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Grasp hypothesis generation for parametric object 3D point cloud models

Grasp hypothesis generation for parametric object 3D point c...

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international conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Karthik Mahesh Varadarajan Ishaan Gupta Markus Vincze Vision for Robotics Automation and Control Institute Technical University of Vienna Austria

Grasping by Components (GBC) is a very important component of any scalable and holistic grasping system that abstracts point cloud object data to work with arbitrary shapes with no apriori data. Superquadric representation of point cloud data is a suitable parametric method for representing and manipulating point cloud data. Most Superquadrics based grasp hypotheses generation methods perform the step of classifying the parametric shapes into one of different simple shapes with apriori established grasp hypotheses. Such a method is suitable for simple scenarios. But for a holistic and scalable grasping system, direct grasp hypothesis generation from Superquadric representation is crucial. In this paper, we present an algorithm to directly estimate grasp points and approach vectors from Superquadric parameters. We also present results for a number of complex Superquadric shapes and show that the results are in line with grasp hypotheses conventionally generated by humans.

关键词： Shape Grasping Fitting three dimensional displays Vectors Equations Solid modeling

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Domain Adaptation for Degraded Remote Scene Classification

Domain Adaptation for Degraded Remote Scene Classification

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international conference on Control, automation, robotics and Vision (ICARCV)

作者： Jianfei Yang Hailin Chen Yuecong Xu Ziji Shi Ruikang Luo Lihua Xie Rong Su School of Electical and Electronic Engineering Nanyang Technological University Singapore Spasee Technology Pte. Ltd. Singapore

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

Remote scene classification serves a vital role in many applications. However, satellite images are often blurred and degraded due to aerosol scattering under fog, haze, and other weather conditions, reducing the image contrast and color fidelity. State-of-the-art remote sensing classification models building upon convolutional neural networks (CNNs) are mostly trained on annotated datasets of clear satellite images. When applied to blurred images, they will suffer a great degradation in performance. To address this problem, we adopt the domain adaptation algorithm TADA and propose Transferable Attention enhanced Adversarial Adaptation Network (TA 3 N), which utilizes annotated data in clear images by applying knowledge transferring from clear image domain to blurred image domain. Our TA 3 N first integrates spatial attention to focus on salient areas which are discriminative and transferable. In addition, domain discriminator and adversarial training via gradient reversal layer are used to minimize the discrepancies in extracted features from clear and degraded domains. We synthesize degraded remote scene classification dataset SSI based on FoHIS model. Experiments on degraded SSI showed that TA 3 N significantly outperforms baseline and other state-of-the-art domain adaptation methods.

关键词： Feature extraction Remote sensing Satellites Task analysis Adaptation models Training Meteorology

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Atr loitmBaeo Deluy Trangulation and Its Improvement

Atr loitmBaeo Deluy Trangulation and Its Improvement

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IEEE international conference on Communication and Information Systems (ICCIS)

作者： Wenyu Li Senchun Chai School of Automation Beijing Institute of Technology Beijing China

ISBN: (数字)9798331531171

ISBN: (纸本)9798331531188

Path planning is an important part of robotics research. A * algorithm and RRT algorithm are very common algorithms in path planning. Each of these algorithms has its own advantages, but there is still room for improvement in planning time, path cost, and adaptability to different environment. In this paper, we propose a map processing method based on Delaunay triangulation, while applying the strategy of obstacle expansion and compensated sampling, and the A * algorithm is used on this basis. Finally, we have improved the path given by the A * algorithm with straight-first strategy. Simulations show that the A * algorithm based on Delaunay triangulation can plan paths efficiently in different environments.

关键词： Adaptation models Costs Simulation Optimization methods Path planning Planning Robots Information systems

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Linear Parameter Varying Model Predictive-Based Controller for Trajectory Tracking of High-Fidelity Autonomous Vehicle

Linear Parameter Varying Model Predictive-Based Controller f...

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automation, Control and robotics Engineering (CACRE), international conference on

作者： Kanani Kulwa-Juma Kanani B K Tiwari Tae-wan Kim Department of Naval Architecture and Ocean Engineering Seoul National University Seoul South Korea Department of Advance Computer Science and Engineering Vignan University Guntur India

ISBN: (数字)9798350350302

ISBN: (纸本)9798350350319

the tracking control of an unmanned autonomous vehicle (UA V) is one of the key factors determining the performance and effectiveness of autonomous navigation. A controller based on Linear Parameter Varying - Model Predictive Control (LPV - MPC) is designed to track the desired trajectory of the UAV. the Linear Parameter Varying (LPV) modeling and control strategy allows the representation of the UAV nonlinear complex model in a linear-like manner which can be used in a linear Model Predictive Control (MPC). the main advantage of the LPV -MPC technique is its ability to improve tracking solutions very close to nonlinear MPC but with significantly low computational costs, enabling LPV-MPC to be used for real-time operations. the simulation results of this paper show that the designed LPV-MPC performs well in tracking the desired trajectory through each waypoint. Furthermore, experiments with different numerical integration methods have been investigated, especially Euler's method, and the Runge-Kutta method. the results showed that the Runge-Kutta method performed better with smaller horizon periods, with minimum tracking errors and shorter elapsed time.

