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Intelligent Autonomous System
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Intelligent Autonomous System

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 1429

Special Issue Editors

Prof. Dr. Soon-Geul Lee

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Guest Editor
Mechanical Engineering Department, Kyung Hee University, Yongin 17104, Republic of Korea
Interests: robot dynamics and control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jinung An

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Guest Editor
Interdisciplinary Studies, Graduate School, DGIST, Daegu 42988, Republic of Korea
Interests: human augmentation; human mobility interaction; automotive in-car UX; brain machine interface
Dr. Hyunn Joon Chung

E-Mail Website
Guest Editor
Intelligent Robotics R&D Division KIRO, Pohang 37666, Republic of Korea
Interests: dynamics; optimization algorithms; artificial intelligence; robotics
Prof. Dr. Sukhan Lee

E-Mail Website
Guest Editor
Artificial Intelligence Department, Sungkyunkwan University, Suwon 16419, Republic of Korea
Interests: robotics; computer vision; artificial intelligence; MEMS/NEMS
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent autonomous systems are increasingly applied in areas ranging from industrial applications to professional service and household domains. New technologies and application domains push forward the need for research and development, resulting in new challenges to be overcome in order to apply intelligent autonomous systems in a reliable and user-independent way. Recent advances in the areas of artificial intelligence, machine learning and adaptive control enable autonomous systems with improved robustness and flexibility.

This Special Issue will include selected papers from the 18th international conference on Intelligent Autonomous System (IAS-18), to be held in Suwon, Korea, 26-29 June, 2023. The theme of the IAS-18 conference is “Impact and Effect of AI on Intelligent Autonomous Systems“.

The main topics of interest are:

  • Mobile robots;
  • Collaborative robots/cobots;
  • Household robots;
  • Long-term autonomous systems;
  • Humanoid robots;
  • Intelligent machines;
  • Climbing robots;
  • Outdoor and field robots;
  • Autonomous vehicles;
  • Healthcare robots;
  • Applied robots;
  • Flying robots;
  • On-water/underwater robots;
  • Robot swarms;
  • Biomimetic robots;
  • Robot vision;
  • Advanced obstacle avoidance;
  • Robot simulations;
  • Human–robot-interactions;
  • Semantic modelling;
  • Intelligent systems proving grounds;
  • Augmented robotics;
  • Data fusion and machine learning;
  • Localization and SLAM;
  • Robots for Industry 4.0;
  • Robotic competitions;
  • Intelligent sensor and systems;
  • Cloud robotics;
  • Intelligent perception;
  • Mechatronics for intelligent systems.

Prof. Dr. Soon-Geul Lee
Prof. Dr. Jinung An
Dr. Hyunn Joon Chung
Prof. Dr. Sukhan Lee
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

31 pages, 3927 KiB  
Article
TimeTector: A Twin-Branch Approach for Unsupervised Anomaly Detection in Livestock Sensor Noisy Data (TT-TBAD)
by Junaid Khan Kakar, Shahid Hussain, Sang Cheol Kim and Hyongsuk Kim
Sensors 2024, 24(8), 2453; https://doi.org/10.3390/s24082453 - 11 Apr 2024
Viewed by 475
Abstract
Unsupervised anomaly detection in multivariate time series sensor data is a complex task with diverse applications in different domains such as livestock farming and agriculture (LF&A), the Internet of Things (IoT), and human activity recognition (HAR). Advanced machine learning techniques are necessary to [...] Read more.
Unsupervised anomaly detection in multivariate time series sensor data is a complex task with diverse applications in different domains such as livestock farming and agriculture (LF&A), the Internet of Things (IoT), and human activity recognition (HAR). Advanced machine learning techniques are necessary to detect multi-sensor time series data anomalies. The primary focus of this research is to develop state-of-the-art machine learning methods for detecting anomalies in multi-sensor data. Time series sensors frequently produce multi-sensor data with anomalies, which makes it difficult to establish standard patterns that can capture spatial and temporal correlations. Our innovative approach enables the accurate identification of normal, abnormal, and noisy patterns, thus minimizing the risk of misinterpreting models when dealing with mixed noisy data during training. This can potentially result in the model deriving incorrect conclusions. To address these challenges, we propose a novel approach called “TimeTector-Twin-Branch Shared LSTM Autoencoder” which incorporates several Multi-Head Attention mechanisms. Additionally, our system now incorporates the Twin-Branch method which facilitates the simultaneous execution of multiple tasks, such as data reconstruction and prediction error, allowing for efficient multi-task learning. We also compare our proposed model to several benchmark anomaly detection models using our dataset, and the results show less error (MSE, MAE, and RMSE) in reconstruction and higher accuracy scores (precision, recall, and F1) against the baseline models, demonstrating that our approach outperforms these existing models. Full article
(This article belongs to the Special Issue Intelligent Autonomous System)
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15 pages, 1203 KiB  
Article
The Effects of Speed and Delays on Test-Time Performance of End-to-End Self-Driving
by Ardi Tampuu, Kristjan Roosild and Ilmar Uduste
Sensors 2024, 24(6), 1963; https://doi.org/10.3390/s24061963 - 19 Mar 2024
Viewed by 423
Abstract
This study investigates the effects of speed variations and computational delays on the performance of end-to-end autonomous driving systems (ADS). Utilizing 1:10 scale mini-cars with limited computational resources, we demonstrate that different driving speeds significantly alter the task of the driving model, challenging [...] Read more.
This study investigates the effects of speed variations and computational delays on the performance of end-to-end autonomous driving systems (ADS). Utilizing 1:10 scale mini-cars with limited computational resources, we demonstrate that different driving speeds significantly alter the task of the driving model, challenging the generalization capabilities of systems trained at a singular speed profile. Our findings reveal that models trained to drive at high speeds struggle with slower speeds and vice versa. Consequently, testing an ADS at an inappropriate speed can lead to misjudgments about its competence. Additionally, we explore the impact of computational delays, common in real-world deployments, on driving performance. We present a novel approach to counteract the effects of delays by adjusting the target labels in the training data, demonstrating improved resilience in models to handle computational delays effectively. This method, crucially, addresses the effects of delays rather than their causes and complements traditional delay minimization strategies. These insights are valuable for developing robust autonomous driving systems capable of adapting to varying speeds and delays in real-world scenarios. Full article
(This article belongs to the Special Issue Intelligent Autonomous System)
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