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Sensors for Navigation and Control Systems: 2nd Edition
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Sensors for Navigation and Control Systems: 2nd Edition

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

Deadline for manuscript submissions: closed (25 August 2024) | Viewed by 9789

Special Issue Editor

Prof. Dr. Antonios Tsourdos

E-Mail Website
Guest Editor
Centre for Autonomous and Cyberphysical Systems, Cranfield University, Cranfield MK43 0AL, UK
Interests: unmanned aircraft systems; decision making on multi-agent systems; data-centric guidance and control; swarm
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to highlight the most recent research regarding the development of sensors for use in navigation and control systems. This Special Issue focuses on the most recent innovations, trends and concerns, as well as practical challenges encountered and solutions adopted, in the field of sensors for use in navigation and control systems. We solicit researchers to submit research articles and reviews that provide comprehensive insights into the use of sensor technologies for navigation and control systems and may focus on any aspect of novel sensor development and applications. Topics of interest include, but are not limited to, the following areas:

  • control sensors and advanced applications;
  • navigation and positioning;
  • sensor technology in applications of control engineering;
  • accelerometers, inclinometers and gyroscopes;
  • multi-sensor fusion technology;
  • smart and intelligent sensors;
  • sensor interfacing and signal conditioning;
  • sensor calibration;
  • data fusion and deep learning in sensor systems;
  • localization and object tracking;
  • path planning;
  • motion planning;
  • adaptive guidance and control;
  • vison-based navigation;
  • fault tolerant control.

Prof. Dr. Antonios Tsourdos
Guest Editor

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.

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Related Special Issue

Published Papers (3 papers)

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Research

19 pages, 9478 KiB  
Article
Improved Hybrid Model for Obstacle Detection and Avoidance in Robot Operating System Framework (Rapidly Exploring Random Tree and Dynamic Windows Approach)
by Ndidiamaka Adiuku, Nicolas P. Avdelidis, Gilbert Tang and Angelos Plastropoulos
Sensors 2024, 24(7), 2262; https://doi.org/10.3390/s24072262 - 2 Apr 2024
Cited by 2 | Viewed by 3548
Abstract
The integration of machine learning and robotics brings promising potential to tackle the application challenges of mobile robot navigation in industries. The real-world environment is highly dynamic and unpredictable, with increasing necessities for efficiency and safety. This demands a multi-faceted approach that combines [...] Read more.
The integration of machine learning and robotics brings promising potential to tackle the application challenges of mobile robot navigation in industries. The real-world environment is highly dynamic and unpredictable, with increasing necessities for efficiency and safety. This demands a multi-faceted approach that combines advanced sensing, robust obstacle detection, and avoidance mechanisms for an effective robot navigation experience. While hybrid methods with default robot operating system (ROS) navigation stack have demonstrated significant results, their performance in real time and highly dynamic environments remains a challenge. These environments are characterized by continuously changing conditions, which can impact the precision of obstacle detection systems and efficient avoidance control decision-making processes. In response to these challenges, this paper presents a novel solution that combines a rapidly exploring random tree (RRT)-integrated ROS navigation stack and a pre-trained YOLOv7 object detection model to enhance the capability of the developed work on the NAV-YOLO system. The proposed approach leveraged the high accuracy of YOLOv7 obstacle detection and the efficient path-planning capabilities of RRT and dynamic windows approach (DWA) to improve the navigation performance of mobile robots in real-world complex and dynamically changing settings. Extensive simulation and real-world robot platform experiments were conducted to evaluate the efficiency of the proposed solution. The result demonstrated a high-level obstacle avoidance capability, ensuring the safety and efficiency of mobile robot navigation operations in aviation environments. Full article
(This article belongs to the Special Issue Sensors for Navigation and Control Systems: 2nd Edition)
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16 pages, 1279 KiB  
Article
IMU Networks for Trajectory Reconstruction in Logistics Applications
by João Silva Sequeira
Sensors 2023, 23(18), 7838; https://doi.org/10.3390/s23187838 - 12 Sep 2023
Viewed by 1374
Abstract
This paper discusses the use of networks of Inertial Measurement Units (IMUs) for the reconstruction of trajectories from sensor data. Logistics is a natural application domain to verify the quality of the handling of goods. This is a mass application and the economics [...] Read more.
This paper discusses the use of networks of Inertial Measurement Units (IMUs) for the reconstruction of trajectories from sensor data. Logistics is a natural application domain to verify the quality of the handling of goods. This is a mass application and the economics of logistics impose that the IMUs to be used must be low-cost and use basic computational devices. The approach in this paper converts a strategy from the literature, used in the multi-target following problem, to reach a consensus in a network of IMUs. This paper presents results on how to achieve the consensus in trajectory reconstruction, along with covariance intersection data fusion of the information obtained by all the nodes in the network. Full article
(This article belongs to the Special Issue Sensors for Navigation and Control Systems: 2nd Edition)
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25 pages, 8755 KiB  
Article
Smartphone MEMS Accelerometer and Gyroscope Measurement Errors: Laboratory Testing and Analysis of the Effects on Positioning Performance
by Vincenzo Capuano, Liangchun Xu and Jose Estrada Benavides
Sensors 2023, 23(17), 7609; https://doi.org/10.3390/s23177609 - 1 Sep 2023
Cited by 5 | Viewed by 3996
Abstract
Embedding various sensors with powerful computing and storage capabilities in a small communication device, smartphones have become a prominent platform for navigation. With the increasing popularity of Apple CarPlay and Android Auto, smartphones are quickly replacing built-in automotive navigation solutions. On the other [...] Read more.
Embedding various sensors with powerful computing and storage capabilities in a small communication device, smartphones have become a prominent platform for navigation. With the increasing popularity of Apple CarPlay and Android Auto, smartphones are quickly replacing built-in automotive navigation solutions. On the other hand, smartphones are equipped with low-performance Micro Electro Mechanical Systems (MEMS) sensors to enhance their navigation performance in Global Navigation Satellite System (GNSS)-degraded or -denied environments. Compared with higher-grade inertial navigation systems (INS), MEMS-based INS have a poor navigation performance due to large measurement errors. In this paper, we present laboratory test results on the stochastic and deterministic errors observed in MEMS inertial sensor measurements of five different smartphones from different manufacturers. Then, we describe and discuss the short-term effects of these errors on the pure inertial navigation performance and also on the navigation performance based on the tight coupling of INS with GNSS measurements using a smartphone. Full article
(This article belongs to the Special Issue Sensors for Navigation and Control Systems: 2nd Edition)
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