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Edge Detection and Alignment control Method for Mobile Robots based on multi-Lidar Fusion 5

Edge Detection and Alignment Control Method for Mobile Robot...

引用

5th World Robot Conference symposium on Advanced Robotics and automation, WRC SARA 2023

作者： Gao, He Tao, Yong Liu, Xiaonan Zhang, Yufan Liu, Haitao Wan, Jiahao Beihang University Research Institute of Aero-Engine Beijing102206 China Beijing New Materials and New Energy Technology Development Promotion Center Administrative Commission of Zhongguancun Science Park Beijing100191 China Beihang University School of Large Aircraft Engineering Beijing100191 China Beihang University School of Mechanical Engineering and Automation Beijing100191 China

ISBN: (纸本)9798350307320

Currently, there is a growing demand for intelligent maintenance of large-scale infrastructure, prompting a shift from manual to semi-automatic solutions in the control schemes of inspection robots. Mobile inspection robots play a crucial role in tasks such as flip-over inspection, fall prevention warning, defect identification, all of which necessitate edge detection and alignment processes. Laser alignment, with its high precision and low computational complexity, offers significant advantages over manual observation and visual alignment methods. This paper addresses practical engineering requirements by proposing a lidar-based structural edge detection algorithm and an automatic alignment controller. To validate the effectiveness of the proposed method, edge detection and alignment control experiments were conducted in a simulated environment. The results show that the edge detection algorithm reduces the direction detection error to 2.7°. The developed alignment controller exhibits a convergence time approximately 40% shorter than the standard PID controller. The proposed scheme effectively meets the accuracy and real-time requirements for edge detection and automatic alignment functions in mobile inspection robots and holds significant application potential for the infrastructure project's intelligent maintenance. © 2023 ieee.

关键词： Alignment

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Ultrasound Speed of Sound Reconstruction Based on Local Event Slopes of Synthetic Aperture Data

Ultrasound Speed of Sound Reconstruction Based on Local Even...

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2023 ieee International Ultrasonics symposium, IUS 2023

作者： Wang, Yonghao Lu, Wenkai Li, Yuxuan Tsinghua University Department of Automation Beijing China China Tsinghua University Institute for Precision Medicine China

ISBN: (纸本)9798350346459

Sound speed estimation in medical pulse-echo ultrasound imaging is a critical area of research with profound implications for disease diagnosis. However, traditional methods, such as tomographic reconstruction and layered media modeling, either involve substantial computational complexity or fail to account for lateral sound speed variations. This study introduces a new, efficient method for sound of speed (SoS) estimation, rooted in local event slope analysis of common middle point (CMP) gathers within synthetic aperture ultrasound channel data. This method facilitates direct estimation of tissue sound speed distribution, thus providing valuable quantitative parameters for clinical evaluations. Distinctly, our method yields a higher resolution velocity spectrum compared to conventional stacking velocity spectra, and effectively manages lateral velocity variations, catering to clinical scenarios with lateral sound speed heterogeneities. Ultimately, our proposed technique holds significant potential for enhancing ultrasound imaging quality and diagnostic precision. © 2023 ieee.

关键词： Synthetic apertures

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Model Predictive control of Industrial Trucks with AI-based Plant Model Selection 9

Model Predictive Control of Industrial Trucks with AI-based ...

引用

9th International Conference on control, Decision and Information Technologies, CoDIT 2023

作者： Sauer, Timm Spielmann, Luca Gorks, Manuel Zindler, Klaus Jumar, Ulrich University of Applied Sciences Aschaffenburg Germany Ifak - Institute of Automation and Communication Magdeburg Germany

ISBN: (纸本)9798350311402

In this paper, a new control concept for automatic track guidance of forklifts within a heterogeneous logistics fleet is presented. The proposed control scheme is universally applicable and based on Artificial intelligence (AI). The lateral control is realized using a Model Predictive controller (MPC). In order to take into account the diversity of the industrial truck variants, a multi-model approach is used. Therefore, a plant model for each truck variant is integrated into the MPC. In a practical application, the most suitable model has to be selected. This decision is based on AI in the form of an Artificial Neural Network (ANN). It is able to distinguish the different truck variants based on that part of the plant's state vector, which represents the vehicle dynamic characteristics. Thus, the appropriate model can be used, which significantly improves the control quality and guarantees an accurate track guidance of different forklifts. Due to the AI-based selection of the model, the computational effort can be kept low and real-time capability can be ensured. In order to classify the performance of the proposed control concept, its simulation results are compared to the closed-loop behavior, using a classical MPC. © 2023 ieee.

