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Indoor Obstacle Avoidance System Design and Evaluation Using Deep Learning and SLAM-Based Approaches 1

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Computing Conference

作者： Benzaoui, Messaouda Benbekhma, Abdelwadoud Taibi, Houssam Eddine QF5F G3G Boumerdes Inst Elect & Elect Engn Boumerdes Algeria

ISBN: (数字)9783031622816

ISBN: (纸本)9783031622830;9783031622816;9783031622809

We aim in this paper to design a robust and cost-efficient system specifically targeted at indoor mobile robot obstacle avoidance. The system incorporates the fusion of 2D LiDAR-based Simultaneous Localization and Mapping (SLAM) with a Rapidly Exploring Random Trees (RRT) algorithm for effective path planning. Furthermore, we propose an innovative and pioneering approach for obstacle avoidance based on deep learning. The deep learning model is trained using data collected from a simulated environment utilizing a 2D LiDAR sensor, which serves both for SLAM and data acquisition purposes. This paper includes a comparative analysis of odometry-based and SLAM-based pose computation methods, providing valuable insights into the data collection and training procedures essential for successful deep learning-based obstacle avoidance. The implementation is carried out within the Robot Operating System 2 (ROS2) interface.

关键词： ROS Deep learning Path planning Dynamic obstacle avoidance

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LAURON VP, A Six-Legged Walking Robot, with a New ROS 2-Based Behavior Control 27th

LAURON VP, A Six-Legged Walking Robot, with a New ROS 2-Base...

引用

27th International Conference on Climbing and Walking Robots and Mobile Machine Support Technologies (CLAWAR)

作者： Plasberg, Carsten Nessau, Hendrik Sinha, Kantesh Hafeez, Imama Kubeneck, David Heppner, Georg Dillmann, Ruediger FZI Forschungszentrum Informat Haid & Neu Str 10-14 D-76131 Karlsruhe Germany

ISBN: (纸本)9783031713002;9783031713019

Hexapods have the ability to exert a strange fascination on humans. Once working with such a system, they develop a personality, and engineers create a special bond with the robot. Emerging from such a bond, LAURON V requires some upgrades to remain a future-proof system and to further prove its unused potential. Those changes contain new motor controllers but, more importantly, a reimplementation of the behavior network in an up-to-date framework. Therefore, an approach is developed on how to model behavior networks using ROS 2. This approach is then tested on the updated robot LAURON VP. With the ability to use the same software stack with only a different option during startup to run either on the robot or in simulation, testing new features becomes effortless.

关键词： Walking Robot Behavior-Based Control ROS 2

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A Simple Recommendation Model Using the Item's Global Popularity and Frequency-Based User Preference

A Simple Recommendation Model Using the Item's Global Popula...

引用

International Conference on Artificial Intelligence: Theory and Applications (AITA)

作者： Suvvari, Somaraju Ahmad, Md Iftekhar Natl Inst Technol Patna Patna Bihar India

ISBN: (纸本)9789819984787;9789819984794

Next-basket recommendation (NBR) is one of the emerging methods in the recommendations system and it has many real-world applications, like where you want to predict the set of items in the next transaction based on its previous continuous transactions. In the literature, many algorithms are proposed for the next-basket recommendation, but very little attention is given to the frequency information in making the recommendation model. In this paper, we presented a simple recommendation model, and in this model, the user representation is done based on the frequent purchasing information, and finally, when the recommendation is made, it considers the user representation and items global support. We made experiments on publicly available three real-time datasets and compared them with two of the existing recommendation algorithms using recall as a measure, and our algorithm outperforms the existing two models. The proposed algorithm is simple in terms of its complexity and the number of operations involved.

关键词： Next-basket recommendation User general preference Collaborative filtering

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Trajectory Planning of Mobile Robot for Obstacle Avoidance Considering Time and Path Length 1

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18th International Conference on Intelligent Autonomous systems (IAS)

作者： Yorozu, Ayanori Yushu, Zou Ohya, Akihisa Univ Tsukuba Inst Syst & Informat Engn 1-1-1 Tennodai Tsukuba Ibaraki 3058573 Japan Univ Tsukuba Grad Sch Sci & Technol 1-1-1 Tennodai Tsukuba Ibaraki 3058573 Japan

ISBN: (数字)9783031448515

ISBN: (纸本)9783031448508;9783031448515

For a mobile robot trajectory planning, the arrival time and the path length to the goal are two important cost factors when there is no conflict among moving obstacles. This study presents an obstacle avoidance trajectory planning method that considers the importance of arrival time and path length to a goal point in 3D X-Y-T configuration space. The trajectory cost function consisting of arrival time and the path length to the goal point is introduced. By expressing the cost function in terms of the time at which the robot avoids moving obstacles, the trajectory planning is formulated as an optimization problem. To verify the effectiveness of considering both time and path length, several simulations and experiments are carried out.

