Chaotic Path Planning for 3D Area Coverage Using a Pseudo-Random Bit Generator from a 1D Chaotic Map

Moysis, Lazaros; Rajagopal, Karthikeyan; Тутуева, Александра; Volos, Christos; Teka, Beteley; Butusov, Denis N.

doi:10.3390/math9151821

Cited by 14 publications

(9 citation statements)

References 54 publications

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“…Random numbers are needed in a variety of applications, yet finding good random number generators (RNG) is a difficult task. In recent years, some new results of building pseudorandom number generators (PRNGs) based on chaos and chaotic map were presented (10), (11) (12) (13) and in work (14).…”

Section: Random Number Generators Overviewmentioning

confidence: 99%

High Performance Parallel Pseudorandom Number Generator on Cellular Automata

Levina¹,

Mukhamedjanov²,

Bogaevskiy³

et al. 2022

Preprint

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Nowadays the practice of developing algorithms to maintain the confidentiality of data shows that there is a lack of some features, such as velocity, predictability, etc. Generating pseudorandom numbers is one such problem that lies in the basement of many algorithms, even in hardware microprograms. An unreliable generator can cause cyberattacks on it, despite the security in the upper layers. At the same time, the algorithm should be fast enough to provide uninterrupted circuit work for the entire system. The paper presents a new algorithm generating pseudorandom numbers on cellular automata, which is not only fast and easy-repeating, but unpredictable enough and can be used in cryptographic systems. Using the NIST statistical test suite for random and pseudorandom number generators (PRNG), it is shown that the presented algorithm is more than three times superior to the state-of-the-art methods and algorithms in terms of ? − ?????. A high level of the presented algorithm’s parallelization allows for implementation it effectively on calculators with parallel structure. CPU-based architecture, FPGA-based architecture, CUDA- based architecture of PRNG and different PRNG implementations are presented to confirm high performance of the proposed solution.

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Section: Random Number Generators Overviewmentioning

confidence: 99%

High Performance Parallel Pseudorandom Number Generator on Cellular Automata

Levina¹,

Mukhamedjanov²,

Bogaevskiy³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

Section: Random Number Generators Overviewmentioning

confidence: 99%

High Performance Parallel Pseudorandom Number Generator on Cellular Automata

et al. 2022

View full text Add to dashboard Cite

Nowadays, the practice of developing algorithms to maintain the confidentiality of data shows that there is a lack of some features, such as velocity, predictability, etc. Generating pseudorandom numbers is one such problem that lies in the basement of many algorithms, even in hardware microprograms. An unreliable generator can cause cyberattacks on it, despite the security in the upper layers. At the same time, the algorithm should be fast enough to provide uninterrupted circuit work for the entire system. The paper presents a new algorithm generating pseudorandom numbers on cellular automata, which is not only fast and easy-repeating, but unpredictable enough and can be used in cryptographic systems. One of the main tasks of pseudorandom number generators (PRNG) is to present a high level of nonlinearity, or as it can also be named, asymmetry. Using the National Institute of Standards and Technology (NIST) statistical test suite for random number generators and pseudorandom number generators, it is shown that the presented algorithm is more than three times superior to the state-of-the-art methods and algorithms in terms of p-value. A high level of the presented algorithm’s parallelization allows for implementation effectively on calculators with parallel structure. Central Processing Unit (CPU)-based architecture, Field-Programmable Gate Array (FPGA)-based architecture, Compute Unified Device Architecture (CUDA)-based architecture of PRNG and different PRNG implementations are presented to confirm the high performance of the proposed solution.

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“…To compare our algorithm with existing state-of-the-art solutions, two previously reported algorithms were implemented. The algorithm described in [43] is a straightforward one and does not perform any processing with the sequence of bits put out to the input of the algorithm. A more advanced version of this method is presented in [23], where some processing is applied, e.g., in the case of return to the original points, and, thereby, the speed of exploration of the area increases, reducing a key disadvantage of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Chaotic Path-Planning Algorithm Based on Courbage–Nekorkin Artificial Neuron Model

Kvitko,

Rybin,

Bayazitov

et al. 2024

Mathematics

Self Cite

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Developing efficient path-planning algorithms is an essential topic in modern robotics and control theory. Autonomous rovers and wheeled and tracked robots require path generators that can efficiently cover the explorable space with minimal redundancy. In this paper, we present a new path-planning algorithm based on the chaotic behavior of the Courbage–Nekorkin neuron model with a coverage control parameter. Our study aims to reduce the number of iterations required to cover the chosen investigated area, which is a typical efficiency criterion for this class of algorithms. To achieve this goal, we implemented a pseudorandom bit generator (PRBG) based on a Courbage–Nekorkin chaotic map, which demonstrates chaotic behavior and successfully passes all statistical tests for randomness. The proposed PRBG generates a bit sequence that can be used to move the tracked robot in four or eight directions in an operation area of arbitrary size. Several statistical metrics were applied to evaluate the algorithm’s performance, including the percentage of coverage of the study area and the uniformity of coverage. The performance of several competing path-planning algorithms was analyzed using the chosen metrics when exploring two test areas of the sizes 50 × 50 cells and 100 × 100 cells, respectively, in four and eight directions. The experimental results indicate that the proposed algorithm is superior compared to known chaotic path-planning methods, providing more rapid and uniform coverage with the possibility of controlling the covered area using tunable parameters. In addition, this study revealed the high dependence of the coverage rate on the starting point. To investigate how the coverage rate depends on the choice of chaotic map, we implemented six different PRBGs using various chaotic maps. The obtained results can be efficiently used for solving path-planning tasks in both real-life and virtual (e.g., video games) applications.

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Chaotic Path Planning for 3D Area Coverage Using a Pseudo-Random Bit Generator from a 1D Chaotic Map

Cited by 14 publications

References 54 publications

High Performance Parallel Pseudorandom Number Generator on Cellular Automata

High Performance Parallel Pseudorandom Number Generator on Cellular Automata

High Performance Parallel Pseudorandom Number Generator on Cellular Automata

Chaotic Path-Planning Algorithm Based on Courbage–Nekorkin Artificial Neuron Model

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