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7th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2023

作者： Zhang, Pengyu Mao, Li Zhang, Wei Tang, Kai Laboratory of Intelligent Control and Robotics Shanghai University of Engineering Science 333 Longteng Road Shanghai China Computer Engineering College Dalian University of Technology 1 Guangning Road Dalian China

ISBN: (纸本)9789819733392

This article investigates the problem of bipartite formation control for a class of multi-agent systems composed of two sub-formations with an inverse connection. A distributed control algorithm with observer-based output feedback is proposed under directed signed topology and unknown external disturbances. Some sufficient conditions are established to achieve a bipartite time-varying formation with bounded tracking error. Furthermore, it is shown that the suggested observer-based formation control has good robustness. Finally, a simulation example is illustrated to show the feasibility and effectiveness of the presented control scheme. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multi agent systems

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PierGuard: A Planning Framework for Underwater Robotic Inspection of Coastal Piers

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IEEE Transactions on Automation Science and engineering 2025年 22卷 15941-15952页

作者： Wang, Pengyu Lin, Hin Wang Li, Jialu Wang, Jiankun Shi, Ling Meng, Max Q.-H Southern University of Science and Technology Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Shenzhen China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Southern University of Science and Technology Jiaxing Research Institute Jiaxing China Hong Kong University of Science and Technology Department of Chemical and Biological Engineering Hong Kong The Chinese University of Hong Kong Department of Electronic Engineering Hong Kong University of Alberta Canada Department of Electrical and Computer Engineering Canada

Using underwater robots instead of humans for the inspection of coastal piers can enhance efficiency while reducing risks. A key challenge in performing these tasks lies in achieving efficient and rapid path planning within complex environments. Sampling-based path planning methods, such as Rapidly-exploring Random Tree* (RRT*), have demonstrated notable performance in high-dimensional spaces. In recent years, researchers have begun designing various geometry-inspired heuristics and neural network-driven heuristics to further enhance the effectiveness of RRT*. However, the performance of these general path planning methods still requires improvement when applied to highly cluttered underwater environments. In this paper, we propose PierGuard, which combines the strengths of bidirectional search and neural network-driven heuristic regions. We design a specialized neural network to generate high-quality heuristic regions in cluttered maps, thereby improving the performance of the path planning. Through extensive simulation and real-world ocean field experiments, we demonstrate the effectiveness and efficiency of our proposed method compared with previous research. Our method achieves approximately 2.6 times the performance of the state-of-the-art geometric-based sampling method and nearly 4.9 times that of the state-of-the-art learning-based sampling method. Our results provide valuable insights for the automation of pier inspection and the enhancement of maritime safety. (Video1). © 2004-2012 IEEE.

关键词： Motion planning

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Experimental evaluation of autonomous map-based Spot navigation in confined environments

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Biomimetic Intelligence & robotics 2022年第1期2卷 50-58页

作者： Anton Koval Samuel Karlsson George Nikolakopoulos Robotics&AI Team Deparment of ComputerElectrical and Space Engineering.Luled University of TechunologyLuled SE-97187Sweden

In this article,we address the task of experimental evaluation of autonomous map-based navigation of a Boston Dynamics Spot quadruped robot in confined environments equipped with the developed autonomy package that incorporates a 3D lidar,an IMU,and an onboard *** that,we propose an integrated software and hardware system that is considered as an enabler for robot localization and risk-aware path planning,based on the known *** system design itself is modular and incorporates the perception capabilities required for autonomous *** Spot robot first is utilized to build the offline map of the environment by utilizing the Google Cartographer simultaneous localization and mapping(SLAM)*** the next step,the online environmental information from the autonomy package sensors and the offline map are provided to the onboard computer to localize the robot on the known map by utilizing means provided by ***,the occupancy information is provided to the online grid-based path planner that generates risk-aware *** extensive experimental evaluation of the proposed system is performed in corridors and SubT environments.

关键词： Spot SubT Map-based navigation 3D lidar Autonomy package

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A Survey on Machine Learning Approaches in Water Analysis 13th

A Survey on Machine Learning Approaches in Water Analysis

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13th Mining Humanistic Data Workshop, MHDW 2024, 9th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st Workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 International Workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Tsimpidi, Ilektra Sartjärvi, Rosa Juntunen, Petri Nikolakopoulos, George Robotics and AI Team Department of Computer Science Space and Electrical Engineering Luleå University of Technology Luleå Sweden Savonia University of Applied Sciences Kuopio Finland

ISBN: (纸本)9783031632266

The aim of this article is to present a survey on Machine Learning approaches for performing water analysis as in general integrating Artificial Intelligence in water analysis has a transformative potential for optimizing and sustaining water resources. Recent trends in water treatment and desalination applications effectively address pollution and scarcity challenges, while by combining AI with technologies like data analytics, water management complexities are simplified, ensuring sustainability and cost-effectiveness through predictive data assessment. This survey presents examples of the ML’s significance in water treatment and in water quality analysis, such as for enhancing accuracy in water quality index (WQI) assessments and examples of exploring and predicting pollutants in water processes, and aiding in the early detection and categorization of contaminants. Furthermore, this survey will reveal the immense potential in leveraging ML algorithms to enhance water analysis accuracy, speed, and efficiency, paving the way for reduced chemical usage and improved understanding of microbiological processes. As it will be presented, ML approaches offer valuable tools across various stages of water analysis, from identifying critical indicators to accurately measuring and predicting water parameters. The survey concludes by emphasizing the potential of AI and particularly ML to revolutionize water resource management, offering precision, efficiency, and foresight in addressing challenges of water scarcity and sustainable resource utilization. © IFIP International Federation for Information Processing 2024.

