Ai, Computer Science and Robotics Technology

Journal Information
EISSN: 27546292
Published by: IntechOpen
Total articles ≅ 13

Latest articles in this journal

Sharon Mistretta
Published: 10 November 2022
Ai, Computer Science and Robotics Technology, Volume 2022, pp 1-3;

Wu Weicong, Wu Wenqiang, Zhang Chunliang, Zhu Houyao, Chen Chaozheng
Published: 2 November 2022
Ai, Computer Science and Robotics Technology, Volume 2022, pp 1-20;

In order to solve the problems of low precision, disturbance and unsmooth movement of heavy-duty long manipulator in working conditions, an IPSMC (improved power sliding mode control) based on force impedance control is proposed. First, the robot is organized and an accurate kinematics model is established and the motion characteristics of special manipulator system are analyzed. Combined with the dynamic model, the deviation of the position and velocity feedback from the expected value is converted by the force impedance controller, which makes the manipulator more flexible under the condition of low damping. At the same time, according to the position and speed feedback, an IPSMC is proposed, which uses the sliding mode control (SMC) to reduce the disturbance and oscillation in the working condition, so as to realize the control in the position space and the control in the force space. Finally, through Adams-Simulink co-simulation, the designed control system is tested. The results show that the force/position hybrid control strategy has good anti-interference ability for the long manipulator with large working range. While improving the flexibility of the manipulator, it also weakens the end chattering problem to some extent, enhances the robustness of the control system, and meets the requirements of working conditions.
Published: 7 October 2022
Ai, Computer Science and Robotics Technology, Volume 2022, pp 1-40;

Heuristic methods, for global optimization, have been receiving much interest in the last years, among which Particle Swarm Optimization (PSO) algorithm can be highlighted. However, the application of heuristic methods can lead to premature convergence. In this work, the addition of a step on the PSO algorithm is proposed. This new step, based in Nelder–Mead simplex search method (NM), consists of repositioning the current particle with global best solution, not for a better position, but away from the current nearest local optimum, to avoid getting stuck on this local optimum. There are other PSO-NM algorithms, but the one we are proposing, has a different strategy. The proposed algorithm was also tested with the repositioning strategy in other particles beyond the current global best particle, depending on the repositioning probability. To evaluate the effectiveness of the proposed methods, and study its better parameters, were used various test functions, and for each test function, various number of particles were used in combination with various probabilities of particles repositioning. A thousand runs were performed for each case, resulting in more than two millions runs. The computational studies showed that the repositioning of of global best particle increases the percentage of success on reaching the global best solution, but better results can be obtained applying the repositioning strategy to other particles with repositioning probabilities between 1–5%.
Published: 1 August 2022
Ai, Computer Science and Robotics Technology, Volume 2022, pp 1-12;

Conceptual designs require optimization methods to identify the best fit in the system. The article investigates the application of quantum computation in gas turbine design and simulation problems with current technologies, approaches and potential capabilities. Quantum optimization algorithms and quantum annealers help in predicting overall efficiency and optimizing various operating parameters of the gas turbine. A comparison of both classical and quantum computers has been discussed briefly. The classical model challenges are mitigated with the use of quantum computation. A novel hybrid model for simulating gas turbines has been proposed, which consists of a combination of both physics and machine learning to eliminate few of the critical problems faced. This review elaborates application of quantum computing based machine learning for design and optimization of a gas turbine. The overall states of the gas paths of gas turbines could be analyzed using the quantum computing model in the future.
Edgar A. Martínez-García, Roman Lavrenov, Evgeni Magid
Published: 21 June 2022
Ai, Computer Science and Robotics Technology, Volume 2022, pp 1-17;

Researchers have developed numerous artificial fish to mimic the swimming abilities of biological species and understand their biomechanical subaquatic skills. The motivation arises from the interest to gain deeper comprehension of the efficient nature of biological locomotion, which is the result of millions of years of evolution and adaptation. Fin-based biological species developed exceptional swimming abilities and notable performance in highly dynamic and complex subaquatic environments. Therefore, based on research by the scientific community, this mini-review concentrates on discussing the mechanical devices developed to implement the caudal propulsive segments of robotic fish. Caudal mechanisms are of considerable interest because they may be designed to control inertial and gravitational forces, as well as exerting great dynamic range in robotic fish. This manuscript provides a concise review focused on the engineering implementations of caudal mechanisms of anguilliform, subcarangiform, subcarangiform, thunniform and ostraciiform swimming modes.
Maria Papathanasaki, Leandros Maglaras, Nick Ayres
Published: 1 June 2022
Ai, Computer Science and Robotics Technology, Volume 2022, pp 1-24;

