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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 Beijing Institute of Technology Chongqing Innovation Center 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. © 2016 IEEE.

关键词： Reinforcement learning

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Autonomous helicopter aerial refueling: controller design and performance guarantees

arXiv

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arXiv 2025年

作者： Jayarathne, Damsara Paternain, Santiago Mishra, Sandipan Department of Mechanical Aerospace and Nuclear Engineering United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States

In this paper, we present a control design methodology, stability criteria and performance bounds for autonomous helicopter aerial refueling. Autonomous aerial refueling is particularly difficult due to the aerodynamic interaction between the wake of the tanker, the contact-sensitive nature of the maneuver, and the uncertainty in drogue motion. Since the probe tip is located significantly away from the helicopter’s center-of-gravity, its position (and velocity) is strongly sensitive to the helicopter’s attitude (and angular rates). In addition, the fact that the helicopter is operating at high speeds to match the velocity of the tanker forces it to maintain a particular orientation, making the docking maneuver especially challenging. In this paper, we propose a novel outer-loop position controller that incorporates the probe position and velocity into the feedback loop. The position and velocity of the probe tip depend both on the position (velocity) and on the attitude (angular rates) of the aircraft. We derive analytical guarantees for docking performance in terms of the uncertainty of the drogue motion and the angular acceleration of the helicopter, using the ultimate boundedness property of the closed-loop error dynamics. Simulations are performed on a high-fidelity UH60 helicopter model with a high-fidelity drogue motion under wind effects to validate the proposed approach for realistic refueling scenarios. These high-fidelity simulations reveal that the proposed control methodology yields an improvement of 36% in the 2-norm docking error compared to the existing standard controller. © 2025, CC BY.

关键词： Helicopters

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Automated Parts Segmentation of Sperm via a Contrastive Learning-Based Part Matching Network

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IEEE Transactions on Automation Science and engineering 2025年 22卷 15314-15325页

作者： Chen, Wenyuan Song, Haocong Shan, Guanqiao Dai, Changsheng Liu, Hang Jiang, Aojun Sun, Chen Ru, Changhai Librach, Clifford Zhang, Zhuoran Sun, Yu University of Toronto Department of Computer Science TorontoONM5S 2E4 Canada University of Toronto Department of Mechanical and Industrial Engineering TorontoONM5S 3G8 Canada Dalian University of Technology Institute of Robotics and Intelligent Systems Liaoning Dalian China Suzhou University of Science and Technology School of Electronic and Information Engineering Suzhou215009 China CReATe Fertility Centre TorontoONM5G 1N8 Canada

Sperm morphology measurement is vital for diagnosing male infertility, which involves quantification of multiple subcellular parts for each sperm. Instance-aware part segmentation networks have been introduced to address this task by automatically identifying individual sperm and segmenting their subcellular parts. However, major limitations of state-of-the-art instance-aware part segmentation networks include: 1) they are time-consuming and computational expensive due to sequential processing and multi-stage frameworks;2) they perform poorly for densely packed sperm that overlap or cross over one another. To overcome these challenges, this paper proposes 1) integrating instance identification and subcellular part segmentation within a single-stage framework to save inference time and memory usage;2) dividing a sperm target into simpler components (head and tail) to improve prediction accuracy, followed by a contrastive learning-based matching method to pair the head and tail. Experimental results on our clinically collected human sperm dataset demonstrated that the proposed network not only outperformed state-of-the-art CP-Net (by 3.5% APpvol) but also achieved realtime inference (48.0 frames per second), effectively meeting the clinical requirements for automated parts segmentation of sperm. © 2004-2012 IEEE.

