Adaptive Ant Colony Optimization with Sub-Population and Fuzzy Logic for 3D Laser Scanning Path Planning

For the precise measurement of complex surfaces, determining the position, direction, and path of a laser sensor probe is crucial before obtaining exact measurements. Accurate surface measurement hinges on modifying the overtures of a laser sensor and planning the scan path of the point laser displa...

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Vydané v:Sensors (Basel, Switzerland) Ročník 24; číslo 4; s. 1098
Hlavní autori: Song, Junfang, Pu, Yuanyuan, Xu, Xiaoyu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 08.02.2024
MDPI
Predmet:
Algorithms
Analysis
ant colony optimization
Fuzzy algorithms
Fuzzy logic
Fuzzy systems
Heuristic
intricate surface measurement
laser scan
Lasers
Learning strategies
Linear programming
Measuring instruments
Optimization algorithms
Pheromones
Planning
scan path planning
Scanners
Scanning devices
Sensors
intricate surface measurement
ant colony optimization
laser scan
scan path planning
fuzzy logic
ISSN:1424-8220, 1424-8220
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Shrnutí:For the precise measurement of complex surfaces, determining the position, direction, and path of a laser sensor probe is crucial before obtaining exact measurements. Accurate surface measurement hinges on modifying the overtures of a laser sensor and planning the scan path of the point laser displacement sensor probe to optimize the alignment of its measurement velocity and accuracy. This manuscript proposes a 3D surface laser scanning path planning technique that utilizes adaptive ant colony optimization with sub-population and fuzzy logic (SFACO), which involves the consideration of the measurement point layout, probe attitude, and path planning. Firstly, this study is based on a four-coordinate measuring machine paired with a point laser displacement sensor probe. The laser scanning four-coordinate measuring instrument is used to establish a coordinate system, and the relationship between them is transformed. The readings of each axis of the object being measured under the normal measuring attitude are then reversed through the coordinate system transformation, thus resulting in the optimal measuring attitude. The nominal distance matrix, which demonstrates the significance of the optimal measuring attitude, is then created based on the readings of all the points to be measured. Subsequently, a fuzzy ACO algorithm that integrates multiple swarm adaptive and dynamic domain structures is suggested to enhance the algorithm’s performance by refining and utilizing multiple swarm adaptive and fuzzy operators. The efficacy of the algorithm is verified through experiments with 13 popular TSP benchmark datasets, thereby demonstrating the complexity of the SFACO approach. Ultimately, the path planning problem of surface 3D laser scanning measurement is addressed by employing the proposed SFACO algorithm in conjunction with a nominal distance matrix.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s24041098