基于无人系统的智能视觉控制算法研究
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桂林电子科技大学

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TP

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Research on Intelligent Visual Control Algorithm Based on Unmanned Systems
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    摘要:

    无人系统应用范围的急剧扩大,使得视觉感知环境愈加复杂多变,致使传统视觉控制算法难以有效控制视觉传感器获取精准的视觉感知图像,从而影响无人系统的稳定运行,故提出基于无人系统的智能视觉控制算法研究。应用Gamma曲线非线性变换无人系统视觉感知图像灰度值,再应用灰度世界法来增强图像的对比度。以处理后的图像为基础,计算其图像矩,即空间矩、中心矩和归一化中心矩,以描述图像的全局和局部特性。根据得到的无人系统视觉感知信息,搭建智能视觉控制框架。获取期望图像特征矩阵,提取当前时刻图像特征矩阵,通过基于改进萤火虫算法的极限学习机对摄像机转角进行非线性映射,从而获取智能视觉控制定律,以此消除视觉感知图像误差,实现智能视觉的有效控制。实验结果显示:在不同实验组别背景下,应用提出算法获得的视觉控制平均时间最小值达到了1s,视觉控制平均误差最小值达到了0.12%,充分证实了提出算法的应用性能更佳。

    Abstract:

    The rapid expansion of the application range of unmanned systems makes the visual perception environment more complex and changeable, which makes it difficult for traditional visual control algorithms to effectively control visual sensors to obtain accurate visual perception images, thus affecting the stable operation of unmanned systems. Therefore, the research on intelligent visual control algorithms based on unmanned systems is proposed. The gray value of the visual perception image of unmanned system is transformed by Gamma curve nonlinear, and the contrast of the image is enhanced by the gray world method. Based on the processed image, the image moment is calculated, namely the space moment, the central moment and the normalized central moment, to describe the global and local characteristics of the image. According to the obtained visual perception information of the unmanned system, the intelligent visual control framework is built. Obtain the desired image feature matrix, extract the current moment image feature matrix, and nonlinear map the camera Angle through the extreme learning machine based on the improved firefly algorithm, so as to obtain the intelligent vision control law, so as to eliminate the visual perception image error and realize the effective control of intelligent vision. The experimental results show that under the background of different experimental groups, the minimum average time of visual control obtained by the proposed algorithm reaches 1s, and the minimum average error of visual control reaches 0.12%, which fully confirms the better application performance of the proposed algorithm.

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  • 收稿日期:2024-02-28
  • 最后修改日期:2024-06-02
  • 录用日期:2024-06-03
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