Special Issue "Modeling, Sensor Fusion and Control Techniques in Applied Robotics"

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Mechatronic and Intelligent Machines".

Deadline for manuscript submissions: 31 December 2022 | Viewed by 4703

Special Issue Editors

Dr. Peter Odry
E-Mail Website
Guest Editor
Institute of Informatics, University of Dunaújváros, 2400 Dunaújváros, Hungary
Interests: complete modelling; optimization; construction and verification of a six-legged walking structure; motion control optimization, with special attention to fuzzy-based motion control solutions
Dr. Akos Odry
E-Mail Website
Guest Editor
Institute of Informatics, University of Dunaújváros, 2400 Dunaújváros, Hungary
Interests: kalman filter; attitude estimation; fuzzy control; fuzzy Logic; robotics; control; inertial measurement unit; imu; MARG; inverted pendulum; adaptive filter; sensor fusion
Prof. Dr. Jan Awrejcewicz
E-Mail Website
Guest Editor

Special Issue Information

Dear Colleagues,

The realization of precise, robust, and intelligent control solutions is based on multiple coordinated design steps in modern robot systems. These design steps include (i) the concrete realization of task-oriented mechatronics systems, (ii) both the derivation and validation of realistic mathematical models, (iii) the calibration of the applied sensor networks, (iv) the estimation of robot states and parameters, and (v) both the design and implementation of intelligent control solutions that provide an energy efficient and robust performance. This Special Issue aims to present novel efficient techniques, which enable the enhancement of overall closed-loop performances in real robot systems (e.g., in mobile robots, UAVs, and robot manipulators). 

Recent research on the below-listed topics is invited. Review papers are also welcome. 

- Optimized mechanics structures in robotic applications, which enable efficient trajectory tracking and translation motion. The analysis can also be extended to vibration diagnostics-based designs, which aim to reduce the levels and patterns of unwanted signals.

- Novel robot modeling and model validation techniques, which provide both relevant and realistic information of real system states. These techniques include novel simulation models and contribute to the obtainment of reliable deductions connected to the behavior of dynamical systems.

- Fusion algorithms of sensor networks for sampling the robot dynamics. These algorithms can include novel filter structures, sensor calibration techniques, and both robust and reliable state estimation methods. Novel measurement solutions that enable the effective derivation of robot states and parameters can also be proposed.

- Novel energy-efficient control solutions that provide a superior performance compared with the conventional methods. Implementation of robust control approaches that handle both the parameter uncertainties/measurement disturbances, and ensure a satisfying control performance.

- Efficient image processing methods that aim to provide the basis for motion planning and reliable trajectory tracking in robot systems.

- Applied industrial solutions in robot systems, which present complex design, implementation, and test results, e.g., artificial intelligence-, IoT-, soft computing-, and/or Industry 4.0-based intelligent robotic solutions in agriculture, construction, medicine, rehabilitation, and biological research. 

Prof. Dr. Jan Awrejcewicz
Dr. Akos Odry
Dr. Peter Odry
Guest Editors

Manuscript Submission Information

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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. Machines is an international peer-reviewed open access monthly 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 1800 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

  • applied robotics
  • robot modeling
  • model validation
  • nonlinear dynamics
  • sensor fusion
  • state estimation
  • sensor calibration
  • robust/adaptive control
  • fuzzy systems
  • servo systems
  • stability problems
  • vibrations

Published Papers (6 papers)

