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Multi-agent Path Planning Based on Conflict-Based Search (CBS) Variations for Heterogeneous Robots

Journal of Intelligent and Robotic Systems: Theory and Applications 2025年第1期111卷 1-12页

作者： Bai, Yifan Kotpalliwar, Shruti Kanellakis, Christoforos Nikolakopoulos, George Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå797187 Sweden

This article introduces a novel Multi-agent path planning scheme based on Conflict Based Search (CBS) for heterogeneous holonomic and non-holonomic agents, designated as Heterogeneous CBS (HCBS). The proposed methodology employs a hybrid A∗ algorithm for non-holonomic car-like robots and a conventional A∗ algorithm for holonomic robots. Following this, a body conflict detection strategy is utilized to construct the conflict tree, bridging the initial path planning with the resolution of conflicts among agents. Moreover, we present two variants of HCBS: the Enhanced Conflict-Based Search (EHCBS) and the Depth-First Conflict-Based Search (DFHCBS). We evaluate the efficacy of our proposed algorithms—HCBS, EHCBS, and DFHCBS—against a standard prioritized planning algorithm, focusing on success rates and computational efficiency in environments with varying numbers of agents and obstacles. The empirical results demonstrate that EHCBS exhibits superior computational efficiency in small, dense environments, while DFHCBS performs well in larger-scale environments. This highlights the adaptability of our proposed approaches in various settings, proving the computational advantage of EHCBS and DFHCBS over traditional methods. © The Author(s) 2025.

关键词： Multi agent systems

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Multi capacitor modeling for triboelectric nanogenerators with multiple effective parameters

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Energy Harvesting and Systems 2023年第2期10卷 325-330页

作者： Keykha, Mohsen Hosseni, Mohsen Department of Electrical and Computer Engineering University of Sistan and Baluchestan Zahedan Iran Department of Electrical Engineering University of Tehran Tehran Iran

Considering the ever-increasing human need for energy resources and on the other hand, the reduction of fossil fuel resources, the use of renewable energy has become one of the attractive topics of researchers. One of the available and ambient sources in nature is mechanical energy in nature, such as the energy of body movements, wind energy, energy in sea waves, etc. One of the methods of converting and extracting energy from the mentioned forms is the use of triboelectric nano generators. Triboelectric nanogenerators have various structures. In the current research, the contact mode and two electrodes of triboelectric nanogenerators have been discussed and also the relationships governing the nano generator have been calculated. By comparing the response of the nano generator in ideal and non-ideal state, it can be understood that considering the effects of edge capacitors will be effective on the final response of the nano generator. Finally, the effect of various parameters on the performance of the triboelectric nano generator has also been investigated. © 2023 Walter de Gruyter GmbH. All rights reserved.

关键词： Nanogenerators

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Visual Observer-Based State-Feedback Autonomous Motion Control on SE(3)

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无人系统（英文） 2024年第2期12卷 375-390页

作者： Tong Zhang Ying Tan Xiang Chen Guoxiang Gu Department of Electrical and Computer Engineering University of WindsorWindsorOntarioN9B3P4 Canada Department of Mechanical Engineering University of MelbourneMelbourneVictoria 3010Australia Department of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLouisianaLA 70803USA

This paper introduces a visual observer-based state-feedback control tailored for autonomous motion on SE(3)to address challenges posed by modeling uncertainties and measurement *** proposed approach unfolds in two fundamental *** the initial stage,the visual observer,based on modeling information,visual sensors,and pre-deployed landmarks,along with the state-feedback controller,are developed independently,underpinning their individual semi-global practical asymptotic(SPA)*** modular approach ensures a robust foundation for the subsequent *** latter stage applies the well-established Small Gain Principle to regulate observer and controller parameters to guarantee the SPA stability of the closed-loop system with the visual-observer based state feedback *** effectiveness of the proposed method is validated through simulation and experiments.

