Lihua Xie Kan Xie Frank Leroy Lewis Yilu Liu Shengli Xie School of Automation Guangdong University of TechnologyGuangdong Key Laboratory of IoT Information TechnologyGuangzhou 510006China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou 510006China UTA Research Institute the University of Texas at ArlingtonFort Worth 76118USA Department of Electrical Engineering and Computer Science University of TennesseeKnoxville 37996USA Oak Ridge National Laboratory Oak Ridge 37830USA Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou 510006China

The concept of reward is fundamental in reinforcement learning with a wide range of applications in natural and social *** an interpretable reward for decision-making that largely shapes the system's behavior has always been a challenge in reinforcement *** this work,we explore a discrete-time reward for reinforcement learning in continuous time and action spaces that represent many phenomena captured by applying physical *** find that the discrete-time reward leads to the extraction of the unique continuous-time decision law and improved computational efficiency by dropping the integrator operator that appears in classical results with integral *** apply this finding to solve output-feedback design problems in power *** results reveal that our approach removes an intermediate stage of identifying dynamical *** work suggests that the discrete-time reward is efficient in search of the desired decision law,which provides a computational tool to understand and modify the behavior of large-scale engineering systems using the optimal learned decision.

关键词： continuous-time state and action decision law learning discrete-time reward dynamical systems reinforcement learning

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