关键词： Machine learning

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A Multi-Scale Control Framework for Electric and Automated Buses in Intercity Lines 63

A Multi-Scale Control Framework for Electric and Automated B...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Graffione, S. Bracco, S. Pasquale, C. Sacone, S. Siri, S. Ferrara, A. University of Genova Department of Informatics Bioengineering Robotics and Systems Engineering Italy University of Genova Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture Italy University of Pavia Department of Electrical Computer and Biomedical Engineering Italy

ISBN: (纸本)9798350316339

This paper proposes a multi-scale control scheme aimed at controlling speed, dwell time, and charging time of electric and automated buses performing the transport service on lines where there are no reserved lanes. The proposed control scheme has two layers characterized by different control objectives, level of detail and time scales. The high-level controller periodically solves a multi-objective optimal control problem formalized to ensure an adequate transport service, while the low-level controller, being based on a more accurate description of the bus dynamics and energy consumption, seeks to follow the high-level control actions while taking into account the effective traffic conditions encountered on the line and the effective dynamic behaviour of the bus. The paper concludes with the application to a case study in order to demonstrate the effectiveness of the proposed control scheme. © 2024 IEEE.

关键词： Bus transportation

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Observer-Based Positive Edge Consensus of Multi-agent Systems on Directed Graphs 7th

Observer-Based Positive Edge Consensus of Multi-agent Syste...

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7th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2023

作者： Zhang, Pengyu Zhang, Wei Mao, Li Chen, Jiayi Hu, Zhi Shanghai University of Engineering Science Laboratory of Intelligent Control and Robotics 333 Longteng Road Shanghai China Computer Engineering College Dalian University of Technology 1 Guangning Road Dalian China

ISBN: (纸本)9789819733231

This paper explores positive edge-consensus in directed networks with spanning trees using output feedback protocols. First, a novel positive system observer is proposed, which introduces a parameter to enhance the design freedom of the observer. Furthermore, using positive system theory and graph theory, sufficient conditions are proposed and the sufficient conditions based solely on the network’s edge count. Specifically, an optimized bound is given for the eigenvalue of the Laplacian matrix, and an improved consensus and non-negative condition is obtained. Finally, the effectiveness of the proposed control protocol is verified utilizing numerical simulations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multi agent systems

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MHSDB: A Comprehensive Benchmark for Multimodal Humor and Sarcasm Detection Leveraging Foundation Models

MHSDB: A Comprehensive Benchmark for Multimodal Humor and Sa...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Dong, Zhongren Wang, Donghao Chen, Ciqiang Huang, Dong-Yan Zhang, Zixing College of Computer Science and Electronic Engineering Hunan University China Shenzhen Research Institute Hunan University China BTECH Robotics Corp China

ISBN: (纸本)9798350368741

Understanding multimodal humor and sarcasm detection remains a key challenge in artificial intelligence. Despite recent advances, inconsistencies in feature extraction, evaluation methods, and experimental setups have hindered fair comparisons across different approaches. To address this issue, we propose the Multimodal Humor and Sarcasm Detection Benchmark (MHSDB), the first unified evaluation platform specifically designed for these tasks. MHSDB combines four datasets in English and Hindi and standardizes feature extraction and evaluation processes to facilitate consistent comparisons. We systematically evaluate mainstream foundation models across audio, video, and text modalities. Unimodal representations are assessed using self-attention mechanisms, while multimodal representations are evaluated through mainstream fusion strategies, including utterance-level and sequence-level approaches. Our experimental results reveal that multimodal approaches outperform unimodal ones in capturing complex contexts and multi-layered semantics. Additionally, specific fusion strategies excel at integrating cross-modal information, achieving state-of-the-art performance, and paving the way for future research on optimizing feature representation and multimodal fusion. © 2025 IEEE.

