  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.