关键词： Autonomous motion on SE(3) observer-based state-feedback controller small gain principle semi-global asymptotic practical stability

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Design and usability assessment of touchscreen interface in medical devices: a case study of a neonatal incubator

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Research on Biomedical engineering 2025年第1期41卷 1-6页

作者： Viviani, Carlos Alessandro Bassi Elias, Leonardo Abdala Calil, Saide Jorge Department of Electronics and Biomedical Engineering School of Electrical and Computer Engineering University of Campinas SP Campinas Brazil Center for Biomedical Engineering University of Campinas Campinas Brazil

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Development of Self-Powered e-Gear Selector Based on Single-Electrode Triboelectric Nanogenerator (TENG) Technology

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2025年第1期4卷 20-29页

作者： Ali, Sam Maddipatla, Dinesh Bazuin, Bradley J. Atashbar, Massood Z. Western Michigan University Department of Electrical and Computer Engineering KalamazooMI49008 United States

A novel e-Gear selector single-electrode-based triboelectric nanogenerator (TENG) was successfully designed, fabricated, and tested on flexible substrates. The proposed device consists of four TENG sensors representing the traditional gear shift selector (P, R, N, and D). Leather, cotton, paper, and thermoplastic polyurethane (TPU) were selected as bottom triboelectric layers for TENGs 1–4, respectively. Silver (Ag) ink was screen-printed on the backside of each layer representing the single electrode. Human skin was chosen as the top triboelectric layer. Based on the selection of the materials, each TENG device generates different output voltages, enabling the selection of the intended gear mode without the need for a complex control algorithm. The TENG device was characterized in terms of open-circuit voltage (Voc) and short-circuit current (Isc). The repeatability and reproducibility of the TENG device were evaluated by fabricating three samples of each TENG device and by testing each TENG device three times. An average Voc of 4.27 ± 0.36, 7 ± 0.31, 9.05 ± 0.21, and 12.38 ± 0.34 Vpp, resulted in a power density of 0.37 ± 0.05, 0.72 ± 0.09, 0.95 ± 0.07, and 1.74 ± 0.10 μW/cm2, was measured for TENGs 1–4, respectively. The performance of the e-Gear selector showcased the potential of the TENG energy harvesting method for integration into the automotive industry, aligning with the rapid advancements in electric vehicle (EV) applications. This technology is particularly promising for future gear selector designs, as it eliminates the need for external power supplies, reduces cost, minimizes wiring, and offers greater flexibility in positioning within the vehicle. © 2022 IEEE.

关键词： Leather

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Energy-Efficient and Robust Communication in Multi-Connectivity Enabled mmWave Ultra-Dense Networks

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年第2期9卷 621-634页

作者： Kim, Jungjun Lee, Jihwan Bahk, Saewoong Seoul National University Department of Electrical and Computer Engineering INMC Seoul08826 Korea Republic of

The ultra-dense network (UDN) concept with multi-connectivity (MC) has emerged as a promising scenario for millimeter-wave (mmWave) communications due to its synergistic effect. However, mmWave UDNs with MC face challenges such as escalated energy consumption and computational complexity for traffic splitting decisions. To save energy and reduce the need for frequent traffic splitting decisions in the considered scenario, we formulate the problem as a mixed integer programming (MIP). Our objective is to determine which base stations (BSs) to enable for energy saving and how to split user traffic to maintain robust communication in MC enabled UDNs. To this end, this paper proposes energy-efficient and robust communication (EERC), an ensemble of heuristic methods and convex optimization, to achieve our goal without directly solving the computationally expensive MIP. In addition, we consider greedy algorithm that potentially offers enhanced optimality. We analyze the complexity of each scheme and demonstrate that EERC is the most viable option considering both performance and complexity. Through extensive simulations, we demonstrate that EERC achieves over 98% optimality for small-sized networks. More importantly, we evaluate EERC in realistic scenarios using a 3-D ray-tracing simulator and confirm that EERC performs very well even in urban terrain environments with buildings. © 2017 IEEE.

关键词： Convex optimization

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Learning from Mistakes: A Multi-level Optimization Framework

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1651-1663页

作者： Zhang, Li Garg, Bhanu Sridhara, Pradyumna Hosseini, Ramtin Xie, Pengtao University of California Department of Electrical and Computer Engineering San DiegoCA92093 United States

Bi-level optimization methods in machine learning are popularly effective in subdomains of neural architecture search, data re-weighting, etc. However, most of these methods do not factor in variations in learning difficulty, which limits their performance in real-world applications. To address the above problems, we propose a framework that imitates the learning process of humans. In human learning, learners usually focus more on the topics where mistakes have been made in the past to deepen their understanding and master the knowledge. Inspired by this effective human learning technique, we propose a multi-level optimization framework, Learning From Mistakes (LFM), for machine learning. We formulate LFM as a three-stage optimization problem: 1) the learner learns, 2) the learner re-learns based on the mistakes made before, and;3) the learner validates his learning. We develop an efficient algorithm to solve the optimization problem. We further apply our method to differentiable neural architecture search and data re-weighting. Extensive experiments on CIFAR-10, CIFAR-100, ImageNet, and other related datasets powerfully demonstrate the effectiveness of our approach. The code of LFM is available at: https://***/importZL/LFM. © 2020 IEEE.