关键词： Image segmentation

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Improved Feed-Forward Control of Active Power Decoupler for PV Microinverter With Non-Sinusoidal Power Decoupling Capability

引用

IEEE Transactions on Power Electronics 2025年

作者： Shen, Yidi Hasnain, Arafat Zakzewski, Daniel Resalayyan, Rakesh Khaligh, Alireza University of Maryland Department of Electrical and Computer Engineering Maryland Power Electronics Laboratory Institute for Systems Research College ParkMD20742 United States

In single-phase power conversion systems, there is an inherent difference between the dc-side constant and ac-side oscillating power, and power decoupling is required especially in applications like photovoltaic (PV) inverters. Active power decoupling (APD) converters are often used to balance instantaneous power while reducing the size of dc link capacitors and improving the system reliability. Conventionally, APD converters are controlled to decouple only sinusoidal power at double-line frequency. However, the control is further complicated when non-sinusoidal power is delivered due to non-linear circuit components, non-linear loads, or grid distortions. This paper proposes an improved feed-forward APD control scheme with the capability of decoupling non-sinusoidal power, which has application in single-phase systems, particularly all types of PV microinverters. The proposed feed-forward control includes the capacitor voltage calculation under arbitrary power harmonics and a dead-time compensation to increase the accuracy of duty prediction. The proposed algorithm can work as open-loop control to eliminate APD sensors and can serve as a feed-forward term in closed-loop control to improve power decoupling performance, thereby simplifying the controller design. The proposed approach is experimentally verified using a dc-parallel APD prototype, a 50 V 250 W microinverter with non-linear ac capacitor, and a diode rectifier load. © 1986-2012 IEEE.

关键词： Closed loop control systems

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Impact of diameter on D-shaped POF sensors for salt detection

Impact of diameter on D-shaped POF sensors for salt detectio...

引用

International Laser Technology and Optics Symposium 2024, iLATOS2024

作者： Khan, Abdul Ali Thirunavakkarasu, Punithavathi M. bte Saidin, Norazlina Noor, Ahmad Shukri Muhammad Waqas, Nawaf Communication Technology Section Universiti Kuala Lumpur British Malaysian Institute Kuala lumpur53100 Malaysia Faculty of I.C.T BUITEMS Quetta87100 Pakistan Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur53100 Malaysia Department of Computer and Communication Systems Engineering Faculty of Engineering Universiti Putra Malaysia Selangor Malaysia Department of Instrumentation and Control Engineering Universiti Kuala Lumpur British Malaysian Institute Bt. 8 Jalan Sungai Pusu Selangor Gombak53100 Malaysia

This study investigates the relationship between the diameter of a D-shaped plastic optical fiber (POF) sensor and its optical response as a function of varying salt concentrations. A simple and efficient dry etching polishing technique was employed to create D-shaped POF sensors with a range of diameters. All the fabricated sensors were carefully developed to maintain a consistent length of 20 cm with a sensing region of 1cm. To evaluate the optical intensity response, each sensor, with its respective diameter, was examined with the salt concentration ranging from (0 to 5%) examination utilizing a spectrometer. The sensor response was characterized by quantifying the variations observed in the intensity of the optical signal propagating through the sensing region. The sensors having a diameter of 500 μm, 700 μm, and 900 μm had a sensitivity of 149 A.U/%salt, 66 A.U/%salt, and 11.2 A.U/%salt respectively. The sensitivity of the 500 μm diameter sensor increased 13 times approximately as compared to the sensor having diameter of 900 μm. © 2025 institute of Physics Publishing. All rights reserved.

关键词： Dry etching

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Thermally robust compact single Bragg grating silicon modulator for photonic computing

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Applied Optics 2025年第16期64卷 4646-4653页

作者： Meng Zhang Nicholas Gangi Amir Begović Alexer Chen Bowen Liu Z Rena Huang Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy New York 12180 USA Department of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute Troy New York 12180 USA

