High-precision indoor three-dimensional positioning system based on visible light communication using modified artificial fish swarm algorithm

Wen, Shangsheng; Cai, Xiaodan; Guan, Weipeng; Jiang, Jiajia; Chen, Bangdong; Huang, Mouxiao

doi:10.1117/1.oe.57.10.106102

Cited by 10 publications

(5 citation statements)

References 26 publications

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“…In [91] ACO utilizes the global search property to identify the optimal PD location point, and the parallel search property to correct the deviation of the intensity attenuation factors, with the average 3D positioning error at 2 cm, 4 cm, and 8 cm when SNR is 30 dB, 20 dB, and 10 dB, respectively. Authors in [95], [96] transform the 3D positioning problem of a PD target, into a global optimization problem, and a fitness function is developed based on the Lambertian radiation pattern to achieve higher accuracy and lower system complexity. The positioning problem can be addressed with MFOA, where the strategy of the fly group in the traditional fruit fly optimization algorithm is altered, and an adaptive search scope is utilized to obtain an average 3D positioning accuracy of 0.76 cm.…”

Section: ) Eas In Vlp Formulationsmentioning

confidence: 99%

Artificial Intelligence in Visible Light Positioning for Indoor IoT: A Methodological Review

Rekkas,

Iliadis,

Sotiroudis

et al. 2023

IEEE Open J. Commun. Soc.

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Section: ) Eas In Vlp Formulationsmentioning

confidence: 99%

Artificial Intelligence in Visible Light Positioning for Indoor IoT: A Methodological Review

Rekkas,

Iliadis,

Sotiroudis

et al. 2023

IEEE Open J. Commun. Soc.

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“…It is worth mentioning that iterative methods have also been utilized in the open technical literature to achieve better accuracy. In [31], a 3D VLP for a horizontally moving MU under four LEDs is proposed based on a modified Artificial Fish Algorithm. Similarly, in [32], an MU position is recovered in 3D using the bat evolutionary algorithm, and accuracy is reported for three different noise levels (3, 5, 15dB).…”

Section: A Related Workmentioning

confidence: 99%

“…In comparison with other studies suggesting iteration methods, we have to mention the following. In [31], the reported VLP achieves a few centimeter positional accuracy for a 3D system, requiring 5 to 10 generations for a population of 50 individuals to converge. Thus a total number of 250 to 500 iterations is necessary for accurate position recovery.…”

Section: E Enhancing Accuracy By Position Averagingmentioning

confidence: 99%

A Low Complexity Indoor Visible Light Positioning Method

et al. 2021

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The continuous positioning of a mobile user equipment (MU) in a prompt way is a fundamental requirement in several indoor applications related to ubiquitous computation, assisted living environments, and security/surveillance systems. Motivated by this fact, the current contribution reports a low-complexity visible light positioning (VLP) method suitable for indoor environments in the Big Data era. The proposed architecture consists of multiple light-emitting diodes (LEDs) as light sources and an MU equipped with a photodiode (PD). To guarantee higher spectral efficiency, the LEDs emit sinusoidal waveforms at slightly different predetermined frequencies. The light intensity received at the PD from every LED is continuously estimated after applying a short-time Fourier transformation. To this end, a grid-based numerical technique is proposed to recover the unknown MU position. Additionally, a closedform solution is presented for the particular case of three LEDs that overrides the need for training points in the grid arrangement. Further, a method for handling noisy signals is proposed, based on averaging the calculated positions from densely overlapping received signals. Finally, a Kalman filter is employed as a post-processing precision improving tool. The proposed VLP efficiency is quantified through respective Monte Carlo simulations that allow the setting of different LED frequencies, received signal processing parameters, MU speed, and noise levels. The results reveal that the suggested approach is robust with significant tolerance in high ambient light levels, computationally efficient, and exhibits low positional error. Finally, to evaluate the performance improvements the new method introduces, we make comparisons against widely-used fingerprint approaches. INDEX TERMSIndoor positioning, Kalman filtering, Localization methods, Visible light.

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“…The artificial fish swarm algorithm (AFSA) is a novel swarm intelligent optimization method inspired by natural fish swarm behavior. It has been successfully used in the field of wireless telemedicine systems [16], fault diagnosis [17], indoor visible light positioning [18], floating wind turbines [19], etc.…”

Section: Hybrid Artificial Fish Swarm Algorithmmentioning

confidence: 99%

“…As shown in Figure 4, the initial poses of the three mobile robots were (1, 1, 0), (1,5,0) and (1, 9, 0). The goal positions were (10,14), (17,1) and (18,13). The global path planning trajectories of the three mobile robots are shown in Figure 5.…”

Section: Simulation Experimentsmentioning

confidence: 99%

Cooperative Path Planning for Multiple Mobile Robots via HAFSA and an Expansion Logic Strategy

Huang

Jiang

et al. 2019

Applied Sciences

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The cooperative path planning problem of multiple mobile robots in an unknown indoor environment is considered in this article. We presented a novel obstacle avoidance and real-time navigation algorithm. The proposed approach consisted of global path planning and local path planning via HAFSA (hybrid artificial fish swarm algorithm) and an expansion logic strategy. Meanwhile, a kind of scoring function was developed, which shortened the time of local path planning and improved the decision-making ability of the path planning algorithm. Finally, using STDR (simple two dimensional robot simulator) and RVIZ (robot operating system visualizer), a multiple mobile robot simulation platform was designed to verify the presented real-time navigation algorithm. Simulation experiments were performed to validate the effectiveness of the proposed path planning method for multiple mobile robots.

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High-precision indoor three-dimensional positioning system based on visible light communication using modified artificial fish swarm algorithm

Cited by 10 publications

References 26 publications

Artificial Intelligence in Visible Light Positioning for Indoor IoT: A Methodological Review

Artificial Intelligence in Visible Light Positioning for Indoor IoT: A Methodological Review

A Low Complexity Indoor Visible Light Positioning Method

Cooperative Path Planning for Multiple Mobile Robots via HAFSA and an Expansion Logic Strategy

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