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A new evolving mechanism of genetic algorithm for multi-constraint intelligent camera path planning

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Abstract

The main goal of intelligent camera path planning is to determine an optimal pathway that proceeds from the starting position to the target position under several constraint conditions in the given environment. Genetic algorithm-based method has found wide application in path optimization problem in the intelligent camera community recently. Because the roaming environments are very complex, the planning path of the intelligent camera should meet other constraint conditions in addition to the path length constraint and the obstacle-free constraint. In this study, a new fitness function was developed in the genetic algorithm, which can consider the constraint conditions in terms of free obstacle, path length, path smoothness, and the visibility of the objective of interest in advance during the camera roaming. In addition, a new evolving operator was introduced into the genetic algorithm, so that the number of iteration can be significantly reduced, and thus, the efficiency of the genetic algorithm can be improved. Experimental results show that the proposed genetic algorithm can obtain a high-quality path under multi-constraint conditions for intelligent camera with less numbers of iteration as compared with several conventional methods.

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Authors and Affiliations

  1. College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China

    Zeqiu Chen, Jianghui Zhou, Ruizhi Sun & Li Kang

  2. Scientific Research Base for Integrated Technologies of Precision Agriculture (Animal Husbandry), the Ministry of Agriculture, Beijing, 100083, China

    Ruizhi Sun

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  1. Zeqiu Chen
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  2. Jianghui Zhou
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  3. Ruizhi Sun
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  4. Li Kang
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Correspondence to Li Kang.

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Chen, Z., Zhou, J., Sun, R. et al. A new evolving mechanism of genetic algorithm for multi-constraint intelligent camera path planning. Soft Comput 25, 5073–5092 (2021). https://doi.org/10.1007/s00500-020-05510-6

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