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IEEE International Conference on robotics and automation (ICRA)

作者： Emanuele Nicotra Chi Cong Nguyen James Davies Phuoc Thien Phan Trung Thien Hoang Bibhu Sharma Adrienne Ji Kefan Zhu Trung Dung Ngo Van Anh Ho Hung Manh La Nigel H. Lovell Thanh Nho Do Graduate School of Biomedical Engineering Faculty of Engineering UNSW Sydney Kensington Campus Australia More-Than-One Robotics Laboratory Faculty of Sustainable Design Engineering University of Prince Edward Island Charlottetown Canada Soft Haptics Laboratory School of Materials Science Japan Advanced Institute of Science and Technology Kawaguchi Saitama Japan Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Aneurysms, balloon-like bulges in blood vessels, present a significant health risk due to their potential to rupture, leading to life-threatening internal bleeding. Current treatments often involve delivering embolic materials or metal coils to fill these bulges, occluding them from the pressure of blood flow. However, clinical micro-catheters that deploy embolic materials used today face limitations, primarily their rigidity and the lack of active control over the bending tip of the catheter. This paper introduces a new soft micro-robotics catheter, with diameter of only 0.8 mm, equipped with a hollow channel. With this new design, the new device can induce bending motions at its tip for active steerability to reach desired aneurysm targets and then perform the delivery of embolic materials and tools. To enhance the control and precise navigation during procedures, a robust mathematical model and image processing techniques are also introduced and validated. Experiments are also performed to characterise and validate the model’s accuracy and the steerability and navigation capabilities of the new micro-catheter.

关键词： Performance evaluation Navigation Metals Aneurysm Bending Mathematical models Rigidity

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Active Visuo-Haptic Object Shape Completion

arXiv

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arXiv 2022年

作者： Rustler, Lukas Lundell, Jens Behrens, Jan Kristof Kyrki, Ville Hoffmann, Matej The Department of Cybernetics Faculty of Electrical Engineering CTU Prague Czech Republic The Intelligent Robotics Group Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo02150 Finland The Czech Institute of Informatics Robotics and Cybernetics CTU Prague Czech Republic

Recent advancements in object shape completion have enabled impressive object reconstructions using only visual input. However, due to self-occlusion, the reconstructions have high uncertainty in the occluded object parts, which negatively impacts the performance of downstream robotic tasks such as grasping. In this work, we propose an active visuo-haptic shape completion method called Act-VH that actively computes where to touch the objects based on the reconstruction uncertainty. Act-VH reconstructs objects from point clouds and calculates the reconstruction uncertainty using IGR, a recent state-of-the-art implicit surface deep neural network. We experimentally evaluate the reconstruction accuracy of Act-VH against five baselines in simulation and in the real world. We also propose a new simulation environment for this purpose. The results show that Act-VH outperforms all baselines and that an uncertainty-driven haptic exploration policy leads to higher reconstruction accuracy than a random policy and a policy driven by Gaussian Process Implicit Surfaces. As a final experiment, we evaluate Act-VH and the best reconstruction baseline on grasping 10 novel objects. The results show that Act-VH reaches a significantly higher grasp success rate than the baseline on all objects. Together, this work opens up the door for using active visuo-haptic shape completion in more complex cluttered scenes. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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HiDe-LLaVA: Hierarchical Decoupling for Continual Instruction Tuning of Multimodal Large Language Model

arXiv

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arXiv 2025年

作者： Guo, Haiyang Zeng, Fanhu Xiang, Ziwei Zhu, Fei Wang, Da-Han Zhang, Xu-Yao Liu, Cheng-Lin SAIS UCAS United Kingdom MAIS Institute of Automation Chinese Academy of Sciences China SAI UCAS United Kingdom Centre for Artificial Intelligence and Robotics HKISI-CAS Hong Kong Department of Computer and Information Engineering Xiamen University of Technology China