关键词： Robust control

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Autonomous Sprinkler System with MAPE-K

Autonomous Sprinkler System with MAPE-K

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2022 International Conference on computational Science and computational intelligence, CSCI 2022

作者： Kim, Mira Jin, Jennifer International Business Machines Corporation IBM Automation Costa MesaCA92626 United States California State University Computer Science and Engineering San BernardinoCA92407 United States

ISBN: (纸本)9798350320282

Sprinkler is a key component for farms and gardens. However, conventional sprinkler systems have limitations: underwatering and overwatering under evolving weather, not considering plant conditions, and burden of manual scheduling. We developed Autonomous Sprinkler System to remedy the limitations. We acquire environmental contexts with loT sensors, cameras, and actuators, and apply MAPE-K for autonomic control. We implemented the Knowledge with a SVM classifier for inferring the situations and a Fast R-CNN model for determining plant condition. Our experiments show the system yields a considerable savings of water consumption and promotes the plant health, all without human users' interventions. © 2022 ieee.

关键词： Machine learning

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Observation-oriented Water Distribution Model for Proton Exchange Membrane Fuel Cell 7

Observation-oriented Water Distribution Model for Proton Exc...

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7th CAA International Conference on Vehicular control and intelligence, CVCI 2023

作者： Li, Jisen Chen, Qihong Zhang, Liyan Xie, Liang School of Automation Wuhan University of Technology Wuhan China School of Sciences Wuhan University of Technology Wuhan China

ISBN: (纸本)9798350340488

As a critical parameter in proton exchange membrane fuel cell (PEMFC), water distribution is closely interrelated with the performance and life of PEMFC. The existing models cannot characterize water distribution states accurately without a high computational load. Therefore, this paper proposed a one-dimensional water distribution model with the consideration of two-phase water flow model in gas diffusion layer (GDL). With the model, the lumped water content or concentration are determined by ordinary differential equation (ODE) of several primary nodes, which is low computing resource usage and convenient for state observers designing. In addition, the water distribution throughout the region is given by the analytic equations which differ with different water distribution regime. In the end, the accuracy and complexity of proposed model is verified by the co-simulation between COMSOL and Simulink. © 2023 ieee.

关键词： co-simulation PEM fuel cell two-phase water flow water distribution model

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Variable search domain improved A-star algorithm for AMR in warehouse environments 7

Variable search domain improved A-star algorithm for AMR in ...

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7th CAA International Conference on Vehicular control and intelligence, CVCI 2023

作者： Li, Wei Cong, Yongzheng Du, Haibo Zhu, Wenwu School of Electrical Engineering and Automation Hefei University of Technology Hefei China

ISBN: (纸本)9798350340488

In order to reduce autonomous mobile robot path planning time and improve efficiency in the warehouse environment, the variable search domain improved A-star algorithm is proposed. Firstly, a raster map matrix is created based on the actual map, then inflection point features and path information are extracted to set up a search template, and finally, different search steps and search directions are adaptively obtained from the search template according to the location information of the nodes to be expanded, simplifying the search space and improving the computational efficiency of the A-star algorithm. The traditional A-star algorithm and the improved A-star algorithm are compared and tested respectively. The simulation test results show that the variable search domain A-star algorithm proposed in this paper has fewer traversing nodes, shorter search time, and better performance. © 2023 ieee.

关键词： A-star algorithm autonomous mobile robot path planning variable search domain

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Distributed Multi-Agent Deep Reinforcement Learning based Navigation and control of UAV Swarm for Wildfire Monitoring 4

Distributed Multi-Agent Deep Reinforcement Learning based Na...

引用

4th ieee Annual Flagship India Council International Subsections Conference, INDISCON 2023

作者： Ali, Adeeba Ali, Rashid Baig, M.F. Aligarh Muslim University Department of Computer Engineering Aligarh India Aligarh Muslim University Department of Mechanical Engineering Aligarh India

ISBN: (纸本)9798350333558

Decentralized deep reinforcement learning is an emerging and most effective approach to solve the problem of resource allocation and coordination among the swarm of UAVs. Nowadays, the use of autonomous aerial vehicles in wildfire monitoring is increasing and considered as the reasonably feasible option as surveillance in calamity-hit areas can benefit from this kind of automation. The flocks of UAVs can generate maps of affected areas which could improve the process of relief planning so that necessary aid can be reached the burnt areas quickly. This paper presents the Multi-agent Deep Q network-based technique for planning optimized trajectories for the UAV swarm which can sense the wildfire in the forests and nearby regions. In this work, the UAV agents are trained over simulated wildfires in virtually generated forests with two reward schemes. The simulation results verify the effectiveness of the proposed strategy for leveraging it in real-world scenarios. © 2023 ieee.

