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Mathematics
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Mathematical Methods in Artificial Intelligence and Robotics
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Mathematical Methods in Artificial Intelligence and Robotics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 2417

Special Issue Editor

Prof. Dr. Andrey Ronzhin

E-Mail Website
Guest Editor
St. Petersburg Federal Research Center of the Russian Academy of Sciences, 199178 St. Petersburg, Russia
Interests: man–machine interaction; artificial intelligence; robotics

Special Issue Information

Dear Colleagues,

Due to the development of mathematical methods, machine learning algorithms, and artificial intelligence technologies, it is possible to create self-learning control systems that provide a more flexible adaptation of robotic and embedded tools in the environment for users. The purpose of this Special Issue is to disclose the latest progress and new methods on artificial intelligence and robotics, which provide intelligent processing of large volumes of fuzzy information in operational environments configured with embedded sensor networks and cloud services under conditions of uncertainty and environmental variability. Original work in the field of technological foundations for the application of artificial intelligence and machine learning methods in the digital analysis of natural language and the management of interoperable socio-cyber-physical systems and robotic research is encouraged. The Special Issue examines mathematical models; algorithms and software; hardware control of ground, water, and underwater robots; and unmanned aerial vehicles and their built-in and attached equipment, including video cameras, sensors, repeaters, manipulators, grippers, and other actuators. Mathematical and algorithmic models, methods, and experimental uses of the automation of information, physical, and energy interactions of heterogeneous robotic and cyber-physical systems will be published.

Prof. Dr. Andrey Ronzhin
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. Mathematics 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.

Keywords

  • artificial intelligence
  • robotics
  • man–machine interaction
  • socio-cyber-physical systems
  • self-learning systems

Published Papers (2 papers)

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Research

18 pages, 5311 KiB  
Article
Upper Extremity Motion-Based Telemanipulation with Component-Wise Rescaling of Spatial Twist and Parameter-Invariant Skeletal Kinematics
by Donghyeon Noh, Haegyeom Choi, Haneul Jeon, Taeho Kim and Donghun Lee
Mathematics 2024, 12(2), 358; https://doi.org/10.3390/math12020358 - 22 Jan 2024
Viewed by 728
Abstract
This study introduces a framework to improve upper extremity motion-based telemanipulation by component-wise rescaling (CWR) of spatial twist. This method allows for separate adjustments of linear and angular scaling parameters, significantly improving precision and dexterity even when the operator’s heading direction changes. By [...] Read more.
This study introduces a framework to improve upper extremity motion-based telemanipulation by component-wise rescaling (CWR) of spatial twist. This method allows for separate adjustments of linear and angular scaling parameters, significantly improving precision and dexterity even when the operator’s heading direction changes. By finely controlling both the linear and angular velocities independently, the CWR method enables more accurate telemanipulation in tasks requiring diverse speed and accuracy based on personal preferences or task-specific demands. The study conducted experiments confirming that operators could precisely control the robot gripper with a steady, controlled motion even in confined spaces, irrespective of changes in the subject’s body-heading direction. The performance evaluation of the proposed motion-scaling-based telemanipulation leveraged Optitrack’s motion-capture system, comparing the trajectories of the operator’s hand and the manipulator’s end effector (EEF). This verification process solidified the efficacy of the developed framework in enhancing telemanipulation performance. Full article
(This article belongs to the Special Issue Mathematical Methods in Artificial Intelligence and Robotics)
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12 pages, 4684 KiB  
Article
Optimal Joint Path Planning of a New Virtual-Linkage-Based Redundant Finishing Stage for Additive-Finishing Integrated Manufacturing
by Jiwon Yu, Haneul Jeon, Hyungjin Jeong and Donghun Lee
Mathematics 2023, 11(24), 4995; https://doi.org/10.3390/math11244995 - 18 Dec 2023
Viewed by 720
Abstract
This paper describes the optimal path planning of a redundant finishing mechanism developed for joint space-based additive-finishing integrated manufacturing (AFM). The research motivation results from an inevitable one-sided layout of a finishing stage (FS) with regard to the additive stage (AS) in the [...] Read more.
This paper describes the optimal path planning of a redundant finishing mechanism developed for joint space-based additive-finishing integrated manufacturing (AFM). The research motivation results from an inevitable one-sided layout of a finishing stage (FS) with regard to the additive stage (AS) in the AFM. These two stages share a 2-dof bed stage (BS), and the FS can lightly shave off the rough-surfaced 3D print on the bed. Since the FS located at the side of the AS cannot reach all the target points of the 3D print, the bed should be able to rotate the 3D print about the z-axis and translate it in the z-axis. As a result, the AS has 4-dof joints for 2P and 1P1R during the additive process with AS-BS, and FS has 4-dof and 2-dof integrated joints for 2P2R and 1P1R during the finishing process with FS-BS, respectively. For the kinematic modeling of the FS part and the BS, the virtual linkage connecting the bed frame origin and the FS’s joint frame for approaching the BS is considered to realize seamless kinematic redundancy. The minimum Euclidian norm of the joint velocity space is the objective function to find the optimal joint space solution for a given tool path. To confirm the feasibility of the developed joint path planning algorithm in the proposed FS-BS mechanism, layer-by-layer slicing of a given 3D print’s CAD model and tool path generation were performed. Then, the numerical simulations of the optimal joint path planning for some primitive 3D print geometries were conducted. As a result, we confirmed that the maximum and mean pose error in point-by-point only, with the developed optimal joint path planning algorithm, were less than 202 nm and 153 nm, respectively. Since precision and general machining accuracies in tool path generation are in the range of ±10 μm and 20 μm, the pose error in this study fully satisfies the industry requirements. Full article
(This article belongs to the Special Issue Mathematical Methods in Artificial Intelligence and Robotics)
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