This paper presents a comprehensive investigation of modern authentication schemes. We start with the importance of authentication methods and the different authentication processes. Then we present the authentication criteria used and we perform a comparison of authentication methods in terms of universality, uniqueness, collectability, performance, acceptability, and spoofing. Finally, we present multi-factor authentication challenges and security issues and present future directions.
, Philippe Dorey, Abigael Taylor
Published: 8 April 2022
Ai, Computer Science and Robotics Technology, Volume 2022, pp 1-18;

For now many years, illegal UAV (Unmanned Aerial Vehicle) flights have been observed in different countries and under various environments. The intent of such illegal flights may cover industrial espionage up to terrorist attacks. Countering such an asymmetric threat is now of increasing and challenging interest for many countries. The main compulsory functions for such an anti-UAV system will be briefly discussed from detection, localization, identification/classification, extraction (an UAV has to be isolated from other detections) to the alert function. After this introduction about the context, a description of a passive DVB-T (Digital Video Broadcasting Terrestrial) radar component will be given, and its potential, in regard to the previously described functions, will be illustrated using experimental results. Such a passive approach will be shortly compared with active radar components. Several measurement campaigns have been conducted with quite a huge variety of nano-small UAVs (multirotors such as ANAFI, Mavik, Phantom 4, F450 up to M600 as well as fixed wings such as ranger, Disco, X8 and X11) evolving under various configurations (bistatic bases, different weather conditions) and a selection of the most meaningful results will be presented.
, Chandrasekhar Kambhampati, Asma Alabad
Published: 28 March 2022
Ai, Computer Science and Robotics Technology, Volume 2022, pp 1-21;

This article provides an optimisation method using a Genetic Algorithm approach to apply feature selection techniques for large data sets to improve accuracy. This is achieved through improved classification, a reduced number of features, and furthermore it aids in interpreting the model. A clinical dataset, based on heart failure, is used to illustrate the nature of the problem and to show the effectiveness of the techniques developed. Clinical datasets are sometimes characterised as having many variables. For instance, blood biochemistry data has more than 60 variables that have led to complexities in developing predictions of outcomes using machine-learning and other algorithms. Hence, techniques to make them more tractable are required. Genetic Algorithms can provide an efficient and low numerically complex method for effectively selecting features. In this paper, a way to estimate the number of required variables is presented, and a genetic algorithm is used in a “wrapper” form to select features for a case study of heart failure data. Additionally, different initial populations and termination conditions are used to arrive at a set of optimal features, and these are then compared with the features obtained using traditional methodologies. The paper provides a framework for estimating the number of variables and generations required for a suitable solution.
Sharon Mistretta
Published: 28 March 2022
Ai, Computer Science and Robotics Technology, Volume 2022, pp 1-23;

The Metaverse is a 3D virtual environment already populated by our students. In the form of avatars, their unique personas happily collaborate in spaces such as Roblox and Minecraft. After two years of being fully online, remote learning became associated with fatigue from business-model video conferencing tools such as Zoom, Microsoft Teams, and Google Meet. It is time to consider the adoption of customizable Metaverse platforms where educators and their students can safely teach and learn again in the same space. This paper examines contagion theory to frame the breakdown of our classroom environment during the Covid-19 Pandemic and the transition back to a mosaic of hybrid contexts. This paper delineates the merits of the Metaverse as an alternative education space that fosters Universal Design for learning. Additionally, this paper reviews platforms that support multiple entry points for engagement, representation, action, and expression.
, M. Guadalupe Sánchez-Escribano
Published: 28 March 2022
Ai, Computer Science and Robotics Technology, Volume 2022, pp 1-19;

Memory in biological beings is as complex as the rational complexity of that concrete being requires. Clearly, memory helps to conform knowledge bases to serve the needs of the specific natural being. To analogize from Robotics concepts, it seems that the degrees of freedom in the biological being’s memory are higher or lower depending upon the rationality of each living being. Robots and artificial systems appear to require analogous structures. That is, to build a reactive system, the requirement of memory is not highly demanding with respect to the degrees of freedom. However, the required degrees of freedom seems to grow as the ability of the artificial system to deliberate increases. Consequently, to design artificial systems that would implement cognitive abilities, it is required to rethink memory structures. When designing a Cognitive Artificial System, memory systems should be thought of as highly accessible discrete units. In addition, these systems would require designs in the form of distributed architectures with non-linear features, such as those of human thought. In addition, they should allow for complex mixed types of data (text, images, time or so). Blockchain has attracted great interest for a few years now, especially since the appearance of Bitcoin. A blockchain is a distributed ledger that combines an append-only data structure designed to be resistant to modifications, with a consensus protocol [1, 2]. This innovation can be thought of as a sequence of containers, the blocks, that store two things: the information of a “system” and the “service” that such system provides [2], and it provides an interesting starting point to rethink memory systems in robots.
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