关键词： Trajectory tracking Simulation Urban areas Predictive models Autonomous aerial vehicles Real-time systems Numerical models

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Human-Led Robotic Transportation of Elastic Objects with Adaptive Control

Human-Led Robotic Transportation of Elastic Objects with Ada...

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international conference on robotics and automation Engineering (ICRAE)

作者： Zhenxi Cui Jiewen Lai Bo Lu Yi Guo Henry K. Chu School of Computing and Engineering University of West London London United Kingdom Department of Electronic Engineering Chinese University of Hong Kong Hong Kong China School of Mechanical and Electrical Engineering Soochow University Suzhou China Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken New Jersey United States Department of Mechanical Engineering The Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9798331518301

ISBN: (纸本)9798331518318

the perception and transportation of soft objects are critical tasks in daily life, and recent sensor advancements enable robots to safely perform these tasks in human-shared environments. this study advances elastic linear object transportation by introducing a collaborative framework where a human leads, providing task-specific instructions to guide the robot. Unlike conventional methods focused on fixed object manipulation, our approach dynamically controls and converges feature points of the elastic object into desired configurations during transportation. We developed an online model estimation technique using a least-squares optimization algorithm, with an exponential forgetting mechanism to adaptively update the model under disturbances from sensors and human interaction. Validated in experiments with a 6-DOF robot and depth camera, our method demonstrated robustness across varied scenarios, achieving faster convergence and reduced positional fluctuation compared to conventional gradient descent approaches.

关键词： Deformable models Adaptation models Shape Robot kinematics Transportation Estimation Robot sensing systems Stability analysis Robots thermal stability

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Use of Yolo Algorithm for Traffic Sign Detection in Autonomous Vehicles and Improvement Using Data Replication methods

Use of Yolo Algorithm for Traffic Sign Detection in Autonomo...

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international conference on Electronics, Computers and Artificial Intelligence (ECAI)

作者： Serkan Budak Firat Bozkaya Mustafa Yasin Akmaz Şükrücan Tiğlioğlu Cansel Boynukara Okan Kazanci Zülal Hilal Yildiz Budak Akif Durdu Cemil Sungur Electrical Electronics Engineering Konya Technical University Konya Türkiye Computer Engineering Konya Technical University Konya Türkiye Electrical Electronics Engineering Robotics Automation Control Laboratory (RAC-LAB) Konya Technical University Konya Türkiye

Autonomous vehicles use many technologies and methods to detect and act on surrounding objects. the most common among these technologies is an algorithm called YOLO (You Only Look Once). this algorithm quickly detects objects in an image and classifies these objects accurately. this study examines the use of the YOLO algorithm for signage detection in autonomous vehicles and how this algorithm can be improved. First of all, the basic principles and working mechanisms of the YOLO algorithm are explained. then, it is explained in detail how this algorithm can be used for plate detection in autonomous vehicles. Various models were trained using the YOLO algorithm and the data set created with real data, and the trained models were tested on real-time systems. Finally, suggestions for the improvement of the YOLO algorithm are presented and how this algorithm can be improved further in the future is discussed.

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the Comprehensive Evaluation Method for the Visibility Ability of ATCT Based on BP Network

The Comprehensive Evaluation Method for The Visibility Abili...

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Bo Wang Xiaofei Peng Jiaquan Ye Gang Su Chenyu Liu The Second Research Institute of Civil Aviation Administration of China Chengdu China Civil Aviation Flight University of China Guanghan China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

Airport Traffic Control Tower (ATCT) is one of the most important facilities at civil aviation airports. the location and height of ATCT will directly affect the controller's observation and judgment. In order to evaluate and compare the visibility capability of different tower construction schemes, a comprehensive evaluation method is set up. this method includes building an index system of obligatory and preference indicators, forming calculation methods for each indicator, and establishing a comprehensive evaluation model. In the evaluation model, two networks have been constructed based on Coefficient Strategy (CS) and Level Strategy (LS) respectively. Selecting a certain airport in East China as a case study, CS-BP and LS-BP network models of 5-10-1 in topology have been set up after 12000 typical samples for training respectively. the research results show that LS-BP network has the smaller Mean Squared Error (MSE) and greater regression coefficient compared to CS-BP network. And the results of CS-BP were similar to Traditional Strategy (TS). therefore, selecting LS-BP network to evaluate calculated indicators of given schemes and considering that scheme 3 was best. the entire method can serve as a reference for decision-making by air traffic control unit.

