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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Eric Hannus Tran Nguyen Le David Blanco-Mulero Ville Kyrki Intelligent Robotics Group at The Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland CSIC-UPC Institut de Robòtica i Informàtica Industrial Barcelona Spain

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Imitation Learning (IL) is a promising paradigm for learning dynamic manipulation of deformable objects since it does not depend on difficult-to-create accurate simulations of such objects. However, the translation of motions demonstrated by a human to a robot is a challenge for IL, due to differences in the embodiments and the robot’s physical limits. These limits are especially relevant in dynamic manipulation where high velocities and accelerations are typical. To address this problem, we propose a framework that first maps a dynamic demonstration into a motion that respects the robot’s constraints using a constrained Dynamic Movement Primitive. Second, the resulting object state is further optimized by quasi-static refinement motions to optimize task performance metrics. This allows both efficiently altering the object state by dynamic motions and stable small-scale refinements. We evaluate the framework in the challenging task of bag opening, designing the system BILBO: Bimanual dynamic manipulation using Imitation Learning for Bag Opening. Our results show that BILBO can successfully open a wide range of crumpled bags, using a demonstration with a single bag. See supplementary material at https://***/view/bilbo-bag.

关键词： Measurement Learning systems Translation Shape Imitation learning Dynamics Kinematics Hardware Manipulator dynamics Intelligent robots

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A Predefined-Time Convergent and Noise-Tolerant Zeroing Neural Network Model for Time Variant Quadratic Programming With Application to Robot Motion Planning

arXiv

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arXiv 2024年

作者： Yang, Yi Wang, Xuchen Voyles, Richard M. Ma, Xin The Multi-Scale Medical Robotics Center The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong 999077 Hong Kong The School of Engineering Technology Purdue University West LafayetteIN47907 United States

This paper develops a predefined-time convergent and noise-tolerant fractional-order zeroing neural network (PTC-NT-FOZNN) model, innovatively engineered to tackle time-variant quadratic programming (TVQP) challenges. The PTC-NT-FOZNN, stemming from a novel iteration within the variable-gain ZNN spectrum, known as FOZNNs, features diminishing gains over time and marries noise resistance with predefined-time convergence, making it ideal for energy-efficient robotic motion planning tasks. The PTC-NT-FOZNN enhances traditional ZNN models by incorporating a newly developed activation function that promotes optimal convergence irrespective of the model’s order. When evaluated against six established ZNNs, the PTC-NT-FOZNN, with parameters 0 © 2024, CC BY.

关键词： Robot programming

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A Comprehensive Analysis of IoT and IoT Security Attacks: Understanding the Threat Landscape

A Comprehensive Analysis of IoT and IoT Security Attacks: Un...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Himanshi Dhayal Vikas Siwach Harkesh Sehrawat Renu Dalai Department of Computer Science and Engineering UIET Maharshi Dayanand University Rohtak Haryana India University School of Automation and Robotics (USAR) GGSIPU Guru Gobind Singh Indraprastha University Delhi India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

IoT has remarkably changed the internet landscape since its introduction. Today, even an isolated physical device is provided with the internet, which allows it to exchange information with other devices. As much flexibility as these devices offer, they also have numerous security issues, causing threats to its users. Adversaries increasingly exploit the vulnerabilities in Internet of Things devices to execute attacks that can inflict significant harm on individuals, communities, and organizations. Consequently, prioritizing the detection, prevention and mitigation of the attacks is imperative to ensure safety and security. The paper provides an in-depth examination of internet of things (IoT) taxonomy, highlighting the associated challenges and security attacks within the IoT ecosystem, which subsequently gives insights into the vulnerabilities of the IoT ecosystem. Additionally, it includes a comparative analysis of various security attacks along with their impacts.

关键词： Prevention and mitigation Ecosystems Taxonomy Organizations Communications technology Safety Security Internet of Things Computational intelligence

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Agd-Net: Attention-Guided Channel Fusion Aided Dual Branch for Breast Histopathology Image Segmentation

SSRN

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SSRN 2025年

作者： Wang, Yi Guo, Rongrong Li, Yi School of Mechanical Engineering and Automation Shanghai University Shanghai China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai China Zhongyuan Development Institute Henan University Henan Zhengzhou China

