Optimal path finding with space‐ and time‐variant metric weights via multi‐layer CNN

Kim, Hyongsuk; Son, Hwa–Young; Roska, Tamás; Chua, Leon O.

doi:10.1002/cta.199

Cited by 22 publications

(3 citation statements)

References 14 publications

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“…By applying relation (11) to all the pixels from the robot neighborhood, the template elements for path.tem must satisfy an inequality system. For the pixel, that represents the future position, the inequality is given by the relation (12) and for the other pixels by the relation (13).…”

Section: Template Design For Path Planning Of a Mobile Robotmentioning

confidence: 99%

Vision based algorithm for path planning of a mobile robot by using cellular neural networks

Gavrilut

Gacsádi

Grava

et al. 2006

2006 IEEE International Conference on Automation, Quality and Testing, Robotics

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The paper presents a new vision based algorithm for mobile robots path planning in an environment with obstacles. Cellular Neural Networks (CNNs) processing techniques are used here for real time motion planning to reach a fixed target. The CNN methods have been considered a solution for image processing in autonomous mobile robots guidance. The choice of CNNs for the visual processing is based on the possibility of their hardware implementation in large networks on a single VLSI chip (Cellular Neural Networks -Universal Machine, CNN-UM [2], [11]).

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Section: Template Design For Path Planning Of a Mobile Robotmentioning

confidence: 99%

Vision based algorithm for path planning of a mobile robot by using cellular neural networks

Gavrilut

Gacsádi

Grava

et al. 2006

2006 IEEE International Conference on Automation, Quality and Testing, Robotics

View full text Add to dashboard Cite

show abstract

“…However, this method was not fully based on a CNN, since a CNN is only used as the first layer to produce the local currents of edges. Kim et al (2002) reported a multi-layer CNN to find the optimal path. In this study, the neural connections are symmetric and only a static environment is discussed without any discussions on a changing environment.…”

Section: Related Workmentioning

confidence: 99%

Optimal Robot Path Planning with Cellular Neural Network

Shirinzadeh

Zhong

Yuan

2011

International Journal of Intelligent Mechatronics and Robotics

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This paper presents a new methodology based on neural dynamics for optimal robot path planning by drawing an analogy between cellular neural network (CNN) and path planning of mobile robots. The target activity is treated as an energy source injected into the neural system and is propagated through the local connectivity of cells in the state space by neural dynamics. By formulating the local connectivity of cells as the local interaction of harmonic functions, an improved CNN model is established to propagate the target activity within the state space in the manner of physical heat conduction, which guarantees that the target and obstacles remain at the peak and the bottom of the activity landscape of the neural network. The proposed methodology cannot only generate real-time, smooth, optimal, and collision-free paths without any prior knowledge of the dynamic environment, but it can also easily respond to the real-time changes in dynamic environments. Further, the proposed methodology is parameter-independent and has an appropriate physical meaning.

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“…Our method is a global algorithm to compute the optimal light trace through taking the local minimum operation at each node. It is also an efficient solution when implemented with parallel processing of locally connected cells such as Cellular Neural Networks (CNN) [13]. This paper is organized as follows: Sect.…”

Section: Introductionmentioning

confidence: 99%

Accurate and Robust Surface Measurement Using Optimal Structured Light Tracking Method

Shi

Kim

Lin

et al. 2010

IEICE Trans. Inf. & Syst.

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SUMMARYStructured light vision systems are based on the fact that the pixel location of light in an image has a unique association with the object depth. However, their applications are mainly limited to the properties of the object surface and the lighting conditions. This paper presents a robust vision system for accurate acquisition of 3-D surface data based on optimal structured light. To achieve depth measurement for a dynamic scene, the data acquisition must be performed with only a single image. Our special arrangement makes the image of the light stripe remaining sharp while the background becomes blurred. Moreover, a dynamic programming approach is proposed to track the optimal path while the laser beam is invisible or divergent under extreme condition. The principle and necessary mathematics for implementing the algorithm are presented. The robustness of the system against uncalibrated errors is demonstrated. key words: structured light, dynamic programming, optimal trace, depth precision

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Optimal path finding with space‐ and time‐variant metric weights via multi‐layer CNN

Cited by 22 publications

References 14 publications

Vision based algorithm for path planning of a mobile robot by using cellular neural networks

Vision based algorithm for path planning of a mobile robot by using cellular neural networks

Optimal Robot Path Planning with Cellular Neural Network

Accurate and Robust Surface Measurement Using Optimal Structured Light Tracking Method

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