关键词： Adversarial machine learning

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Deep learning for object recognition: A comprehensive review of models and algorithms

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International Journal of Cognitive Computing in engineering 2025年 6卷 298-312页

作者： Tsirtsakis, Paschalis Zacharis, Georgios Maraslidis, George S. Fragulis, George F. Department of Electrical and Computer Engineering University of Western Macedonia ZEP Campus Kozani50100 Greece

The rapid advancements in artificial intelligence (AI) and machine learning (ML) have significantly enhanced progress in computer vision, opening doors to innovative technological possibilities and enabling a range of real-world applications. Despite these developments, object recognition remains a complex domain with persistent challenges and limitations. This work seeks to address these challenges by investigating the effectiveness of deep learning (DL) methods in object detection tasks. Leveraging DL allows for the direct learning of feature representations from image data, resulting in advanced performance. This review provides a comprehensive examination of fundamental models and algorithms, with a particular focus on neural network (NN) frameworks utilized for feature extraction. Additionally, it evaluates the benchmark datasets commonly employed to assess their performance in object recognition tasks. © 2025 The Authors

关键词： Contrastive Learning

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Ensemble based unsupervised anomaly detection with concept drift adaptation for time series data 10

Ensemble based unsupervised anomaly detection with concept d...

引用

10th IEEE Smart World Congress, SWC 2024

作者： Li, Danlei Nair, Nirmal Wang, Kevin I-Kai Sakurai, Kouichi University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand Kyushu University Department of Informatics Faculty of Information Science and Electrical Engineering Fukuoka Japan

ISBN: (纸本)9798331520861

Anomaly detection is the identification of instances that substantially deviate from the majority of the data and do not conform to a well-defined normal behavior. Investigating time series anomalies has become increasingly popular in recent years due to widely deployed IoT devices and sensors. Although most existing methods apply image anomaly detection algorithms directly on time series and commonly assume the data is IID (independent and identically distributed). However, in most realworld applications, the underlying data distribution alters over time. Changes in data over time are referred to as concept drift and can significantly degrade the efficacy of an anomaly detection model. Existing research also indicates that the majority of anomaly detection frameworks ignored concept drift, resulting in poor long-term model performance. This paper presents an ensemble-based, unsupervised anomaly detection framework that is adaptive to the concept drift in time series data. Both distribution and performance-based techniques are used to detect concept drift and update the model to ensure the framework's longterm performance reliability. The proposed method is evaluated using a real-world univariate time series data that captures sea surface temperature data over a 2 -year period. By comparing our proposed framework to other state-of-the-art algorithms, we demonstrate our proposed method has obvious benefits in accuracy and long-term stability. © 2024 IEEE.

关键词： Anomaly detection Concept drift adaptation Ensemble method Time series Unsupervised method

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DisCovHAR: Contrastive Attention for Human Activity Recognition Under Distribution Shifts

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IEEE Internet of Things Journal 2025年第12期12卷 21973-21983页

作者： Chen, Luke Odema, Mohanad Al Faruque, Mohammad Abdullah University of California Department of Electrical Engineering and Computer Science IrvineCA92697 United States

Advances in Internet-of-Things (IoT) wearable sensors and Edge-Artificial Intelligence (Edge-AI) have enabled practical realizations of Machine Learning (ML)-enabled mobile sensing applications like Human Activity Recognition (HAR). The effective deployment of these data-driven models necessitates learning robust representations capable of handling prevalent Distribution Shifts (DS), including new users, device positions, rotations, and more. In that respect, Contrastive Learning (CL) has shown promise in learning transformation-invariant features, outperforming traditional HAR methods. However, recent findings reveal that the contrastive loss induces shrinkage and expansion of the feature space which may limit the generalization capacity of the model. To address this, we propose DisCovHAR, a contrastive attention method to selectively apply the contrastive loss to a subset of the feature space through the transformer encoder attention mechanism. Extensive experiments on three HAR datasets (DSADS, PAMAP2, USCHAD) demonstrate its superiority over state-of-the-art methods. Specifically, our approach yields up to 4.47% and 7.82% average accuracy improvements in subject-wise and position-wise generalization settings. Furthermore, DisCov-HAR demonstrates up to 5.07% increased robustness compared to prior methods under multivariate distribution shift scenarios. © 2014 IEEE.

关键词： Contrastive Learning

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