Breast cancer is a significant global health risk for women, highlighting the essential role of cell nucleus segmentation in the analysis of tumor pathology using microscopic images. However, this process is often time-consuming and labor-intensive, emphasizing the need for effective computer-aided diagnostic tools for early detection and treatment planning. Accurate segmentation of cell nuclei faces challenges due to staining variations, as well as differences in shape, size, and overlapping clusters. To tackle these challenges, we propose AGD-Net, a deep learning model specifically designed to aid pathologists in the efficient and accurate analysis of complex features in pathology images. Our model employs an improved MultiResU-Net as the segmentation backbone, leveraging its multi-scale analysis capabilities and efficient feature propagation through skip connections. Through a dual-branch structure, our model captures both semantic and detailed information, enhancing overall image comprehension while preserving local details. Furthermore, we integrate an attention mechanism to effectively fuse multi-scale features from the encoder and upsampled features from the decoder, ensuring seamless integration of high-resolution low-level features and low-resolution high-level semantics. Furthermore, we present a Channel Space Dense Residual Block (CSRB) within the skip connections, which facilitates controlled information transfer while enhancing feature representation and transfer efficiency. We validate our method on two reputable breast cancer histopathological datasets, TNBC and MonuSeg, demonstrating superior segmentation performance compared to state-of-the-art methods. The source code is publicly available on GitHub (https://***/wangyear/***). © 2025, The Authors. All rights reserved.

关键词： Diseases

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Based on the improved YOLOV8 small target detection algorithm

Based on the improved YOLOV8 small target detection algorith...

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2024 International Conference on Mechatronic engineering and Artificial Intelligence, MEAI 2024

作者： Jia, Libin Gao, Hongwei Wang, Qingyv Jiao, Canguang Sun, Jian School of Automation and Electrical Engineering Shenyang Ligong University Liaoning Shenyang110159 China Institute of International Engineering Shenyang Ligong University Liaoning Shenyang110159 China China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang110159 China

ISBN: (纸本)9781510689176

With the rapid development of drone technology, drone-based target detection has become a hot topic in current research. However, existing methods face challenges such as low detection accuracy and poor real-time performance. To address this issue, we propose an improved target detection method named YOLOV8-SNP for application in aerial images. The algorithm combines a more lightweight startnet backbone network to enhance detection speed. Additionally, we utilize the consistent dual assignment strategy(CDAS)method to accelerate post-processing and add a dedicated layer for small targets to improve their accurate detection. Experimental results show that our YOLOV8-SNP improves detection accuracy by 3% compared to YOLOV8 on the Visdrone dataset. © 2025 SPIE.

关键词： Aircraft detection

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Dynamic Manipulation of Deformable Objects using Imitation Learning with Adaptation to Hardware Constraints

arXiv

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arXiv 2024年

作者： Hannus, Eric Le, Tran Nguyen Blanco-Mulero, David Kyrki, Ville Intelligent Robotics Group The Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Institut de Robòtica i Informàtica Industrial CSIC-UPC Barcelona Spain

关键词： Robots

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A 22-DOFs bio-inspired soft hand achieving 6 kinds of in-hand manipulation

A 22-DOFs bio-inspired soft hand achieving 6 kinds of in-han...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Zhou, Jianshu Li, Yunquan Yang, Yang Cao, Hanwen Huang, Junda Liu, Yunhui Chinese University of Hong Kong Department of Mechanical and Automation Engineering Hong Kong Center of Logistics Robotics Mechanical Engineering University of Hong Kong School of Automation Nanjing University of Information Science and Technology

ISBN: (纸本)9781665436786

Bio-inspired soft robots present distinctive superiorities in safety issues working in a human-centered environment. Soft robotic hands are of prominent popularity for soft robots to be applied in real applications. With continuous efforts being devoted, researchers have been developing various soft hands performing excellent grasping capability towards daily objects with salient safety insurance. However, the functional dexterity of soft robotic hands is still limited, especially because lacking in-hand object manipulation capabilities, which inevitably limits the application potential of soft robotic hands. This restriction is induced by the limited dexterity of soft actuators usually with a one-DOF per actuator providing a predefined motion trajectory. To tackle this challenge, a novel bio-inspired multi-DOF soft actuator based on V-joint is proposed, which achieves human finger-joint-like motion. By connecting the V-joint in a desired order, it is possible to build multi-DOF soft robots like traditional rigid counterparts. A 22-DOFs anthropomorphic soft hand (S-22) is fabricated out based on the V-joint, which is capable of realizing dexterous hand gestures and 6 kinds of in-hand manipulation. The presented soft robotic approach is a promising solution for dexterous soft robotic hand design and broden the potential application of soft hands. © 2021 IEEE.