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Research

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Article
Mechanical Design and a Novel Structural Optimization Approach for Hexapod Walking Robots
Machines 2022, 10(6), 466; https://doi.org/10.3390/machines10060466 - 11 Jun 2022
Viewed by 305
Abstract
This paper presents a novel model-based structural optimization approach for the efficient electromechanical development of hexapod robots. First, a hexapod-design-related analysis of both optimization objectives and relevant parameters is conducted based on the derived dynamical model of the robot. A multi-objective optimization goal [...] Read more.
This paper presents a novel model-based structural optimization approach for the efficient electromechanical development of hexapod robots. First, a hexapod-design-related analysis of both optimization objectives and relevant parameters is conducted based on the derived dynamical model of the robot. A multi-objective optimization goal is proposed, which minimizes energy consumption, unwanted body motion and differences between joint torques. Then, an optimization framework is established, which utilizes a sophisticated strategy to handle the optimization problems characterized by a large set of parameters. As a result, a satisfactory result is efficiently obtained with fewer iterations. The research determines the optimal parameter set for hexapod robots, contributing to significant increases in a robot’s walking range, suppressed robot body vibrations, and both balanced and appropriate motor loads. The modular design of the proposed simulation model also offers flexibility, allowing for the optimization of other electromechanical properties of hexapod robots. The presented research focuses on the mechatronic design of the Szabad(ka)-III hexapod robot and is based on the previously validated Szabad(ka)-II hexapod robot model. Full article
(This article belongs to the Special Issue Modeling, Sensor Fusion and Control Techniques in Applied Robotics)
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Article
Improved Cubature Kalman Filtering on Matrix Lie Groups Based on Intrinsic Numerical Integration Error Calibration with Application to Attitude Estimation
Machines 2022, 10(4), 265; https://doi.org/10.3390/machines10040265 - 07 Apr 2022
Cited by 1 | Viewed by 507
Abstract
This paper investigates the numerical integration error calibration problem in Lie group sigma point filters to obtain more accurate estimation results. On the basis of the theoretical framework of the Bayes–Sard quadrature transformation, we first established a Bayesian estimator on matrix Lie groups [...] Read more.
This paper investigates the numerical integration error calibration problem in Lie group sigma point filters to obtain more accurate estimation results. On the basis of the theoretical framework of the Bayes–Sard quadrature transformation, we first established a Bayesian estimator on matrix Lie groups for system measurements in Euclidean spaces or Lie groups. The estimator was then employed to develop a generalized Bayes–Sard cubature Kalman filter on matrix Lie groups that considers additional uncertainties brought by integration errors and contains two variants. We also built on the maximum likelihood principle, and an adaptive version of the proposed filter was derived for better algorithm flexibility and more precise filtering results. The proposed filters were applied to the quaternion attitude estimation problem. Monte Carlo numerical simulations supported that the proposed filters achieved better estimation quality than that of other Lie group filters in the mentioned studies. Full article
(This article belongs to the Special Issue Modeling, Sensor Fusion and Control Techniques in Applied Robotics)
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Article
Design of an Embedded Energy Management System for Li–Po Batteries Based on a DCC-EKF Approach for Use in Mobile Robots
Machines 2021, 9(12), 313; https://doi.org/10.3390/machines9120313 - 25 Nov 2021
Viewed by 727
Abstract
In mobile robotics, since no requirements have been defined regarding accuracy for Battery Management Systems (BMS), standard approaches such as Open Circuit Voltage (OCV) and Coulomb Counting (CC) are usually applied, mostly due to the fact that employing more complicated estimation algorithms requires [...] Read more.
In mobile robotics, since no requirements have been defined regarding accuracy for Battery Management Systems (BMS), standard approaches such as Open Circuit Voltage (OCV) and Coulomb Counting (CC) are usually applied, mostly due to the fact that employing more complicated estimation algorithms requires higher computing power; thus, the most advanced BMS algorithms reported in the literature are developed and verified by laboratory experiments using PC-based software. The objective of this paper is to describe the design of an autonomous and versatile embedded system based on an 8-bit microcontroller, where a Dual Coulomb Counting Extended Kalman Filter (DCC-EKF) algorithm for State of Charge (SOC) estimation is implemented; the developed prototype meets most of the constraints for BMSs reported in the literature, with an energy efficiency of 94% and an error of SOC accuracy that varies between 2% and 8% based on low-cost components. Full article
(This article belongs to the Special Issue Modeling, Sensor Fusion and Control Techniques in Applied Robotics)
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Article
An Improved Invariant Kalman Filter for Lie Groups Attitude Dynamics with Heavy-Tailed Process Noise
