A Heuristic Operator-Based Quantum Ant Colony Optimization Algorithm for Robot Path Planning

You, Xiaoming; Liu, Sheng; Zhang, Chen

doi:10.14257/ijca.2017.10.4.17

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Chen et al used an improved ant colony algorithm to perform path planning for free-surface measuring points, and their optimized results were found to be superior to those achieved by taboo search and genetic algorithms [4]. Aiming at the path planning problem in a complex environment, You et al used the improved ant colony algorithm to not only accelerate the convergence speed but also effectively improve the quality of the optimal solution [5]. Sun Bo et al improved and optimized the genetic algorithm to make the improved algorithm more efficient in AGV path planning [6].…”

Section: Introductionmentioning

confidence: 99%

Path Optimization of Aircraft-Gear-Tooth-Surface Detection Based on Improved Genetic Algorithm

Chu,

Zhou

2024

Processes

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Aiming at the problems of low detection efficiency and complexity of aircraft gear tooth surfaces, a path optimization algorithm based on an improved genetic algorithm is proposed. The detection area of the tooth surface is planned, the sampling points of the tooth surface are determined based on the digital technology of the tooth surface, and the sampling mesh is obtained by the truncated plane method to reduce the sampling distortion of the shape and improve the sampling efficiency. Adaptive crossover and mutation probability are used to improve the convergence speed and accuracy of the genetic algorithm. The selected individuals of the binary tournament are used to guide the global optimal search by a simulated annealing algorithm, and the local optimal is avoided by the Metropolis criterion. In the simulation experiment, the proposed method and other algorithms are used to optimize the detection path. The optimized tooth-surface-detection path has the shortest distance and the shortest time, that is, the tooth-surface-detection path efficiency is improved, verifying the practicability of the algorithm.

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Section: Introductionmentioning

confidence: 99%

Path Optimization of Aircraft-Gear-Tooth-Surface Detection Based on Improved Genetic Algorithm

Chu,

Zhou

2024

Processes

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“…Ant colony optimization (ACO) was proposed by the Italian scholar Dorigo (DORIGO, 1992) in 1992. The algorithm is a meta-heuristic algorithm derived by imitating the behavior of ants searching for food sources (You et al , 2017). The ACO algorithm has been widely used in the field of mobile robot path planning in recent years because of its parallel processing, distributed computing and strong robustness (Jiang et al , 2019; Xu et al , 2021).…”

Section: Introductionmentioning

confidence: 99%

Research on robot path planning with safe distance optimization

Xiangde,

Hao,

et al. 2024

RIA

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Purpose With the development of technology, the application scenarios of mobile robots are becoming more and more extensive, accompanied by a variety of application scenarios suitable and safe path planning algorithms are indispensable for mobile robots. Design/methodology/approach The purpose of this paper to improve the safety performance of your bot during the execution of tasks. The methods are synthesized in three main areas: setting appropriate safety distances based on the actual radius of the robot, turn penalty reduces the number of turns by applying an additional penalty to the number of turns in a heuristic function and path smoothing is used to improve path reliability by reducing the number of right-angle turns. Findings A suitable safety distance greatly improves the safety of mobile robots and facilitates their development. Optimization of turns in the path of mobile robots improves the travel efficiency of robots. Enhancing the safety of mobile robots has become a research hotspot for path-planning algorithms. Originality/value This paper proposes a path planning scheme for mobile robots with safe distances, which provides readers with a comprehensive and systematic progress of path planning research. It helps readers to get inspiration from enhancing the safety of mobile robots.

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