关键词： Robotic arms

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A New MBC-TSI Topology for Microinverter PV Application

A New MBC-TSI Topology for Microinverter PV Application

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Power engineering Application (ICPEA), IEEE International Conference in

作者： A. Razi M. Nabil Hidayat A. Jidin S. A. A. Shukor S. Z. M. Noor Centre of Robotics and Automation (CeRIA) Universiti Teknikal Malaysia Melaka Melaka Malaysia School of Electrical Engineering College of Engineering Universiti Teknologi MARA Shah Alam Malaysia

This paper presents the design of simulation model of the proposed Multilevel Boost Converter (MBC)-Three Switch Inverter (TSI) for Self-Consumption (SELCO) or known as microinverter in Off-grid Photovoltaic (OGPV) system. Individual PV without array combination is the applied rule towards Microinverter PV panel. The proposed MBC-TSI topology utilize only two isolated individual PV panel for a new approach toward two-stage microinverter (TSM) PV configuration. Conventional H-bridge topology with a total of 7 identical unit of PV panels and H-Bridge topology is taken for reference study aims for 230Vrms PV system with voltage gain almost the same with the proposed topology. The simulation model has been developed using MATLAB Simulink at Standard Test Condition (STC) with open loop switching technique adopted by implementing the switching signals produced by the pulse generator block. The steady-state simulation results show that the proposed MBC-TSI topology successfully generated a voltage gain, AV of 7.34 through reduced number of PV panel (2-unit PV instead of 7-unit PV from the reference study) and generated a balanced 3-level 230Vrms output with voltage and current harmonic percentage of 48.34% and 16.47% respectively. Thus, proved the proposed topology offering a new approach towards TSM in SELCO PV network.

关键词： Photovoltaic systems Network topology Software packages Simulation Switches Predictive models Mathematical models

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A Numerical Method for the Compliance Analysis of Continuum Robots

A Numerical Method for the Compliance Analysis of Continuum ...

引用

robotics, Intelligent Control and Artificial Intelligence (RICAI), International Conference on

作者： Yicheng Dai Sheng Wang Xin Wang Han Yuan School of Mechanical Engineering and Automation Harbin Institute of Technology (Shenzhen) Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen China

Continuum robots are characterized by their high compliance and dexterity. However, these features also lead to a reduction in accuracy. While many literature on continuum robots address aspects such as design, kinematics, and statics, there are few in-depth studies on the compliance of these robots. This paper introduces a numerical approach for obtaining the compliance of continuum robots, taking into account the influences of cable tensions, external loads, and gravity. Based on this method, an analysis to examine the compliance of a two-section cable-driven continuum robot within its workspace under various load conditions is conducted. Results indicate that the robot's compliance is correlated with the external load and the shape of the robot. Moreover, the compliance of the robot in the x direction and y direction is highly related with the distance between the robot's tip end and axes. These conclusions could be useful for the active compliance control of the robot.

关键词：

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Hybrid Trajectory Optimization for Autonomous Terrain Traversal of Articulated Tracked Robots

arXiv

引用

arXiv 2023年

作者： Xu, Zhengzhe Chen, Yanbo Jian, Zhuozhu Tan, Junbo Wang, Xueqian Liang, Bin The School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China The Center for Artificial Intelligence and Robotics Shenzhen International Graduate School Tsinghua University Shenzhen China

Autonomous terrain traversal of articulated tracked robots can reduce operator cognitive load to enhance task efficiency and facilitate extensive deployment. We present a novel hybrid trajectory optimization method aimed at generating efficient, stable, and smooth traversal motions. To achieve this, we develop a planar robot-terrain contact model and divide the robot’s motion into hybrid modes of driving and traversing. By using a generalized coordinate description, the configuration space dimension is reduced, which facilitates real-time planning. The hybrid trajectory optimization is transcribed into a nonlinear programming problem and divided into subproblems to be solved in a receding-horizon planning fashion. Mode switching is facilitated by associating optimized motion durations with a predefined traversal sequence. A multi-objective cost function is formulated to further improve the traversal performance. Additionally, map sampling, terrain simplification, and tracking controller modules are integrated into the autonomous terrain traversal system. Our approach is validated in simulation and real-world scenarios with the Searcher robotic platform. Comparative experiments with expert operator control and state-of-the-art methods show advantages in terms of time and energy efficiency, stability, and smoothness of motion. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost functions

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