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Control and Navigation Design for Robotic Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 2054

Special Issue Editors

Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
Interests: observer-navigation algorithm; nonlinear control system; robotic systems
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Guest Editor
Department of Mechanical and Aerospace Engineering, New Mexico State University, Las Cruces, NM 88003, USA
Interests: multi-agent control; multi-agent navigation; cooperative control

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Guest Editor
Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
Interests: path-planning design; multi-agent control; robotic unmanned systems

Special Issue Information

Dear Colleagues,

The last few years have seen a growing interest in the development of intelligent vehicles capable of moving autonomously in space and being aware of their surroundings. Their great potential makes them ideal for the most varied fields of application: agriculture, manufacturing, land and aerial surveillance, naval operations, commercial transport, and space exploration. Despite the significant results achieved in terms of accuracy in the real-time implementation of on-board algorithms for small autonomous systems, assessed solutions are not available, and complex technical challenges still need to be addressed.

The scope of this Special Issue is to present the latest methodological and applied developments for control and navigation algortihms for robotic systems. The topics for this Special Issue involve new advances in observer and nonlinear navigation algorithms, multi-agent control and navigation systems, and cooperative control. Applications should include aerospace, robotics, and agriculture, as examples.

Dr. Elisa Capello
Dr. Hyeongjun Park
Dr. Stefano Primatesta
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. Applied Sciences 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 2400 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 (1 paper)

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Research

13 pages, 637 KiB  
Article
Nonlinear UGV Identification Methods via the Gaussian Process Regression Model for Control System Design
by Enza Incoronata Trombetta, Davide Carminati and Elisa Capello
Appl. Sci. 2022, 12(22), 11769; https://doi.org/10.3390/app122211769 - 19 Nov 2022
Viewed by 1198
Abstract
In this paper, two identification methods are proposed for a ground robotic system. A Gaussian process regression (GPR) model is presented and adopted for a system identification framework. Its performance and features were compared with a wavelet-based nonlinear autoregressive exogenous (NARX) model. Both [...] Read more.
In this paper, two identification methods are proposed for a ground robotic system. A Gaussian process regression (GPR) model is presented and adopted for a system identification framework. Its performance and features were compared with a wavelet-based nonlinear autoregressive exogenous (NARX) model. Both algorithms were compared and experimentally validated for a small ground robot. Moreover, data were collected throughout the onboard sensors. The results show better prediction performance in the case of the GPR method, as an estimation algorithm and in providing a measure of uncertainty. Full article
(This article belongs to the Special Issue Control and Navigation Design for Robotic Systems)
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