We report, to our knowledge, a novel compact, thermally operated optical modulator with 6-bit resolution. The device comprises a slow-light photonic structure, a Bragg grating with a footprint of $10\,\,{\unicode{x00B5}{\rm m}}$ by $50\,\,{\unicode{x00B5}{\rm m}}$ . This single Bragg-grating modulator (SBG-M) operates based on thermally induced band edge shift of the photonic stop band and demonstrates almost constant normalized mean square error (NMSE) lower than 0.001 from room temperature to ${40^\circ}{\rm C}$ , while maintaining thermal robustness up to ${65^\circ}{\rm C}$ limited by the Johnson noise of the on-chip photodetector. The clock frequency, computing accuracy, power consumption, thermal stability, and fabrication-induced spectrum non-ideality are thoroughly studied. The SBG-M achieves a measured lowest NMSE of $5.0 \times {10^{- 4}}$ , with an average power consumption of 5.29 mW. Compared to other optical devices such as micro-ring resonators and slow-light incorporated Mach–Zehnder modulators, the reported device demonstrates significant advantages in compactness, lower power consumption, and thermal robustness.

关键词： Optical computing Optical modulators Phase shift Quantum computation Silicon modulators Silicon photonics

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A Unified Framework for Robots that Influence Humans over Long-Term Interaction

arXiv

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arXiv 2025年

作者： Sagheb, Shahabedin Parekh, Sagar Pandya, Ravi Mun, Ye-Ji Driggs-Campbell, Katherine Bajcsy, Andrea Losey, Dylan P. Virginia Tech Department of Mechanical Engineering United States Carnegie Melon University Robotics Institute United States University of Illinois Urbana-Champaign Department of Electrical and Computer Engineering United States

Robot actions influence the decisions of nearby humans. Here influence refers to intentional change: robots influence humans when they shift the human’s behavior in a way that helps the robot complete its task. Imagine an autonomous car trying to merge;by proactively nudging into the human’s lane, the robot causes human drivers to yield and provide space. Influence is often necessary for seamless interaction. However, if influence is left unregulated and uncontrolled, robots will negatively impact the humans around them (e.g., autonomous cars that repeatedly merge in front of humans may cause human drivers to become more aggressive). Prior works have begun to address this problem by creating a variety of control algorithms that seek to influence humans. Although these methods are effective in the short-term, they fail to maintain influence over time as the human adapts to the robot’s behaviors. In this paper we therefore present an optimization framework that enables robots to purposely regulate their influence over humans across both short-term and long-term interactions. Here the robot maintains its influence by reasoning over a dynamic human model which captures how the robot’s current choices will impact the human’s future behavior. Our resulting framework serves to unify current approaches: we demonstrate that state-of-the-art methods are simplifications of our underlying formalism. Our framework also provides a principled way to generate influential policies: in the best case the robot exactly solves our framework to find optimal, influential behavior. But when solving this optimization problem becomes impractical, designers can introduce their own simplifications to reach tractable approximations. We experimentally compare our unified framework to state-of-the-art baselines and ablations, and demonstrate across simulations and user studies that this framework is able to successfully influence humans over repeated interactions. See videos of our experiments he

关键词： Human robot interaction

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From Vague Instructions to Task Plans: A Feedback-Driven HRC Task Planning Framework based on LLMs

arXiv

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arXiv 2025年

作者： Shervedani, Afagh Mehri Walter, Matthew R. Žefran, Miloš Robotics Laboratory Department of Electrical and Computer Engineering University of Illinois Chicago ChicagoIL60607 United States Robotics Laboratory Toyota Technological Institute at Chicago ChicagoIL60637 United States

Recent advances in large language models (LLMs) have demonstrated their potential as planners in human-robot collaboration (HRC) scenarios, offering a promising alternative to traditional planning methods. LLMs, which can generate structured plans by reasoning over natural language inputs, have the ability to generalize across diverse tasks and adapt to human instructions. This paper investigates the potential of LLMs to facilitate planning in the context of human-robot collaborative tasks, with a focus on their ability to reason from high-level, vague human inputs, and fine-tune plans based on real-time feedback. We propose a novel hybrid framework that combines LLMs with human feedback to create dynamic, context-aware task plans. Our work also highlights how a single, concise prompt can be used for a wide range of tasks and environments, overcoming the limitations of long, detailed structured prompts typically used in prior studies. By integrating user preferences into the planning loop, we ensure that the generated plans are not only effective but aligned with human intentions. Copyright © 2025, The Authors. All rights reserved.

