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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Goran Vasiljević Dean Martinović Stjepan Bogdan Faculty of Electrical Engineering and Computing Laboratory for Robotics and Intelligent Control Systems Unska 3 University of Zagreb Zagreb Croatia

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

In this paper, we present a method to control the UAV to follow the power line with a specific position and orientation based only on the measurement of the magnetic field generated by the current flow in the power line. In this way, it is possible to localize the UAV with respect to the power line without the need for additional sensors, even in poor visibility conditions. The measurements from four magnetometers attached to the UAV are used to solve an optimization problem that involves determining the relative pose of the UAV with respect to the power line. Based on the relative pose, the UAV is controlled to follow the power line in a predefined position and orientation. Experiments in a test setup have confirmed that the method is applicable in a realistic environment.

关键词： Location awareness Visualization Power measurement Magnetic field measurement Magnetometers Current measurement Position measurement Autonomous aerial vehicles Magnetic fields Optimization

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Enhancing Impulse Response-Based SSVEP Decoding Method by Incorporating Temporal Local Correlation 7

Enhancing Impulse Response-Based SSVEP Decoding Method by In...

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7th International Conference on Biological Information and Biomedical engineering, BIBE 2024

作者： Huang, Jiayang Wang, Jiaxi Yang, Pengfei Xiong, Bang Zhang, Zhi-Qiang School of Computer Science and Technology The Key Laboratory of Smart Human-Computer Interaction Wearable Technology of Shaanxi Province Xidian University China School of Electronic and Electrical Engineering Institute of Robotics Autonomous Systems and Sensing University of Leeds United Kingdom

ISBN: (纸本)9783800762910

Various training-based spatial filtering methods have been proposed to classify steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). However, many overlook the temporal instability of SSVEPs, leading to decreased classification performance. Therefore, we propose an SSVEP decoding method that calculates template signals and spatial filters using the subject's impulse responses and periodic impulses, incorporating the temporally local correlation coefficient in the SSVEP decoding process. Then, we employ a combined correlation coefficient for SSVEP classification. The proposed method addresses the temporal instability of SSVEPs, with template signals that exhibit strong individual characteristics. Utilizing a public benchmark dataset, we achieved a classification accuracy of 87.43% and an ITR of 256.1 bits/min at 1s time window, demonstrating the performance of this method. Compared to other classical spatial filtering methods, the proposed method demonstrates superior classification performance, which can enhance the practical usability of SSVEP-BCI systems. © VDE VERLAG GMBH.

关键词： Wiener filtering

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Control Aspects in Programming by Demonstation for Robotic Deep- Micro- Hole Drilling

Control Aspects in Programming by Demonstation for Robotic D...

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Mediterranean Conference on Control and Automation (MED)

作者： Bruno Maric Frano Petric Matko Orsag LARICS Laboratory for Robotics and Intelligent Control Systems Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

This paper explores control aspects in a collab-orative robotic system designed to replicate human operators' drilling skills in deep- micro- hole tasks, focusing on the glass-container mould industry. To replicate human performance, we develop a single-DOF Impedance-based drill controller for a standard industrial position-controlled robot, enhancing safe and smooth interactions between the drill bit and the mould. To address high feed rates observed in worker demonstrations and prevent bit breaks, we focus on minimizing non-axial motion during drilling on an off-the-shelf available industrial manipulator KUKA KR 10. This involves analyzing the impact of joint dynamics on drill motion and designing a control system for the robot's low-level control to achieve axial drill motion. We show that the proposed joint control system that takes into account relationship between joint and tool dynamics outperforms available robot low-level control in minimizing non-axial motion, leading to the robot being able to outperform the human in deep- micro- hole drilling.

关键词： Drilling Service robots Dynamics Robot control Time factors Synchronization Task analysis

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Learning Rat-Like Behavior for a Small-Scale Biomimetic Robot

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engineering 2022年第10期17卷 232-243页

作者： Zihang Gao Guanglu Jia Hongzhao Xie Qiang Huang Toshio Fukuda Qing Shi Key Laboratory of Biomimetic Robots and Systems(Beijing Institute of Technology) Ministry of EducationBeijing 100081China Intelligent Robotics Institute School of Mechatronical EngineeringBeijing Institute of TechnologyBeijing 100081China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of TechnologyBeijing 100081China