关键词： Trajectory planning Mobile robot Obstacle avoidance

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Hybrid Algorithm of Adhesive Joint Shape Optimization

Hybrid Algorithm of Adhesive Joint Shape Optimization

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International Scientific and Technical Conference on Integrated Computer Technologies in Mechanical Engineering - Synergetic Engineering (ICTM)

作者： Kurennov, Serhii Barakhov, Kostiantyn Taranenko, Igor Poliakov, Olexandr G. Barakhova, Hanna Vernadska, Kristina Natl Aerosp Univ Kharkiv Aviat Inst Kharkiv Ukraine

ISBN: (纸本)9783031614149;9783031614156

The subject of this article is the algorithm for optimization of symmetrical adhesive lapped joint. The goal of the paper is development of the method for optimization problem solving, which allows to unite high rate of calculations with stability of obtained results. This goal can be reached by means of two algorithms of optimization - genetic algorithms implemented on the original stage and swarm of particles algorithm - on the last stage of optimization. The problem of optimization is in finding optimal shape of doublers, i.e. doubler length and function of thickness variation along joint length. To describe stress-strain state of a joint modified Holland-Reissner model is used. To solve direct problem of structural stress state estimation the finite elements method is used. For optimization problem solving combination of multi-population model of genetic algorithm and swarm of particles algorithm are used. Introduction of individuals from other populations to the considered one allows to escape of homogenization of genotypes in separate population and premature breakage of optimization process. To describe doubler shape thickness variation function development of Fourier series are used. Above-mentioned implemented methods allow to create algorithm for topologic optimization which unites advantages of both methods and find solution of considered problem quite quickly. Duration of algorithm realization on Python language requires several minutes only to find optimal parameters.

关键词： Glued Joint Optimization Genetic Algorithm Particle Swarm Optimization

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Predicting the Duration of User Stories in Agile Project Management 21st

Predicting the Duration of User Stories in Agile Project Man...

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21st International Conference on Smart Technologies and Education (STE) on Smart Technologies for a Sustainable Future

作者： Raza, Asif Espinosa-Leal, Leonardo VizTrend OY Espoo 02620 Finland Arcada Univ Appl Sci Grad Sch & Res Jan Magnus Janssons Pl 1 Helsinki 00560 Finland

ISBN: (纸本)9783031619045;9783031619052

Effective effort estimation in agile project planning is vital because it helps organizations build product plans that they can stick to, have shorter turn-around time, and have better cost discipline. Machine learning can play an essential role in planning and estimating the project schedule. In this paper, a series of supervised machine learning models were studied, analyzed, and implemented to solve the problem of predicting effort estimates in Agile Scrum. The obtained results are compared with similar previous studies. We performed experiments using different Natural Language Processing (NLP) methods such as Term Frequency-inverse document frequency (TF-IDF), fastText, and different Neural networks, including Recurrent Neural networks (RNN), Long Short Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT). The trained models were fitted with three publicly available datasets. Our findings show that fastText (with a pre-trained model) significantly performed better in predicting the story-points of user-stories. The second-best performing model was bidirectional LSTM. Moreover, distilBERT performs poorly among all the models analyzed. This study can pave the way for organizations to benefit from these machine learning models and accurately predict project deadlines and schedules.

关键词： Agile Scrum Effort Estimation NLP Project Management fastText TF-IDF BERT distilBERT bi-LSTM

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Adaptable Segmented Straps with Anti-slip Pads for Modular Assistive Knee Exoskeleton Enhancement 27th

Adaptable Segmented Straps with Anti-slip Pads for Modular A...

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27th International Conference on Climbing and Walking Robots and Mobile Machine Support Technologies (CLAWAR)

作者： Tarapongnivat, Kanut Punapanont, Sanpoom Janna, Run Manawakul, Matas Mahuttanatan, Suksakaow Akkawutvanich, Chaicharn Manoonpong, Poramate Vidyasirimedhi Inst Sci & Technol VISTEC Rayong Thailand

ISBN: (纸本)9783031713002;9783031713019

This paper introduces a modular assistive knee exoskeleton designed to enhance physical gait assistance. The exoskeleton is engineered to support flexion/extension motions of the knee joint to mitigate slippage across various activities (walking, sit-stand, and walking up-down stairs) and accommodate personalized adjustments. A specific design approach involving a flexible strap with an anti-slip pad feature is proposed to address these requirements. The results indicate that our design is more effective in maintaining its position than the traditional one in all testing activities and can reduce a peak torque at the extreme slipping position during walking by 47% compared to the traditional design. This research contributes to the advancement of assistive technologies for mobility support and offers insights into the practical implementation of modular exoskeleton systems.

