Robot path planning model based on RNN and visual quality evaluation in the context of crowds is analyzed in this paper. Mobile robot path planning is the key to robot navigation and an important field in robot research. Let the motion space of the robot be a two-dimensional plane, and the motion of the robot is regarded as a kind of motion under the virtual artificial potential field force when the artificial potential field method is used for the path planning. Compared to simple image acquisition, image acquisition in a complex crowd environment requires image pre-processing first. We mainly use OpenCV calibration tools to pre-process the acquired images. In themethodology design, the RNN-based visual quality evaluation to filter background noise is conducted. After calibration, Gaussian noise and some other redundant information affecting the subsequent operations still exist in the image. Based on RNN, a new image quality evaluation algorithm is developed, and denoising is performed on this basis. Furthermore, the novel path planning model is designed and simulated. The expeirment compared with the state-of-the-art models have shown the robustness of the model.

中文翻译：

---

人群背景下移动机器人路径规划模型

本文分析了基于RNN和视觉质量评估的人群背景下机器人路径规划模型。移动机器人路径规划是机器人导航的关键，也是机器人研究的重要领域。设机器人的运动空间为二维平面，当采用人工势场法进行路径规划时，将机器人的运动看作是在虚拟人工势场力作用下的一种运动。与简单的图像采集相比，复杂人群环境中的图像采集需要先进行图像预处理。我们主要使用 OpenCV 校准工具对获取的图像进行预处理。在方法设计上，进行了基于RNN的视觉质量评价过滤背景噪声。校准后，高斯噪声和其他一些影响后续操作的冗余信息仍然存在于图像中。基于RNN，开发了一种新的图像质量评价算法，并在此基础上进行去噪。此外，设计并仿真了新颖的路径规划模型。与最先进模型相比的实验表明了模型的鲁棒性。