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In the course of the task, the mobile robot should find the shortest and most smooth obstacle-free path to move from the current point to the target point efficiently, which is namely the path planning problem of the mobile robot. After analyzing a large number of planning algorithms, it is found that the combination of traditional planning algorithm and heuristic programming algorithm based on artificial intelligence have outstanding performance. Considering that the basic rapidly exploring random tree algorithm is widely used for some of its advantages, meanwhile there are still defects such as poor real-time performance and rough planned path. So, in order to overcome these shortcomings, this article proposes target bias search strategy and a new metric function taking both distance and angle into account to improve the basic rapidly exploring random tree algorithm, and the neural network is used for curve post-processing to obtain a smooth path. Through simulating in complex environment and comparison with the basic rapidly exploring random tree algorithm, it shows good real-time performance and relatively shorter and smoother planned path, proving that the improved algorithm has good performance in handling path planning problem.

Aircraft seat pitch is one of the most important factors affecting passengers ability to sit comfortably for a longer time on the plane. The purpose of this study was to illustrate the association between different seat pitches and overall comfort index, and seat‐interface pressure variables as well as try to predict aircraft seat comfort. Through an experimental study, 11 subjects (age, 26.3 ± 1.6; height, 169.8 ± 8.5; body mass, 65.1 ± 15.6) rated five different seat pitch settings (55 examples, 11 participants and five scenarios). Descriptive statistics, together with one‐way analysis of variance were used to determine which of the metrics could be used to distinguish the overall comfort index. The results show that overall comfort index was statistically significant (p < 0.05) between different seat pitches, but there was no statistically significant difference (p > 0.05) in interface pressure variables. In addition, a multilayer feed forward neural network with one hidden layer was proposed with the help of Matlab Neural Network Toolbox. The model explained 99% of the variance in overall comfort index with the root mean square error (RMSE) of 0.12551. The remainder of the total data was used for validation purposes; the correlation is r = 0.775, p < 0.01, and the RMSE is 1.21031. That suggests this model has a significant relationship between the actual and predicted overall comfort index.