Intelligent Robots Control and Navigation and Their Mathematical Methods and Insights

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 691

Special Issue Editors


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Guest Editor
Department of Applied Physics, Autonomous University of Baja California, Mexicali 21100, Mexico
Interests: automated metrology; 3D coordinates measurement; robotic navigation; machine vision; simulation of the robotic swarms behaviour
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Guest Editor
Department of Computer-Integrated Technologies, Automation and Mechatronics, Kharkiv National University of Radio Electronics, 61166 Kharkiv, Ukraine
Interests: control systems; mechatronics; robotics; decision-making; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, titled “Intelligent Robots Control and Navigation and Their Mathematical Methods and Insights” promotes a deeper understanding and design of all fundamental aspects in intelligent robot control and navigation, and bridges theoretical questions, foundational issues, and the continuing evolution of applications. The emphasis is on mathematical methods and insights that lead to a better understanding of the aspects of robot control and navigation and on the latter’s expansion into new domains.

Technological advancements have not only enhanced the utilization of mechanical systems in industrial settings but, more significantly, have also extended their applications to sectors previously unimaginable just a few years ago. The term "mechatronics" now broadly refers to modern robotic systems equipped with sophisticated electronic control devices. These devices play a crucial role in enabling systems to achieve high performance and facilitate their integration into various aspects of our daily lives. This synergy has the potential to profoundly alter certain facets of the production world. In particular, with the New Deal” proposed in the industrial revolution named Industry 4.0, new challenges and intelligent control strategies are necessary. Industry 4.0 refers to the intelligent networking of robots in industry with the help of information and communication technology. There are many ways for companies to use this form of intelligent networking. Intelligence is directly related to the capability to establish connections and cooperation between robots and machines. In this context, mathematical insights play a crucial role in the conception an design of the control and navigation strategies of robots.

In recent years, there has been growing interest in robots, a distinct category of mechanical systems, alongside concerns and uncertainties about their impact on productivity and, ultimately, their societal implications. Future developments in robotics pose a formidable challenge in the realm of mathematics, given the pivotal role that control mechanisms play in this field. Indeed, robot control stands out as one of the most significant and complex subjects for mathematicians, engineers, physicians, and practitioners alike. Mathematical considerations form the core of the design of control systems for the movements and performance of robots.

This Special Issue aims to compile the latest advancements in mathematical methods and insights, addressing not only theoretical but also practical challenges in classical and modern robot structures. These structures encompass a range of types, including robotic manipulators, walking robots, flexible robots, haptic robots, and various traditional and innovative mechanisms, each designed to tackle diverse tasks such as grasp, manipulation, and motion in various applications. Particular emphasis is given to the mathematical insights provided by the approaches.

Prof. Dr. Paolo Mercorelli
Dr. Oleg Sergiyenko
Dr. Oleksandr Tsymbal
Guest Editors

Manuscript Submission Information

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Keywords

  • mathematics problems in robot motion and grasp
  • robot control
  • robot optimal control
  • robot stability and analysis of the dynamics
  • robot motion planning and navigation

Published Papers (1 paper)

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Research

16 pages, 2736 KiB  
Article
An Adaptive Cubature Kalman Filter Based on Resampling-Free Sigma-Point Update Framework and Improved Empirical Mode Decomposition for INS/CNS Navigation
by Yu Ma and Di Liu
Mathematics 2024, 12(10), 1607; https://doi.org/10.3390/math12101607 - 20 May 2024
Viewed by 452
Abstract
For the degradation of the filtering performance of the INS/CNS navigation system under measurement noise uncertainty, an adaptive cubature Kalman filter (CKF) is proposed based on improved empirical mode decomposition (EMD) and a resampling-free sigma-point update framework (RSUF). The proposed algorithm innovatively integrates [...] Read more.
For the degradation of the filtering performance of the INS/CNS navigation system under measurement noise uncertainty, an adaptive cubature Kalman filter (CKF) is proposed based on improved empirical mode decomposition (EMD) and a resampling-free sigma-point update framework (RSUF). The proposed algorithm innovatively integrates improved EMD and RSUF into CKF to estimate measurement noise covariance in real-time. Specifically, the improved EMD is used to reconstruct measurement noise, and the exponential decay weighting method is introduced to emphasize the use of new measurement noise while gradually discarding older data to estimate the measurement noise covariance. The estimated measurement noise covariance is then imported into RSUF to directly construct the posterior cubature points without a resampling step. Since the measurement noise covariance is updated in real-time and the prediction cubature points error is directly transformed to the posterior cubature points error, the proposed algorithm is less sensitive to the measurement noise uncertainty. The proposed algorithm is verified by simulations conducted on the INS/CNS-integrated navigation system. The experimental results indicate that the proposed algorithm achieves better performance for attitude angle. Full article
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