Existing biomimetic robots can perform some basic rat-like movement primitives(MPs)and simple behavior with stiff combinations of these *** mimic typical rat behavior with high similarity,we propose parameterizing the behavior using a probabilistic model and movement ***,an analysis of fifteen 10 min video sequences revealed that an actual rat has six typical behaviors in the open field,and each kind of behavior contains different bio-inspired combinations of eight *** used the softmax classifier to obtain the behavior-movement hierarchical probability ***,we specified the MPs using movement parameters that are static and *** obtained the predominant values of the static and dynamic movement parameters using hierarchical clustering and fuzzy C-means clustering,*** predominant parameters were used for fitting the rat spinal joint trajectory using a second-order Fourier series,and the joint trajectory was generalized using a back propagation neural network with two hidden ***,the hierarchical probability model and the generalized joint trajectory were mapped to the robot as control policy and commands,*** implemented the six typical behaviors on the robot,and the results show high similarity when compared with the behaviors of actual rats.

关键词： Biomimetic Bio-inspired robot Neural network learning system Behavior generation

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AI-Enhanced Structural Health Monitoring with a Multi-Rotor Aerial Vehicle

AI-Enhanced Structural Health Monitoring with a Multi-Rotor ...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Filip Zorić Ana Milas Tamara Petrović Zdenko Kovačić Matko Orsag Faculty of Electrical Engineering and Computing LARICS Laboratory for Robotics and Intelligent Control Systems University of Zagreb Zagreb Croatia

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

Roads, bridges, tunnels, railways and canals are crucial for the transportation of goods and people in modern society. Modern infrastructure is subject to damage and requires therefore regular inspection and maintenance to remain safe and functional. While this can be done manually, large parts of the mentioned infrastructure are hardly accessible, which is a main motivational factor for the use of multi-rotor aerial vehicles (MAV s) for inspection and maintenance tasks. MAV s, paired with novel deep learning computer vision techniques, such as instance segmentation, are emerging as an obvious choice for automating certain parts of structural health monitoring (SHM). In this manuscript, we provide an overview of available SHM datasets and how they can be utilized for the task of autonomous crack detection. We use available datasets to train and compare two neural network architectures for instance segmentation. The segmented crack instances are then localized in a global coordinate frame to perform autonomous mapping of the potentially dangerous infrastructure defects. Experimental studies demonstrate the effectiveness of the proposed SHM approach, which results in precisely localized infrastructure defects marked on the global map.

关键词： Instance segmentation Irrigation Structural panels Neural networks Transportation Inspection Rail transportation

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Active Scout: Multi-Target Tracking Using Neural Radiance Fields in Dense Urban Environments

Active Scout: Multi-Target Tracking Using Neural Radiance Fi...

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

作者： Christopher D. Hsu Pratik Chaudhari DEVCOM Army Research Laboratory Department of Electrical & Systems Engineering and General Robotics Automation Sensing and Perception (GRASP) Laboratory University of Pennsylvania

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

We study pursuit-evasion games in highly occluded urban environments, e.g. tall buildings in a city, where a scout (quadrotor) tracks multiple dynamic targets on the ground. We show that we can build a neural radiance field (NeRF) representation of the city—online—using RGB and depth images from different vantage points. This representation is used to calculate the information gain to both explore unknown parts of the city and track the targets—thereby giving a completely first-principles approach to actively tracking dynamic targets. We demonstrate, using a custom-built simulator using Open Street Maps data of Philadelphia and New York City, that we can explore and locate 20 stationary targets within 300 steps. This is slower than a greedy baseline, which does not use active perception. But for dynamic targets that actively hide behind occlusions, we show that our approach maintains, at worst, a tracking error of 200m; the greedy baseline can have a tracking error as large as 600m. We observe a number of interesting properties in the scout’s policies, e.g., it switches its attention to track a different target periodically, as the quality of the NeRF representation improves over time, the scout also becomes better in terms of target tracking.

关键词： Target tracking Urban areas Buildings Games Neural radiance field Information filters Probabilistic logic Mutual information Intelligent robots Quadrotors

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Risk Assessment Based Time-adjusted Optimization for Safe and Fast Flight of Quadrotor UAVs

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhu, Hai Li, Baoquan Yin, Haolin Zhang, Xuebo Tiangong University School of Control Science and Engineering The Tianjin Key Laboratory of Intelligent Control of Electrical Equipment Tianjin300387 China Nankai University Institute of Robotics and Automatic Information Systems The Tianjin Key Laboratory of Intelligent Robotics Tianjin300071 China