Instruction tuning is widely used to improve a pre-trained Multimodal Large Language Model (MLLM) by training it on curated task-specific datasets, enabling better comprehension of human instructions. However, it is infeasible to collect all possible instruction datasets simultaneously in real-world scenarios. Thus, enabling MLLM with continual instruction tuning is essential for maintaining their adaptability. However, existing methods often trade off memory efficiency for performance gains, significantly compromising overall efficiency. In this paper, we propose a task-specific expansion and task-general fusion framework based on the variations in Centered Kernel Alignment (CKA) similarity across different model layers when trained on diverse datasets. Furthermore, we analyze the information leakage present in the existing benchmark and propose a new and more challenging benchmark to rationally evaluate the performance of different methods. Comprehensive experiments showcase a significant performance improvement of our method compared to existing state-of-the-art methods. Our code will be public available. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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A Framework for Active Vision-Based Robot Planning using Spiking Neural Networks

A Framework for Active Vision-Based Robot Planning using Spi...

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Mediterranean Conference on Control and automation (MED)

作者： Katerina Maria Oikonomou Ioannis Kansizoglou Antonios Gasteratos Laboratory of Robotics and Automation Democritus University of Thrace Xanthi Greece Department of Production and Management Engineering Democritus University of Thrace Xanthi Greece

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

Robust and energy-efficient robot planning is of utmost importance for mobile robots since the dynamic changes of the environment entail robotic agents with high adaptation capacities, so as to excel in their tasks. In this work, we introduce a hybrid spiking and deep neural network architecture for actor-critic control of a 6-DOF robot arm. Our method firstly involves autonomous object detection via active vision exploration and thereafter, the entire hybrid architecture is described. In specific, the actor utilises an integrated-and-fire model for action generation, while the critic a deep neural one for action evaluation. Lastly, the benefits of this approach in terms of energy efficiency are extensively discussed.

关键词： Deep learning automation Neural networks Object detection Manipulators Energy efficiency Planning

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Design a High-Frequency Dual Active Bridge Converter for Discrete Time Frequency

Design a High-Frequency Dual Active Bridge Converter for Dis...

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Advancements in Electrical, Electronics, Communication, Computing and automation (ICAECA), International Conference on

作者： M. Karthik G. Hemalatha M. Mathankumar L. Sreevidya R. Deepalakshmi V. Kandasamy Department of Electrical and Electronics Engineering SRM Madurai College for Engineering and Technology Pottapalayam Tamil Nadu India Department of Robotics and Automation Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Trichy Tamil Nadu India Department of Electrical and Electronics Engineering V.S.B College of Engineering Technical Campus Coimbatore Tamil Nadu India Department of Computer Science Engineering SRM Madurai College for Engineering and Technology Pottapalayam Tamil Nadu India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

The dual active bridge (DAB) has become a crucial interface element in today's sophisticated systems. Renewable energy sources produce DC power, which is then stored in batteries, ultra-capacitors, and other energy storage devices. DAB is a crucial part of power exchange between two different buses because of its galvanic separation and bidirectional power transfer capabilities. The operation of the DAB is compatible with a broad range of input voltages and output loads. The creation of DAB is covered in most works. A well-known semiconductor integrated circuit with programmable control parameters is used in this straightforward, inexpensive method.

关键词： Time-frequency analysis Renewable energy sources Costs Bridge circuits Switched reluctance motors Stability analysis Batteries Telecommunication computing Time-domain analysis Stress

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Reconfigurable Smart Grid Management for Electric Vehicle Charging and the Integration of Renewable Energy Sources using Pied Kingfisher Optimizer 2

