Automation of Synthesized Optimal Control Problem Solution for Mobile Robot by Genetic Programming

Diveev, Askhat; Sofronova, Elena

doi:10.1007/978-3-030-29513-4_77

Cited by 13 publications

(4 citation statements)

References 8 publications

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“…Nowadays, many symbolic regression methods are known. Let us name some of them: GP [1], analytic programming [4], grammatical evolution [2], Cartesian GP [3], inductive genetic programming [13], network operator method [5], parser-matrix evolution [6], and complete binary GP [7]. Only eight symbolic regression methods are listed here.…”

Section: Small Variations For Symbolic Regression Methodsmentioning

confidence: 99%

“…Nowadays, there are many symbolic regression methods, such as grammatical evolution [2], Cartesian GP [3], analytic programming [4], network operator method [5], parser-matrix evolution [6], complete binary GP [7] including sparse regression [8][9][10], and others [11][12][13][14][15], for finding solutions to various non-numerical optimization problems in which it is necessary to find optimal structures, graphs, constructions, formulas, mathematical expressions, schemes, etc. Some known applications in different areas are robotics [16], code cracking [17], design of antennas [18], Rubik's cube solving [19], "deriving" partial differential equations [20], control synthesis [21], extraction of explicit physical relations [22,23], etc.…”

Section: Introductionmentioning

confidence: 99%

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Universal Approach to Solution of Optimization Problems by Symbolic Regression

Sofronova

Diveev

2021

Applied Sciences

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Optimization problems and their solution by symbolic regression methods are considered. The search is performed on non-Euclidean space. In such spaces it is impossible to determine a distance between two potential solutions and, therefore, algorithms using arithmetic operations of multiplication and addition are not used there. The search of optimal solution is performed on the space of codes. It is proposed that the principle of small variations of basic solution be applied as a universal approach to create search algorithms. Small variations cause a neighborhood of a potential solution, and the solution is searched for within this neighborhood. The concept of inheritance property is introduced. It is shown that for non-Euclidean search space, the application of evolution and small variations of possible solutions is effective. Examples of using the principle of small variation of basic solution for different symbolic regression methods are presented.

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Section: Small Variations For Symbolic Regression Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Universal Approach to Solution of Optimization Problems by Symbolic Regression

Sofronova

Diveev

2021

Applied Sciences

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“…This particular methodology, within the context of established nomenclature, is commonly referred to as unsupervised learning. Currently, a variety of symbolic regression techniques exist, including genetic programming (GP) [73], Cartesian GP [74], parse matrix evolution [75], network operator method [76], complete binary GP [77] and others.…”

Section: Symbolic Regression Techniquesmentioning

confidence: 99%

Elevating Mobile Robotics: Pioneering Applications of Artificial Intelligence and Machine Learning

Nasrallah,

Stepanyan,

Nassrullah

et al. 2024

RIA

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The present study delves into the utilization of subsumption architecture for the modeling of mobile robot behaviors, particularly those that respond adaptively to environmental dynamics and inaccuracies in sensor measurements. Central to this investigation is the deployment of reactive controller networks, wherein each node-representing a distinct state-is governed by sensor-triggered conditions that dictate state transitions. The methodology adopted comprises a thorough literature review, encompassing sources from IEEE Xplore, ScienceDirect, and the ACM Digital Library, which discuss the integration of subsumption architecture in the realm of mobile robot control. Through this review, the effectiveness of subsumption architecture in crafting reactive robotic behaviors is underscored. It has been established that augmented finite state machines (AFSMs), which are integral to the subsumption architecture and possess internal timing mechanisms, are pivotal in managing the temporal aspects of state transitions. Additionally, the technique of layering-merging multiple simple networks to form intricate behavior patterns-emerges as a significant finding, accentuating the architecture's capability to facilitate complex behavioral constructs. The prime contribution of this body of work lies in identifying and elucidating the strategic role of subsumption architecture in enhancing the adaptability and robustness of mobile robots. The insights gleaned from this study not only advance our understanding of robotic control systems but also hold implications for the amplification of industrial efficiency and effectiveness through the application of sophisticated AI and machine learning techniques in mobile robotics.

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“…EC comprises techniques such as evolutionary programming [ 5 , 6 , 7 ], evolutionary strategies [ 8 ], genetic algorithms [ 9 ] and genetic programming (GP) [ 10 , 11 , 12 , 13 , 14 ]. GP has been used extensively on conventional robots programming such us [ 15 , 16 , 17 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

A Genetic Programming-Based Low-Level Instructions Robot for Realtimebattle

Romero

Santos

Carballal

et al. 2020

Entropy

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RealTimeBattle is an environment in which robots controlled by programs fight each other. Programs control the simulated robots using low-level messages (e.g., turn radar, accelerate). Unlike other tools like Robocode, each of these robots can be developed using different programming languages. Our purpose is to generate, without human programming or other intervention, a robot that is highly competitive in RealTimeBattle. To that end, we implemented an Evolutionary Computation technique: Genetic Programming. The robot controllers created in the course of the experiments exhibit several different and effective combat strategies such as avoidance, sniping, encircling and shooting. To further improve their performance, we propose a function-set that includes short-term memory mechanisms, which allowed us to evolve a robot that is superior to all of the rivals used for its training. The robot was also tested in a bout with the winner of the previous “RealTimeBattle Championship”, which it won. Finally, our robot was tested in a multi-robot battle arena, with five simultaneous opponents, and obtained the best results among the contenders.

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Automation of Synthesized Optimal Control Problem Solution for Mobile Robot by Genetic Programming

Cited by 13 publications

References 8 publications

Universal Approach to Solution of Optimization Problems by Symbolic Regression

Universal Approach to Solution of Optimization Problems by Symbolic Regression

Elevating Mobile Robotics: Pioneering Applications of Artificial Intelligence and Machine Learning

A Genetic Programming-Based Low-Level Instructions Robot for Realtimebattle

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