Generating an executable reference trajectory in realtime is a core of autonomous navigation for a quadrotor unmanned aerial vehicle (UAV). In the paper, a timeadjusted optimization method is proposed for UAV motion planning to achieve safe and fast flight. Firstly, based on parameterization for uniform B-spline trajectories, relationship of knot spans and UAV speeds is analyzed, and an iterative velocity restrict algorithm is introduced to tackle nonexecutable flight. Subsequently, combining start and target points in local planning, an evaluation criterion function is designed with control points of the above trajectory and directions of collision avoidance. On this basis, risk assessment is conducted according to surrounding environments, where four phases of passing obstacles is constructed by considering risk level variation. Then, the weight function in each phase is determined by relationship of obstacle and planned local trajectory. Moreover, time intervals of B-spline trajectories are adjusted to obtain nonuniform trajectories, so as to affect UAV speeds. Simulation and experiment are conducted with comparison to verify performance of the proposed method. © 2025 IEEE.

关键词： Risk assessment

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Resilient Event-Triggered Model Predictive Control for Adaptive Cruise Control Under Sensor Attacks

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 807-809页

作者： Zhijian Hu Rong Su Kai Zhang Zeyuan Xu Renjie Ma School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore Key Laboratory of Ultra-Precision Intelligent Instrumentation Ministry of Industry and Information Technologyand the Center of Ultra-Precision Optoelectronic Instrument EngineeringHarbin Institute of TechnologyHarbin 150001China Department of Control Science and Engineering Harbin Institute of TechnologyHarbin 150001China Department of Chemical and Biomolecular Engineering National University of SingaporeSingapore 117585Singapore State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150001China IEEE

Dear Editor,This letter addresses the resilient model predictive control(MPC)problems for adaptive cruise control(ACC)systems under sensor *** the light of vulnerabilities of ACC systems to sensor attacks,an intrusion detection mechanism is proposed at the controller side to distinguish abnormal ***,the robust control gains are derived to design the terminal region constraint for MPC.

关键词： Trigger Event constraint

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Collision-Aware MPCC Integrated with Neural Augmentation for Quadrotor Trajectory Tracking

Collision-Aware MPCC Integrated with Neural Augmentation for...

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Hu, Chuanqi Wang, Jin Chen, Yikai Chen, Yonghang Zheng, Zhi Meng, Jun Lu, Guodong Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou310058 China Zhejiang University Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Hangzhou310058 China Robotics Research Center of Yuyao City Ningbo315400 China National Elite Institute of Engineering Chongqing University Chongqing401135 China School of Mechanical Engineering Zhejiang University Hangzhou310058 China College of Electrical Engineering Zhejiang University Hangzhou310027 China Center for Data Mining and Systems Biology College of Electrical Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9798331533892

This paper presents a trajectory tracking algorithm designed for autonomous quadrotors to navigate complex dy-namic environments that is resilient to collision disturbances. Since it combines Model Predictive Contouring Control (MPCC) with the capabilities of neural networks, our method is named MPCC Integrated with Neural Augmentation (MINA). By equip-ping the quadrotor with PVDF piezoelectric sensors, we achieve real-time perception of collision forces and utilize S4D-Conv Adaptive Net Architecture (SANA) for state computation, a network that integrates the advantages of S4D and convolutional kernels to enhance the accuracy of state predictions. We incorporate the predicted results into the MPCC framework to optimize the thrust output of the quadrotor for the next time step, ensuring precise trajectory tracking. Experimental validation demonstrates the reliability and effectiveness of our algorithm, providing an innovative solution for autonomous flight and trajectory tracking of quadrotors in complex dynamic environments. © 2025 IEEE.

关键词： Flight paths

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Towards AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

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IEEE Internet of Things Journal 2025年第11期12卷 16990-16997页

作者： Ahmed, Imran Ahmad, Misbah Siddiqi, Muftooh Ur Rehman Chehri, Abdellah Jeon, Gwangill Anglia Ruskin University School of Computing and Information Science Cambridge United Kingdom University of West of England Centre for Machine Vision Bristol Robotics Laboratory Bristol United Kingdom Hartpury University Department of Animal and Agriculture Gloucester United Kingdom Aston University College of Engineering and Physical Sciences Birmingham United Kingdom Department of Mathematics and Computer Science Canada Incheon National University Department of Embedded Systems Engineering Incheon Korea Republic of

In the rapidly advancing field of intelligent transportation systems, integrating Artificial Intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model's effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model's capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision-making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step towards recognising the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology. © 2025 IEEE.

关键词： Artificial intelligence (AI) autonomous vehicles deep leaning edge computing Internet of Vehicles (IoV)

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