Reconfigurable Smart Grid Management for Electric Vehicle Ch...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Sivaramkrishnan, M. Ramkumar, M. Siva Uday, Kaparthi Kanan, Mohammad Raj, C. Mohan Giri, Jayant Karpagam College of Engnieering Dept of EEE India SNS College of Technology Dept of ECE India CVR College of Engineering Ibrahimpatanam EIE Department Hyderabad India College of Engineering University of Business and Technology Department of Industrial Engineering Jeddah21448 Saudi Arabia College of Engineering Zarqa University Department of Mechanical Engineering Zarqa Jordan Centre for Robotics and Industrial Automation Sri Eshwar College of Engineering Department or EEE Coimbatore641202 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India

ISBN: (纸本)9798331505745

Electric Vehicle (EV) charging management under the Smart Grid (SG) framework with incorporated Renewable Energy Sources (RESs) functions to advance Energy Management (EM). The operational cost of EV charging varies because renewable energy availability can change so this affects the financial performance of charging operations. Integration of RESs with the SG becomes challenging for operational system efficiency particularly when renewable energy generation levels are low. To overcome these drawbacks, this manuscript proposes an efficient approach for SG management for EV charging with the integration of RESs. The suggested method is Pied Kingfisher Optimizer (PKO). The main aim of the suggested technique is to minimize the operational expenses of EV charging as well as maximize the efficiency of the SG system. The PKO is used to optimize the scheduling and allocation of energy resources within the SG system. By then, the suggested approach is implemented in MATLAB and contrasted with several other methods that are currently in use. The suggested technique outperforms all existing approaches such as Bald Eagle Search (BES), Red Kite Optimization (RKO), Artificial Neural Network-Particle Swarm Optimization (ANN-PSO), and Coati Optimization Algorithm-Constitutive Artificial Neural Network (COA-CANN). The suggested approach delivers superior performance than conventional methods by reducing operational costs to 638,322$ under coordinated charging and 629,745$ under smart charging without impacting system performance metrics. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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Autonomous Agricultural Farming Robot for Automatic Ploughing in a Closed Field

Autonomous Agricultural Farming Robot for Automatic Ploughin...

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IEEE India Conference (INDICON)

作者： Anand Padmanabhan A Rajesh Kannan Megalingam MTech in Robotics and Automation Department of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Kollam Kerala Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Kollam Kerala

ISBN: (纸本)9781665452724

The objective of this project is to design an agricultural robot for automatic ploughing. In the domain of agriculture, robots are undoubtedly playing an essential role in the autonomous farming process. In agriculture, the use of robots is increasing production, and robots are becoming more common in the field. The ongoing project aims to develop an agricultural farming robot that can plough a closed area naturally and involuntarily. It uses a LiDAR sensor. The sensor detects obstacles in the field and detects field boundaries. The Raspberry Pi serves as the system's heart and brain, allowing for quick, precise, and autonomous movement. The rest of the processes could be automated in the future. The development of autonomous vehicle in agriculture has sparked growing attention in recent times. This robot will enable farmers in doing farming tasks more effectively and timely.

关键词： Solid modeling Visualization Laser radar Prototypes Production Robot sensing systems Agriculture

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Electric Vehicle Charging Navigation Using Waterwheel Plant Algorithm for Coordinating Smart Grids and Intelligent Transportation Systems 2

Electric Vehicle Charging Navigation Using Waterwheel Plant ...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Siva Ramkumar, M. Sivaramkrishnan, M. Uday, Kaparthi Kanan, Mohammad Mohan Raj, C. Giri, Jayant SNS College of Technology Dept of ECE India Karpagam College of Engnieering Dept of EEE India CVR College of Engineering EIE Department Hyderabad India College of Engineering University of Business and Technology Department of Industrial Engineering Jeddah21448 Saudi Arabia College of Engineering Zarqa University Department of Mechanical Engineering Zarqa Jordan Centre for Robotics and Industrial Automation Sri Eshwar College of Engineering Department or EEE Coimbatore641202 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India

