A wall surface inspection robot mainly relies on the inertial measurement unit and global positioning system (GPS) signal during intelligent wall surface inspection. The robot may encounter incorrect positioning under a GPS-denied environment, easily triggering safety accidents. In order to obtain a path suitable for the safe work of the robot wall surface inspection robot in a GPS-denied environment, a global path-planning method for wall surface inspection robots was proposed based on the improved generic algorithm (GA). The influencing factor for GPS signal strength was introduced into the heuristic function in path planning for GA to address the aforementioned problem. Using the PSO algorithm, GA was initialized and the influencing term of GPS signal was introduced into the fitness degree function so as to achieve point-to-point path planning of vertical wall surface inspection robot. Path angle and probability of intersection and variation was taken into account for better path planning capability. Finally, the simulation experiments were performed. The generated path using the improved GA was found to avoid the blind area of the GPS signal. The algorithm proposed has a good performance with average convergence times of 35.9 times and an angle of 55.88° in simple environment. Contrary to the traditional GA and PSO algorithm, the method showed advantages in terms of the convergence rate, path quality, path angle change, and algorithm stability. The research presented in this article is meaningful and relatively sufficient. The simulation test is also quite convincing. The proposed method was proved to be effective in global path planning for a wall surface inspection robot.

This work aimed to study the application of pelvic floor dynamic images of magnetic resonance imaging (MRI) based on the particle swarm optimization (PSO) algorithm in female stress urinary incontinence (SUI). 20 SUI female patients were selected as experimental group, and another 20 healthy females were taken as controls. PSO algorithm, K-nearest neighbor (KNN) algorithm, and back propagation neural network (BPNN) algorithm were adopted to construct the evaluation models for comparative analysis, which were then applied to 40 cases of female pelvic floor dynamic MRI images. It was found that the model proposed had relatively high prediction accuracy in both the training set (87.67%) and the test set (88.46%). In contrast to the control group, there were considerable differences in abnormal urethral displacement, urethral length changes, bladder prolapse, and uterine prolapse in experimental patients ( P < 0.05 ). After surgery, the change of urethral inclination angle was evidently reduced ( P < 0.05 ). To sum up, MRI images can be adopted to assess the occurrence of female SUI with abnormal urethral displacement, shortening of urethra length, bladder prolapse, and uterine prolapse. After surgery, the abnormal urethral movement was slightly improved, but there was no obvious impact on bladder prolapse and uterine prolapse.

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This paper aimed to explore pelvic lymphadenectomy for gynecological malignant tumors guided by computed tomography angiography (CTA) images under region-growing algorithm (RGA). 100 cases of malignant tumor patients who received pelvic lymphadenectomy in hospital from January 2018 to January 2020 were analyzed. Patients were classified into control group (CTA image) and experimental group (RGA-based CTA image), each with 50 cases. The overall accuracy (OA) of the pelvic CT image segmentation parameters under RGA, the watershed segmentation algorithm (WA), and the swarm intelligence optimization algorithm (SIOA) was compared. Comparisons of segmentation parameters, denoising performance, and CT imaging of patients as well as diagnosis rate and total efficiency rate were carried out. The results showed that overall accuracy (OA) of RGA was considerably higher versus watershed segmentation algorithm (WA) and swarm intelligence optimization algorithm (SIOA). However, false positive rate (FPR) and false negative rate (FNR) of RGA were greatly lower than those of other algorithms. RGA greatly improved the accuracy of pelvic tumor detection. The peak signal-to-noise ratio (PSNR) of RGA was superior to that of WA and SIOA, but differences in edge preservation index (EPI) value were not significant. The diagnosis rate of the experimental group was 48/50 (96%), while the diagnosis rate by manual means was 38/50 (76%). For the diagnosis rate and total efficiency, results of the experimental group were evidently higher in contrast to the control group ( P < 0.05 ). In conclusion, under RGA, CTA image-guided pelvic lymphadenectomy had good segmentation accuracy and denoising performance, and it was superior in total efficiency and diagnosis rate, which was worthy of clinical promotion.

Current research on autonomous mobile robots focuses primarily on perceptual accuracy and autonomous performance. In commercial and domestic constructions, concrete, wood, and glass are typically used. Laser and visual mapping or planning algorithms are highly accurate in mapping wood panels and concrete walls. However, indoor and outdoor glass curtain walls may fail to perceive these transparent materials. In this study, a novel indoor glass recognition and map optimization method based on boundary guidance is proposed. First, the status of glass recognition techniques is analyzed comprehensively. Next, a glass image segmentation network based on boundary data guidance and the optimization of a planning map based on depth repair are proposed. Finally, map optimization and path-planning tests are conducted and compared using different algorithms. The results confirm the favorable adaptability of the proposed method to indoor transparent plates and glass curtain walls. Using the proposed method, the recognition accuracy of a public test set increases to 94.1%. After adding the planning map, incorrect coverage redundancies for two test scenes reduce by 59.84% and 55.7%. Herein, a glass recognition and map optimization method is proposed that offers sufficient capacity in perceiving indoor glass materials and recognizing indoor no-entry regions.