Machines 2021, 9(9), 182; https://doi.org/10.3390/machines9090182 - 27 Aug 2021
Cited by 1 | Viewed by 731
Abstract
Attitude estimation is a basic task for most spacecraft missions in aerospace engineering and many Kalman type attitude estimators have been applied to the guidance and navigation of spacecraft systems. By building the attitude dynamics on matrix Lie groups, the invariant Kalman filter [...] Read more.
Attitude estimation is a basic task for most spacecraft missions in aerospace engineering and many Kalman type attitude estimators have been applied to the guidance and navigation of spacecraft systems. By building the attitude dynamics on matrix Lie groups, the invariant Kalman filter (IKF) was developed according to the invariance properties of symmetry groups. However, the Gaussian noise assumption of Kalman theory may be violated when a spacecraft maneuvers severely and the process noise might be heavy-tailed, which is prone to degrade IKF’s performance for attitude estimation. To address the attitude estimation problem with heavy-tailed process noise, this paper proposes a hierarchical Gaussian state-space model for invariant Kalman filtering: The probability density function of state prediction is defined based on student’s t distribution, while the conjugate prior distributions of the scale matrix and degrees of freedom (dofs) parameter are respectively formulated as the inverse Wishart and Gamma distribution. For the constructed hierarchical Gaussian attitude estimation state-space model, the Lie groups rotation matrix of spacecraft attitude is inferred together with the scale matrix and dof parameter using the variational Bayesian iteration. Numerical simulation results illustrate that the proposed approach can significantly improve the filtering robustness of invariant Kalman filter for Lie groups spacecraft attitude estimation problems with heavy-tailed process uncertainty. Full article
(This article belongs to the Special Issue Modeling, Sensor Fusion and Control Techniques in Applied Robotics)
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Article
A Simple Soft Computing Structure for Modeling and Control
Machines 2021, 9(8), 168; https://doi.org/10.3390/machines9080168 - 14 Aug 2021
Cited by 3 | Viewed by 712
Abstract
Using the interpolation/extrapolation skills of the core function of an iterative adaptive controller, a structurally simple single essential layer neural network-based topological structure is suggested with fast and explicit single-step teaching and data-retrieving abilities. Its operation does not assume massive parallelism, therefore it [...] Read more.
Using the interpolation/extrapolation skills of the core function of an iterative adaptive controller, a structurally simple single essential layer neural network-based topological structure is suggested with fast and explicit single-step teaching and data-retrieving abilities. Its operation does not assume massive parallelism, therefore it easily can be simulated by simple sequential program codes not needing sophisticated data synchronization mechanisms. It seems to be advantageous in approximate model-based common, robust, or adaptive controllers that can compensate for the effects of minor modeling imprecisions. In this structure a neuron can be in either a firing or a passive (i.e., producing zero output) state. In firing state its activation function realizes an abstract rotation that maps the desired kinematic data into the space of the necessary control forces. The activation function allows the use of a simple and fast incremental model modification for slowly varying dynamic models. Its operation is exemplified by numerical simulations for a van der Pol oscillator in free motion, and within a Computed Torque type control. To reveal the possibility for efficient model correction, a robust Variable Structure/Sliding Mode Controller is applied, too. The novel structure can be obtained by approximate experimental observations as e.g., the fuzzy models. Full article
(This article belongs to the Special Issue Modeling, Sensor Fusion and Control Techniques in Applied Robotics)
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Review

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Review
Perspectives of RealSense and ZED Depth Sensors for Robotic Vision Applications
Machines 2022, 10(3), 183; https://doi.org/10.3390/machines10030183 - 03 Mar 2022
Viewed by 664
Abstract
This review paper presents an overview of depth cameras. Our goal is to describe the features and capabilities of the introduced depth sensors in order to determine their possibilities in robotic applications, focusing on objects that might appear in applications with high accuracy [...] Read more.
This review paper presents an overview of depth cameras. Our goal is to describe the features and capabilities of the introduced depth sensors in order to determine their possibilities in robotic applications, focusing on objects that might appear in applications with high accuracy requirements. A series of experiments was conducted, and various depth measuring conditions were examined in order to compare the measurement results of all the depth cameras. Based on the results, all the examined depth sensors were appropriate for applications where obstacle avoidance and robot spatial orientation were required in coexistence with image vision algorithms. In robotic vision applications where high accuracy and precision were obligatory, the ZED depth sensors achieved better measurement results. Full article
(This article belongs to the Special Issue Modeling, Sensor Fusion and Control Techniques in Applied Robotics)
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