关键词： Training Service robots Network topology Poles and towers Evaluation models Lighting Traffic control Airports Topology Indexes

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Comparative Analysis of Listwise Reranking with Large Language models in Limited-Resource Language Contexts

Comparative Analysis of Listwise Reranking with Large Langua...

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Artificial Intelligence, robotics, and Communication (ICAIRC), IEEE international conference on

作者： Yanxin Shen Lun Wang Chuanqi Shi Shaoshuai Du Yiyi Tao Yixian Shen Hang Zhang Simon Fraser University Canada Duke University North Carolina USA University of California San Diego California USA University of Amsterdam Amsterdam Netherlands Johns Hopkins University Maryland USA

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

Large Language models (LLMs) have demonstrated significant effectiveness across various NLP tasks, including text ranking. this study assesses the performance of large language models (LLMs) in listwise reranking for limited-resource African languages. We compare proprietary models RankGPT3.5, Rank4o-mini, RankGPTo1-mini and RankClaude-sonnet in cross-lingual contexts. Results indicate that these LLMs significantly outperform traditional baseline methods such as BM25-DT in most evaluation metrics, particularly in nDCG@10 and MRR@100. these findings highlight the potential of LLMs in enhancing reranking tasks for low-resource languages and offer insights into cost-effective solutions.

关键词： Measurement Translation Costs Large language models Robots Context modeling

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Evolutionary Neural Architecture Search with Semi-supervised Accuracy Predictor

Evolutionary Neural Architecture Search with Semi-supervised...

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international conference on Control and Robots (ICCR)

作者： Songyi Xiao Bo Zhao Derong Liu School of Automation Guangdong University of Technology Guangzhou China School of Systems Science Beijing Normal University Beijing China Institute of Control Science and Technology Southern Unversity of Science and Technology Shenzhen China

Neural architecture search (NAS) has low efficiency in evaluating a large number of candidate architectures. As an efficient evaluation method, accuracy predictor-based NAS algorithms have become popular because the performance (accuracy) can be evaluated without training the candidate architectures. However, accuracy predictors still need some evaluated architectures that are difficult to train for achieving promising performance. In order to break this bottleneck, we investigate a semi-supervised accuracy predictor-based evolutionary NAS method (MSNAS) which requires only a small number of evaluated neural architectures. the accuracy predictor obtains high prediction performance by extracting the evaluated architectures, strong regressors and truncation mechanism. To find truly high-accuracy candidate architectures more easily, the multi-objective optimization method is presented to trade-off the prediction accuracy and confidence of candidate architectures. the MSNAS variants from different strong regressors are employed to validate the competitive performance of the MSNAS on NAS-Bench 201.

关键词： Training Radio frequency Optimization methods Predictive models Aerospace electronics Prediction algorithms Robots

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A State-of-Charge Estimation Method based on Bidirectional LSTM Networks for Lithium-ion Batteries

A State-of-Charge Estimation Method based on Bidirectional L...

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international conference on Control, automation, robotics and Vision (ICARCV)

作者： Zhen Zhang Ming Xu Longhua Ma Binchao Yu Collage of Control Science and Engineering (CSE) Zhejiang University Hangzhou PR China Institute of Automation and Electrical Engineering Zhejiang University Ningbo Institute of Technology Ningbo PR China Zhejiang Zheneng Natural Gas Operation Co. Hangzhou PR China

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

State-of-charge (SOC) estimation is the key to the BMS of lithium-ion batteries. At present, data-driven methods are more popularly used to estimate the SOC. Among them, the deep learning technology especially recurrent neural networks (RNNs) performed very well. In this paper, we proposed a bidirectional long short-term memory (Bi-LSTM) neural network to perform SOC estimation. the bidirectional LSTM layer is employed to catch the forward and backward temporal dependencies of battery sequential states, and shows better performance in SOC estimation of LiBs. With a public dataset of vehicle driving cycles, we separately train our proposed network at three different constant temperatures (0 °C, 10 °C, and 25 °C), and the evaluated MAEs are 0.498%, 0.411 %, 0.738%. Besides, the Bi-LSTM network trained with overall datasets at three temperatures achieves a mean absolute error (MAE) of 0.616% and maximum absolute error (MaxE) of 3.809%. It shows that the proposed method is robust and accurate in SOC estimation.

关键词： State of charge Batteries Estimation Temperature measurement Battery charge measurement Logic gates Recurrent neural networks

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