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New Challenges and Sensor Techniques in Robot Positioning

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

Deadline for manuscript submissions: 31 October 2025 | Viewed by 383

Special Issue Editors


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Guest Editor
The Autonomous Navigation and Sensor Fusion Lab (ANSFL), The Hatter Department of Marine Technologies, Charney School of Marine Sciences, University of Haifa, Haifa 3498838, Israel
Interests: inertial sensing and robotics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Control and Information Systems (DCIS), Faculty of Electrical Engineering and Information Technology (FEIT), University of Zilina, 010 26 Zilina, Slovakia
Interests: mobile robotics; inertial sensors; aviation

Special Issue Information

Dear Colleagues,

Precise, robust, and reliable position estimation is crucial in achieving higher autonomy levels for mobile robots. The quality of the estimated position, including reliability, self-diagnostics, and data redundancy, has a direct positive impact on the safety of the whole mobile system. This further supports the public acceptance of cutting-edge technologies in robotics and autonomous transportation.

This Special Issue will cover a wide range of challenges related to robot positioning and navigation. These challenges include new sensors, perception and signal processing algorithms, AI-based positioning systems, safety-related sensing, and their implementation in both indoor and outdoor applications.

Review articles and original research papers contributing to the topic of positioning in robotics and autonomous transport are welcome.

Dr. Itzik Klein
Dr. Dušan Nemec
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.

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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.

Keywords

  • robot navigation
  • sensor fusion
  • positioning and localization
  • sensors
  • data-driven navigation
  • nonlinear filtering
  • bio-inspired navigation

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Published Papers (1 paper)

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Research

17 pages, 2347 KiB  
Article
Fuzzy Logic-Based Adaptive Filtering for Transfer Alignment
by Zhaohui Gao, Jiahui Yang, Chengfan Gu and Yongmin Zhong
Sensors 2025, 25(16), 4998; https://doi.org/10.3390/s25164998 - 12 Aug 2025
Viewed by 126
Abstract
The transfer alignment of strapdown inertial navigation systems (SINSs) is of great significance for improving the strike accuracy of airborne tactical vehicles. This study designed a new fuzzy logic-based adaptive filtering method by using the fuzzy logic theory to address the influence of [...] Read more.
The transfer alignment of strapdown inertial navigation systems (SINSs) is of great significance for improving the strike accuracy of airborne tactical vehicles. This study designed a new fuzzy logic-based adaptive filtering method by using the fuzzy logic theory to address the influence of system model error on the state estimation of the Kalman filter for SINS transfer alignment. It established the state error model and measurement error model, which were embedded with the state prediction residual and measurement residual, respectively, for SINS transfer alignment. The fuzzy rules were designed and introduced into the Kalman filtering framework to estimate the covariances of the system measurement and predicted state by minimizing their residuals to improve filtering accuracy for SINS transfer alignment. Simulation and experimentation together with associated comparative analyses were conducted, demonstrating that the proposed method can effectively handle the influence of system model error on SINS transfer alignment, and its accuracy is at least 18.83% higher than benchmark methods for transfer alignment. Full article
(This article belongs to the Special Issue New Challenges and Sensor Techniques in Robot Positioning)
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