Application of vision measurement model with an improved moth-flame optimization algorithm

Ya-ru, LI; Wang, Zhongyu; Cheng, Yinbao; Tang, Yingqi; Shang, Zhendong

doi:10.1364/oe.27.020800

Cited by 4 publications

(5 citation statements)

References 21 publications

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“…The MFO algorithm uses a unique method in which each moth updates its localization using only the unique flame corresponding to it, which effectively prevents the computer from entering local extremes and thus greatly enhances the global search function of the computer. Therefore, in the search space, the moth height and the flame height are a matrix of changes in a common angle [11]. The matrix F represents the height of the fire, while the matrix OF records the fitness value of the fire for better control and management.…”

Section: Moth-flame Optimization Algorithmmentioning

confidence: 99%

“…The moths are updated using a logarithmic helix, its definition is shown in Equation ( 11) Equation (11) describes the movement path of the moth during different positions, where t represents the position where the moth is closest to the fire, and t=1 represents the position where the moth is farthest from the fire. With this equation, the trajectory of the moth's movement can be predicted more accurately and thus its flight direction can be better controlled.…”

Section: Moth-flame Optimization Algorithmmentioning

confidence: 99%

“…ZHANG Y proposed an improved Moth Flame Optimization algorithm based on sinusoidal mapping and Gaussian variance to address the problems of the standard Moth Flame Optimization algorithm with low search accuracy and easy to fall into local optimal solutions [10].Li Y R proposed an improved Moth Flame Optimization (IMFO) algorithm in order to improve the localization accuracy of the visual measurement system. This algorithm optimizes the initial pose parameters by improving a series of random solutions to achieve the desired accuracy [11].…”

Section: Introductionmentioning

confidence: 99%

“…returning to step(3) and iteratively updating until a predetermined maximum number of iterations is reached (11). Output the spatial location of the best moth individual searched and the corresponding fitness function value, which is the final spatial location of the flame and the fitness function value, and the algorithm ends.The flow chart is shown in Figure3.START Select the optimal individual value by greedy mechanism Find a new flame Whether the stagnation count is reached Record the optimal flame position Update the moth position Crossover and mutation methods to save the optimal…”

mentioning

confidence: 99%

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Parameter identification based on resistance furnace model

Sun

2023

International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023)

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With the popularity of heat treatment technology, the accuracy of temperature control and the correctness of control method selection are crucial. More and more researchers combine genetic algorithms, model predictive control algorithms, and particle swarm algorithms for the parameter identification of resistance furnace models have disadvantages such as local optimum and slow convergence speed. Therefore, the paper establishes an approach to the parameter identification of resistance furnace model based on the Improved Moth-flame optimization algorithm (IMFO) algorithm, selects the Particle Swarm Optimization (PSO) and Moth-flame optimization algorithm (MFO) as comparison algorithms, and obtains the parameter identification results of the above three intelligent optimization algorithms through MATLAB simulation tests and compares them with the actual resistance furnace output. It is found that the IMFO can identify the resistance furnace temperature sintering process more accurately, and the parameter identification results of the resistance furnace model using the IMFO are better fitted with the actual resistance furnace parameter variation curve. Compared with Moth-flame optimization algorithm (MFO) and Particle Swarm Optimization (PSO), the Improved Moth-flame optimization algorithm (IMFO) has the advantages of fast convergence, solving the local optimum problem, and improving the speed of finding the optimum, etc. It is very effective in the field of parameter identification of furnace temperature sintering process and better meets the needs of general industrial control.

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Section: Moth-flame Optimization Algorithmmentioning

confidence: 99%

Section: Moth-flame Optimization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Parameter identification based on resistance furnace model

Sun

2023

International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023)

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“…A checkerboard can be used to provide target pose information. In [9,10], a method with pairwise parallel and intersecting relationships of a four-point coplanar was used to solve the positioning problem.…”

Section: Introductionmentioning

confidence: 99%

Application of a Vision-Based Single Target on Robot Positioning System

Jiang

Luo

et al. 2021

Sensors

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In this paper, we propose a Circular-ring visual location marker based on a global image-matching model to improve the positioning ability in the fiducial marker system of a single-target mobile robot. The unique coding information is designed according to the cross-ratio invariance of the projective theorem. To verify the accuracy of full 6D pose estimation using the Circular-ring marker, a 6 degree of freedom (DoF) robotic arm platform is used to design a visual location experiment. The experimental result shows in terms of small resolution images, different size markers, and long-distance tests that our proposed robot positioning method significantly outperforms AprilTag, ArUco, and Checkerboard. Furthermore, through a repeatable robot positioning experiment, the results indicated that the proposed Circular-ring marker is twice as accurate as the fiducial marker at 2–4 m. In terms of recognition speed, the Circular-ring marker processes a frame within 0.077 s. When the Circular-ring marker is used for robot positioning at 2–4 m, the maximum average translation error of the Circular-ring marker is 2.19, 3.04, and 9.44 mm. The maximum average rotation error is also 1.703°, 1.468°, and 0.782°.

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