关键词： Exoskeleton Rehabilitation Anti-slip mechanism

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Performance Analysis of Synthetic Events via Visual Object Trackers

Performance Analysis of Synthetic Events via Visual Object T...

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Computing Conference

作者： Alansari, Mohamad AlRemeithi, Hamad Alansari, Sara Werghi, Naoufel Javed, Sajid Khalifa Univ Dept Elect Engn & Comp Sci Abu Dhabi U Arab Emirates Tawazun Technol & Innovat Abu Dhabi U Arab Emirates Khalifa Univ Ctr Autonomous Robot Syst Abu Dhabi U Arab Emirates Khalifa Univ Ctr Cyber Phys Syst Abu Dhabi U Arab Emirates

ISBN: (纸本)9783031622717;9783031622694;9783031622687

This study investigated the effectiveness of synthetic events in enhancing the accuracy of visual object trackers, particularly in scenarios where conventional RGB data encounters difficulties, such as with rapidly moving objects, motion blur, and varying lighting conditions, thereby highlighting the potential of event cameras in tracking applications. Synthetic events were generated from RGB Visual Object Tracking (VOT) datasets using the v2e toolbox and post processed in Inivation Dynamic Vision (DV) software. This post processed data was subsequently fused with traditional RGB data. Evaluation was conducted through the Pytracking library to measure potential tracking improvements. The results showed a notable increase in tracking efficacy upon the integration of post processed synthetic events with RGB data. Conclusively, synthetically generated events have the capacity to augment current state-of-the-art (SOTA) VOT frameworks with minimal Neural Network (NN) adjustments.

关键词： Event cameras Fusion technique Neuromorphic vision Visual object tracking (VOT)

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Using Natural Language Processing and Data Mining for Forecasting Consumer Spending Through Social Media

Using Natural Language Processing and Data Mining for Foreca...

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Intelligent systems Conference (IntelliSys)

作者： Mostafa, Noha Abdelazim, Kholoud Grida, Mohamed British Univ Egypt Cairo 11837 Egypt Zagazig Univ Zagazig 44519 Sharkia Egypt

ISBN: (纸本)9783031477171;9783031477188

With the considerable rise in social media users, the platforms are no longer limited to sharing blog posts and emotions and making online friends. It is currently used for quite larger purposes which have created what is called the Big Data. This research paper covers one of the most recent studies in forecasting, which is forecasting through social media. Social media forecasting is a very promising yet controversial technique that numerous researchers have been quite interested to investigate. With the Big Data challenges, social media forecasting requires a few advanced machine learning and social media analytic techniques such as: data mining and sentimental analysis. The objective of this research covers the study of forecasting consumer intentions and understanding whether it can be possible to make future predictions about consumers' spending from the enormous data shared every day. Data used in the experimental procedures of this research was collected in two methods, public surveys, and data mining techniques. The survey included in this research was publicly shared for people on a social media platform to anonymously participate and share their experience. For data mining techniques, some algorithms are proposed such as Part-of-speech Tagging, word2vec and semantic vectors. Forecasting techniques are applied as Time series analysis and SARIMA. The results discuss the different errors with each method to conclude which is the most accurate forecasting technique in our model.

关键词： Purchase intentions Sentiment analysis Big data analytics Social media NLP Time series analysis Regression models Quantitative forecasting

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Data Mining Applied to the HFC Network to Analyze the Availability of Telecommunication Services 4th

Data Mining Applied to the HFC Network to Analyze the Availa...

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4th International Research and Innovation Congress - Smart Technologies and systems (CI3)

作者： Alarcon-Loza, Shirley Estacio-Corozo, Karen Inst Super Tecnol ARGOS Guayaquil Ecuador

ISBN: (纸本)9783031634338;9783031634345

The failures that affect the telecommunications service in hybrid fiber-coaxial networks (HFC) can be anticipated, through data analysis, to minimize the impact on service availability and users. This work seeks to analyze the causes that affect the performance of HFC networks in Ecuador, through a predictive model for their timely detection and application of anticipated works. The base studied included the components and causes that affected the availability of television and telephony service in a HFC network in 2020, from a sample of 17707 records. For the research, the dependent variable CATEGORY CAUSE is considered, and the objective was to analyze the causes that affect the telecommunications service. The KDD methodology was used and, from the use of WEKA software, classification algorithms were established to determine suitable predictive features, being the causes, the type of event, and the network components, the most significant. The results showed that, for the television service, the best classification algorithm was J48, with a precision value of 68.668% and an area under the curve (ROC Area) of 0.913. For the telephony service, the best classification algorithm was Random Tree, with a precision of 73.666% and an area under the curve of 0.969. The conducted research demonstrated the importance of data mining in the process of analyzing the causes of the impact on television and telephone services.

关键词： HFC network Hybrid fiber-coaxial networks Data Mining Classification Algorithm

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