关键词： Benchmark Foundation Models Multimodal Fusion Multimodal Humor Detection Sarcasm Detection

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Fuzzy-NMS: Improving 3D Object Detection With Fuzzy Classification in NMS

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Wang, Li Zhang, Xinyu Zhao, Fachuan Wu, Chuze Wang, Yichen Song, Ziying Yang, Lei Xu, Bin Li, Jun Ge, Shuzhi Sam School of Machanical Engineering Beijing Institute of Technology Beijing China School of Vehicle and Mobility Tsinghua University Beijing China School of Computer and Information Technology Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering Interactive Digital Media Institute Social Robotics Laboratory National University of Singapore Singapore

Non-maximum suppression (NMS) is an essential post-processing module in many 3D object detection frameworks to remove overlapping candidate bounding boxes. However, an overreliance on classification scores and difficulties in determining appropriate thresholds can affect the resulting accuracy directly. To address these issues, we introduce fuzzy learning into NMS and propose a novel generalized Fuzzy-NMS module to achieve finer candidate bounding box filtering. The proposed Fuzzy-NMS module combines the volume and clustering density of candidate bounding boxes, refining them with a fuzzy classification method and optimizing the appropriate suppression thresholds to reduce uncertainty in the NMS process. Adequate validation experiments use the mainstream KITTI and large-scale Waymo 3D object detection benchmarks. The results of these tests demonstrate the proposed Fuzzy-NMS module can improve the accuracy of numerous recently NMS-based detectors significantly, including PointPillars, PV-RCNN, and IA-SSD, etc. This effect is particularly evident for small objects such as pedestrians and bicycles. As a plug-and-play module, Fuzzy-NMS does not need to be retrained and produces no obvious increases in inference time. IEEE

关键词： Object detection

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DeepRice: A deep learning and deep feature based classification of Rice leaf disease subtypes

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Artificial Intelligence in Agriculture 2024年第1期11卷 34-49页

作者： P.Isaac Ritharson Kumudha Raimond X.Anitha Mary Jennifer Eunice Robert Andrew J Department of CSE Karunya Institute of Technology and Sciences CoimbatoreTamilnaduIndia Department of Robotics Engineering Karunya Institute of Technology and SciencesCoimbatoreIndia Department of Mechatronics Engineering Manipal Institute of TechnologyManipal Academy of Higher EducationManipalKarnataka 576104India Department of Computer Science and Engineering Manipal Institute of TechnologyManipal Academy of Higher EducationManipalKarnataka 576104India

Rice stands as a crucial staple food globally,with its enduring sustainability hinging on the prompt detection of rice leaf ***,efficiently detecting diseases when they have already occurred holds paramount importance for solving the cost of manual visual identification and chemical *** the recent past,the identification of leaf pathologies in crops predominantly relies on manual methods using specialized equipment,which proves to be time-consuming and *** study offers a remedy by harnessing Deep Learning(DL)and transfer learning techniques to accurately identify and classify rice leaf diseases.A comprehensive dataset comprising 5932 self-generated images of rice leaves was assembled along with the benchmark datasets,categorized into 9 classes irrespective of the extent of disease spread across the *** classes encompass diverse states including healthy leaves,mild and severe blight,mild and severe tungro,mild and severe blast,as well as mild and severe brown *** meticulous manual labelling and dataset segmentation,which was validated by horticulture experts,data augmentation strategies were implemented to amplify the number of *** datasets were subjected to evaluation using the proposed tailored Convolutional Neural Networks *** performance are scrutinized in conjunction with alternative transfer learning approaches like VGG16,Xception,ResNet50,DenseNet121,Inception ResnetV2,and Inception *** effectiveness of the proposed custom VGG16 model was gauged by its capacity to generalize to unseen images,yielding an exceptional accuracy of 99.94%,surpassing the benchmarks set by existing state-of-the-art ***,the layer wise feature extraction is also visualized as the interpretable AI.

关键词： Rice leaf disease Deep learning CNN Crop yield Agriculture Food security

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Probabilistic Modelling and Safety Assurance of an Agriculture Robot Providing Light-Treatment 19

Probabilistic Modelling and Safety Assurance of an Agricultu...

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19th IEEE International Conference on Automation Science and engineering, CASE 2023

作者： Adam, Mustafa Ye, Kangfeng Anisi, David A. Cavalcanti, Ana Woodcock, Jim Morris, Robert Robotics Group Faculty of Science & Technology Norway University of York Dept. of Computer Science United Kingdom Faculty of Engineering and Science Dept. of Mechatronics Norway Saga Robotics As Norway

ISBN: (纸本)9798350320695

Continued adoption of agricultural robots postulates the farmer's trust in the reliability, robustness and safety of the new technology. This motivates our work on safety assurance of agricultural robots, particularly their ability to detect, track and avoid obstacles and humans. This paper considers a probabilistic modelling and risk analysis framework for use in the early development phases. Starting off with hazard identification and a risk assessment matrix, the behaviour of the mobile robot platform, sensor and perception system, and any humans present are captured using three state machines. An auto-generated probabilistic model is then solved and analysed using the probabilistic model checker PRISM. The result provides unique insight into fundamental development and engineering aspects by quantifying the effect of the risk mitigation actions and risk reduction associated with distinct design concepts. These include implications of adopting a higher performance and more expensive Object Detection System or opting for a more elaborate warning system to increase human awareness. Although this paper mainly focuses on the initial concept-development phase, the proposed safety-assurance framework can also be used during implementation, and subsequent deployment and operation phases. © 2023 IEEE.

关键词： Agricultural robots

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