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Robot path planning model based on RNN and visual quality evaluation in thecontext of crowds is analyzed in this paper. Mobile robot path planning is thekey to robot navigation and an important field in robot research. Let themotion space of the robot be a two-dimensional plane, and the motion of therobot is regarded as a kind of motion under the virtual artificial potentialfield force when the artificial potential field method is used for the pathplanning. Compared to simple image acquisition, image acquisition in a complexcrowd environment requires image pre-processing first. We mainly use OpenCVcalibration tools to pre-process the acquired images. In themethodology design,the RNN-based visual quality evaluation to filter background noise isconducted. After calibration, Gaussian noise and some other redundantinformation affecting the subsequent operations still exist in the image. Basedon RNN, a new image quality evaluation algorithm is developed, and denoising isperformed on this basis. Furthermore, the novel path planning model is designedand simulated. The expeirment compared with the state-of-the-art models haveshown the robustness of the model.

Path planning model of mobile robots in the context of crowds