Open AccessImproved Genetic Algorithm-Based Impulse Optimization for Cable-driven Camera Robot
Author(s) -
Siqi Zhao,
Yu Su,
Quing Zhu
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2228/1/012016
Subject(s) - adaptability , genetic algorithm , impulse (physics) , robot , computer science , premature convergence , convergence (economics) , algorithm , meta optimization , simulation , mathematical optimization , artificial intelligence , mathematics , machine learning , ecology , physics , quantum mechanics , economics , biology , economic growth
Cable-driven camera robot has the characteristics of high load/mass ratio, large working space, fast response speed and strong environmental adaptability, which has a wide application prospect and important development value. However, the high-speed movement of the camera robot has a great impact on its performance, and an optimal design method that meets its actual characteristics is needed to improve its overall performance. In view of the shortcomings of the standard genetic algorithm, such as premature convergence and insufficient global optimization searching ability, an improved genetic algorithm is proposed. Compared with the optimization results of the standard genetic algorithm, The improved genetic algorithm converges to the global optimal solution and the impulse decreases obviously, it shows that the improved genetic algorithm is more effective and practical, and reduces the impact of cable tension, thus obtaining better camera robot design parameters.