关键词： Robot programming

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Systematic exploration of fuzzing in IoT: techniques, vulnerabilities, and open challenges

引用

The Journal of Supercomputing 2025年第8期81卷 1-46页

作者： Touqir, Asma Iradat, Faisal Iqbal, Waseem Rakib, Abdur Taskin, Nazim Jadidbonab, Hesam Haas, Olivier Department of Computer Science School of Mathematics and Computer Science Institute of Business Administration Karachi Pakistan Department of Electrical and Computer Engineering Sultan Qaboos University Muscat Oman Institute for Future Transport and Cities Coventry University Coventry UK Department of Management Information Systems Bogazici University Istanbul Turkey

As our dependence on the internet and digital platforms grows, the risk of cyber threats rises, making it essential to implement effective measures to safeguard sensitive information through cybersecurity, ensure system integrity, and prevent unauthorized data access. Fuzz testing, commonly known as fuzzing, is a valuable technique for software testing as it uncovers vulnerabilities and defects in systems by introducing random data inputs, often leading to system crashes. In the Internet of Things (IoT) domain, fuzzing is crucial for identifying vulnerabilities in networks, devices, and applications through automated tools that systematically inject malformed inputs into IoT systems. However, despite its importance, existing research on fuzzing techniques in IoT contexts remains limited by the absence of standardized benchmarks, inefficiencies in re-hosting strategies, and difficulties in detecting complex, condition-dependent vulnerabilities. The primary objective of this study is to comprehensively evaluate current fuzzing practices, emphasizing adaptive techniques designed for IoT systems. Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) model, a systematic literature review was conducted across 32 academic articles published between 2020 and 2024. The analysis revealed that although fuzzing enhances IoT security, its effectiveness is hindered by device heterogeneity, limited system resources, and evolving cyber threat landscapes. The findings suggest that to overcome these limitations, future research should focus on AI-driven fuzzing methods, robust multi-architecture support, and the development of standardized evaluation frameworks to strengthen IoT cybersecurity.

关键词： Model checking

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Identification of Medicinal Plants and Related Fungal Diseases Based on Deep Learning Model

Identification of Medicinal Plants and Related Fungal Diseas...

引用

Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Mathina Kani M Mahalakshmi R Shunmugavel V Abdul Kareem M Department of Computer Science and Engineering Sethu Institute of Technology Virudhunagar India Department of Computer Science and Business Systems Sethu Institute of Technology Virudhunagar India Department of Electrical and Electronics Engineering Quantanics TechServ pvt Ltd Madurai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

In today's technological landscape, manual methods are increasingly being replaced by automated computer systems, streamlining tasks and enhancing efficiency. Within the realm of agricultural research, two critical areas plant identification and disease classification have shifted toward computerized solutions. With millions of plant species globally, medicinal plants hold particular significance due to their healing properties in traditional medicine. These readily accessible and culturally important remedies not only contribute to affordable healthcare but also sustain livelihoods in indigenous and rural communities. To address these challenges, our study evaluates the performance of two widely used pre-trained deep learning models U-Net and F-Net. By comparing their accuracy levels, we aim to enhance disease detection. Additionally, we present performance metrics such as accuracy and error rates, visualized through graphs. Our user interface (UI) leverages Kivy, an open-source multi-platform GUI development library for Python. Kivy enables cross-device app development, emphasizing code reusability and rapid prototyping, making it a valuable tool for interaction design across iOS, Android, Windows, OS X, and GNU/Linux platforms.

关键词： Medicinal plants Deep learning Measurement Visualization Accuracy Medical services Manuals Rapid prototyping Mobile computing Diseases

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