Guided Autowave Pulse Coupled Neural Network (GAPCNN) based real time path planning and an obstacle avoidance scheme for mobile robots

Syed, Usman Ahmed; Kunwar, Faraz; Iqbal, Mazhar

doi:10.1016/j.robot.2013.12.004

Cited by 51 publications

(26 citation statements)

References 20 publications

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“…In addition, various types of artificial neural networks were utilized to extract optimum paths. A Guided Autowave Pulse Coupled Neural Network [59] and a Deep Convolutional Neural Network [21] were applied to create collision free trajectories for mobile robots. Despite their efficiency, special hardware resources and/or centralized control are required for their implementation to real robotic systems.…”

Section: Shortest Path Definition Based On Cellular Structures For Comentioning

confidence: 99%

Cellular Automata Applications in Shortest Path Problem

Tsompanas

Dourvas

Ioannidis

et al. 2018

Emergence, Complexity and Computation

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Cellular Automata (CAs) are computational models that can capture the essential features of systems in which global behavior emerges from the collective effect of simple components, which interact locally. During the last decades, CAs have been extensively used for mimicking several natural processes and systems to find fine solutions in many complex hard to solve computer science and engineering problems. Among them, the shortest path problem is one of the most pronounced and highly studied problems that scientists have been trying to tackle by using a plethora of methodologies and even unconventional approaches. The proposed solutions are mainly justified by their ability to provide a correct solution in a better time complexity than the renowned Dijkstra's algorithm.

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Section: Shortest Path Definition Based On Cellular Structures For Comentioning

confidence: 99%

Cellular Automata Applications in Shortest Path Problem

Tsompanas

Dourvas

Ioannidis

et al. 2018

Emergence, Complexity and Computation

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show abstract

“…Nevertheless, they tend to consume expensive computation and easily fall into traps in complex problems. Comparing to traditional methods, heuristic algorithms have been proven to be efficient in robot path planning [6], including neural network [7], fuzzy logic technique [8] and nature spired algorithms such as genetic algorithm (GA) [9], particle swarm optimization (PSO) [10] and ant colony algorithm (ACO) [11]. The global searching ability of a good path planning method should be strong, as well as characterized by stability.…”

Section: Introductionmentioning

confidence: 99%

A New Robot Navigation Algorithm Based on a Double-Layer Ant Algorithm and Trajectory Optimization

Yang

Miao

et al. 2019

IEEE Trans. Ind. Electron.

130

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This paper presents an efficient double-layer ant colony algorithm, called DL-ACO, for autonomous robot navigation. This DL-ACO consists of two ant colony algorithms which run independently and successively. First, a parallel elite ant colony optimization (PEACO) method is proposed to generate an initial collision-free path in a complex map, and then we apply a path improvement algorithm called turning point optimization algorithm (TPOA), in which the initial path is optimized in terms of length, smoothness and safety. Besides, a piecewise B-spline path smoother is presented for easier tracking control of the mobile robot. Our method is tested by simulations and compared with other path planning algorithms. The results show that our method can generate better collision-free path efficiently and consistently, which demonstrates the effectiveness of the proposed algorithm. Furthermore, its performance is validated by experiments in indoor and outdoor environments.

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“…In other study, they used the new deep residual convolutional neural network for image denoising, and the results showed that its denoising effect was better than other algorithms [3]. Some of researchers proposed a pulse-coupled neural network with multi-channel link and feed field, which can be used in satellite image segmentation to improve the processing speed [4]. In other hand, researchers showed that deep convolutional neural network can significantly improve image denoising, but it lacks fine high-frequency detail processing [5].…”

Section: Introductionmentioning

confidence: 99%

Effect of Functional Motion Image Analysis Under Multi-Layer Convolutional Neural Network on Improving Speed and Force

Long¹,

Thomas²

2020

Malays. sport. j.

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To explore the effect of applying multi-layer convolutional neural network to noise removal during the acquisition and transmission of functional motion images, the classical image denoising algorithms of mean filtering, median filtering, and wavelet transform filtering are first introduced. In addition, the evaluation methods of mean square error (MSE), image enhancement factor (IEF), peak signal to noise ratio (PSNR) and structural similarity measure (SSIM) in the image quality evaluation index system are introduced. Based on the convolutional neural network model, a multi-scale parallel convolutional neural network (MP-CNN) model is constructed to remove the noise in the image, and the functional action image is devoted to different degrees. Finally, the denoising effect is evaluated by the objective and subjective evaluation system. The objective evaluation results show that MP-CNN's MSE, IEF, PSNR, and SSIM are better than the single-channel model, and the test time is shorter. The subjective evaluation results show that the MP-CNN model has the best effect on noise removal after 25 denoising of functional action images. In this study, a multi-channel image denoising model based on the multi-layer convolutional neural network can improve the effect of functional motion image noise removal. KEYWORDSMulti-layer convolutional neural network, Functional action-image, Noise, Evaluation system METHODOLOGY Image denoising algorithmNoise generally has the characteristics of random distribution, and the probability density function can be used to describe the noise distribution, which can be divided into Gaussian, Rayleigh, exponent, γ, uniform and impulse noise and so on. The classical denoising methods include mean filtering, median filtering and wavelet transform filtering. Mean filtering is a common spatial linear filtering method, which plays an obvious role in smoothing noise, and the Box template is often used to filter noise images.

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Guided Autowave Pulse Coupled Neural Network (GAPCNN) based real time path planning and an obstacle avoidance scheme for mobile robots

Cited by 51 publications

References 20 publications

Cellular Automata Applications in Shortest Path Problem

Cellular Automata Applications in Shortest Path Problem

A New Robot Navigation Algorithm Based on a Double-Layer Ant Algorithm and Trajectory Optimization

Effect of Functional Motion Image Analysis Under Multi-Layer Convolutional Neural Network on Improving Speed and Force

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