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Discussion on different controllers used for the navigation of mobile robot

Raj Ravi, Kos Andrzej

International Journal of Electronics and Telecommunications

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2024

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Vol. 70, No. 1

229--239

EN

Robots that can comprehend and navigate their surroundings independently on their own are considered intelligent mobile robots (MR). Using a sophisticated set of controllers, artificial intelligence (AI), deep learning (DL), machine learning (ML), sensors, and computation for navigation, MR's can understand and navigate around their environments without even being connected to a cabled source of power. Mobility and intelligence are fundamental drivers of autonomous robots that are intended for their planned operations. They are becoming popular in a variety of fields, including business, industry, healthcare, education, government, agriculture, military operations, and even domestic settings, to optimize everyday activities. We describe different controllers, including proportional integral derivative (PID) controllers, model predictive controllers (MPCs), fuzzy logic controllers (FLCs), and reinforcement learning controllers used in robotics science. The main objective of this article is to demonstrate a comprehensive idea and basic working principle of controllers utilized by mobile robots (MR) for navigation. This work thoroughly investigates several available books and literature to provide a better understanding of the navigation strategies taken by MR. Future research trends and possible challenges to optimizing the MR navigation system are also discussed.

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Optimizing complex systems by intelligent evolotion: the LEMd method and case study

Michalski R.S.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2006

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Vol. 54, nr 4

505-513

EN

Most methods of evolutionary computation follow a Darwinian-type model that proceeds through random mutations or recombinations of the genetic material and natural selection of individuals carried out according to the principle of the survival of the fittest. In such a model, the creation of new individuals is not guided by any reasoning process or "external mind", but rather by random or semi-random changes. Recently, a new, non-Darwinian approach to evolutionary computation bas been proposed, called Learnable Evolution Model (LEM), in which the evolutionary process is guided by computational intelligence. In LEM, a new way of creating individuals is proposed, namely, by hypothesis formation and instantiation. In numerous experiments, LEM bas consistently and significantly outperformed compared conventional Darwinian-type algorithms in terms of the evolution length (the number of fitness evaluations) in solving complex function optimization problems. Based on the LEM ideas, we developed a method, called LEMd, which is tailored to problems of optimizing very complex engineering systems. This article provides a brief description of LEMd and its application to the development of a specialized system, ISHED, for the optimization of evaporator designs in cooling systems. According to experts in cooling systems, ISHED-developed designs have matched or outperformed the best human designs. These results and those from the experimental testing of learnable evolution on problems with hundreds of variables suggest that LEMd may be an attractive new tool for optimizing very complex engineering systems.

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Evaluating concrete materials by application of automatic reasoning

Alterman D., Kasperkiewicz J.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2006

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Vol. 54, nr 4

353-361

EN

There were two aims of the research. One was to enable more or less automatic confirmation of the known associations - either quantitative or qualitative - between technological data and selected properties of concrete materials. Even more important is the second aim - demonstration of expected possibility of automatic identification of new such relation-ships, not yet recognized by civil engineers. The relationships are to be obtained by methods of Artificial lntelligence, (Al), and are to be based on actual results from experiments on concrete materials. The reason of applying the Al tools is that in Civil Engi-neering the real data are typically non perfect, complex, fuzzy, often with missing details, which means that their analysis in a traditional way, by building empirical models, is hardly possible or at least can not be done quickly. The main idea of the proposed approach was to combine application of different Al methods in a one system, aimed at es-timation, prediction, design and/or optimization of composite materials. The paradigm of the approach is that the unknown rules concerning the properties of concrete are hidden in experimental results and can be obtained from the analysis of examples. Different Al techniques like artificial neural networks, machine learning and certain techniques related to statistics were applied. The data for the analysis originated from direct observations and from reports and publications on concrete technology. Among others it has been demonstrated that by combining different Al methods it is possible to improve the quality of the data, (e.g. when encountering outliers and missing values or in clustering problems), so that the whole data processing system will be giving better prediction, (when applying ANNs), or the newly discovered rules will be more effective, (e.g. with descriptions more complete and - at the same time - possibly more consistent, in case of ML algorithms).