关键词： Unmanned aerial vehicles (UAV)

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Validation of a Limit Ellipsis controller for Rescue Drones 16

Validation of a Limit Ellipsis Controller for Rescue Drones

引用

16th ieee International symposium on Applied computational intelligence and Informatics, SACI 2022

作者： Varga, Balint Doer, Christopher Trommer, Gert F. Hohmann, Soren Institute of Control Systems Karlsruhe Institute for Technology Karlsruhe76131 Germany

ISBN: (数字)9781665481250

ISBN: (纸本)9781665481250

In recent years, more and more robotic systems have been supporting rescue forces in their missions. This paper presents the control algorithm and its application of an unmanned areal vehicle (UAV), which can support emergency personnel in their work. An adaption of the so-called Limit Ellipsis controller (LEC) for indoor UAV is proposed. This adaptation enables the LEC to be used in semi-structured environments with static and dynamic obstacles. The main benefit of the LEC is that it can prevent deadlocks caused by other methods in complex environments. Furthermore, the LEC function is implemented on an experimental UAV system and tested in various environments. The proposed technical system enables exploration of a building by a semi-autonomous UAV, saving valuable rescue time. The UAV can fly into the building and explore the interior without collision. The results show that the proposed controller can adequately avoid local minima, guide the UAV to the desired target, and provide essential information for the rescue team in real demonstration scenarios. © 2022 ieee.

关键词： controllers

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Performance Modeling for Power Converters with Light Gradient Boosting Machine 2

Performance Modeling for Power Converters with Light Gradien...

引用

2nd ieee International Power Electronics and Application symposium, PEAS 2023

作者： Li, Xinze Lin, Fanfan Zhang, Xin Liu, Xueqi Ma, Hao Nanyang Technological University Electrical and Electronic Engineering Singapore ZJU-UIUC Institute Zhejiang University Haining China College of Electrical Engineering Zhejiang Univeristy Hangzhou China

ISBN: (纸本)9798350313765

Performance modeling of power converters is indispensable for the design, control, and maintenance of power electronics systems. Conventional knowledge-based modeling approaches suffer from low accuracy and heavy computation burden induced by heavy human involvements. Data-driven modeling methods have emerged as potential solutions, leveraging empirical data to automatically extract functional relationships between input variables and output objectives. However, existing data-driven methods face challenges in terms of generalization capability, computational cost, and model interpretability. To address these challenges, this paper proposes the use of the light gradient boosting machine (LightGBM), a high-performance ensemble learning algorithm for performance modeling. LightGBM is a gradient boosting decision tree that utilizes gradient-based sampling and feature bundling techniques for efficient training and pruning. The paper applies LightGBM to model the efficiency and current stress of a multi-level DAB converter under a hybrid modulation, which is compared with several prevalent benchmark algorithms. 2-kW hardware experiments comprehensively validate the superiority and feasibility of the LightGBM for performance modeling. © 2023 ieee.

关键词： artificial intelligence dc-dc converter ensemble learning light gradient boosting machine performance modeling

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MILP-Based Optimization of the Extended Real- Time Railway Traffic Management Problem 16

MILP-Based Optimization of the Extended Real- Time Railway T...

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16th ieee International symposium on Applied computational intelligence and Informatics, SACI 2022

作者： Lindenmaier, Laszlo Aradi, Szilard Becsi, Tamas Levetei, Istvan Ferenc Budapest University of Technology and Economics Dept. of Control for Transportation and Vehicle Systems Budapest Hungary

ISBN: (数字)9781665481250

ISBN: (纸本)9781665481250

The conflict-free preplanned railway timetable often results in conflicting trains due to traffic perturbation. Therefore, the real-time railway traffic management problem arises that requires the rerouting and rescheduling of the trains. The optimization aims to minimize the secondary delays assigned to the trains solving the traffic management problem. In this paper, we propose a mixed-integer linear programming model extending the previous works in this field with a safety-relevant signaling concept, the so-called overlaps. The proposed model provides a strategic decision minimizing the delay propagation that is safer and more feasible considering the actual railway traffic regulations. The results are verified through a simulation environment highlighting the impact of overlaps in different infrastructures and scenarios. © 2022 ieee.

关键词： Integer programming

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