ISBN: (纸本)9798331505745

The efficient charging of electric vehicles (EVs) remains essential to create sustainable mobility while EVs transform into crucial elements of upcoming transportation systems. The present EV charging navigation systems deal with three main issues including grid congestion and inadequate charging station (CS) selection and delays from traffic *** paper proposes a novel Waterwheel Plant Algorithm (WWPA) for optimizing EV charging navigation within the Smart Grid (SG) and Intelligent Transportation Systems (ITS). The suggested method strives to enhance SG-EV coordination through demand balancing in addition to charge price reduction and decreased grid congestion levels. WWPA algorithm serves as the proposed method to optimize EV charging schedules. The MATLAB platform serves as the implementation environment for the proposed method which demonstrates superior performance than Multi-agent Reinforcement Learning (MARL), Deep Reinforcement Learning (DRL), Coati-Northern Goshawk Optimization (Coati-NGO) and Grey Sail Fish Optimization (GSFO). Results demonstrate that the WWPA algorithm reduces average charging expenses by 18.14% throughout diverse charging operations as it produces superior performance than existing algorithms operating in the SG ITS. © 2025 IEEE.

关键词： Reinforcement learning

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Backstepping-Based Adaptive Cooperative Control For Multiple Euler-Lagrange Systems Under Unknown Harmonic Disturbances

Backstepping-Based Adaptive Cooperative Control For Multiple...

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Chinese automation Congress (CAC)

作者： Ying Chen Zi-Yi Zhao Peng Sun Xiang-Gui Guo Jian-Liang Wang Daniel Coutinho Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Collaborative Flying Robotics Hangzhou Innovation Institute of Beihang University HangZhou China Department of Automation and Systems Universidade Federal de Santa Catarina Florianópolis Brazil

This paper studies cooperative control problem for multiple Euler-Lagrange systems with unknown harmonic disturbances, where the disturbances are the superposition of sinusoidal components with unknown amplitude, phase, and frequency. A novel adaptive disturbance observer without requiring the disturbance frequency information is designed to estimate the disturbances. Over the directed network topology, a cooperative control scheme is proposed based on backstepping techniques, so that the tracking error is ultimately uniformly bounded. The scheme developed can be used to reject the harmonic disturbances in a uniform way, which means the characteristics of the disturbances can be described as a fixed parameter vector. Finally, an application is presented to demonstrate the effectiveness of the proposed control strategy in the context of a two-link manipulator system.

关键词：

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Towards Autonomous Robotic Ultrasound Scanning Using the Reinforcement Learning-Based Volumetric Data Navigation Method 5

Towards Autonomous Robotic Ultrasound Scanning Using the Rei...

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5th World Robot Conference Symposium on Advanced robotics and automation, WRC SARA 2023

作者： Shen, Cuifeng Deng, Zhaokun Wang, Jinzhi Wang, Shuangyi Chen, Chen Peking University State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Beijing100871 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong

ISBN: (纸本)9798350307320

Ultrasound scanning is an indispensable diagnostic tool in modern medicine, but obtaining an accurate standard plane relies heavily on the sonographer's skill and expertise. This paper presents a novel approach toward autonomous robotic ultrasound scanning using a reinforcement learning-based navigation method with volumetric ultrasound data. The proposed method enables an ultrasound robot to autonomously acquire high-quality 2D standard planes from patients, thereby reducing the need for human intervention. The core of the approach is a deep reinforcement learning algorithm that detects 2D standard planes within the 3D ultrasound volume and guides the ultrasound probe to extract high-quality standard planes. To assess the effectiveness of the 2D standard plane detection method, an evaluation was conducted using a realworld ultrasound dataset, and a comparison was made between its performance and that of other state-of-the-art autonomous approaches. The simulation experiment was also conducted to validate the autonomous robotic ultrasound acquisition workflow. Results demonstrate that the reinforcement learning-based volumetric data navigation method outperforms existing techniques in terms of standard plane accuracy and is very effective for volumetric data-based robotic navigation. © 2023 IEEE.

关键词： Diagnosis

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