Path planning is an essential step for mobile robots. This paper presents a novel shape parameter search‐ (SP‐Search) based path planning algorithm for mobile robots using quintic trigonometric Bézier curve and its two shape parameters. The proposed search algorithm recursively divides the shape parameter space into a grid and uses a multi‐objective function to determine the shape parameter values. The multi‐objective function is expressed by the sum of four terms multiplied by coefficients: the integral of the squared jerk, the integral of the squared curvature, the mean of minimum distances between the predefined skeleton and quintic trigonometric Bézier path, and the arclength. Optimal shape parameters can be determined according to the required cases by the user using four coefficient values. Experimental results illustrate the feasibility of the proposed SP‐Search algorithm. Also, we have developed another intuitive algorithm, the Brute Force Grid Search algorithm, which is the brute force approach for shape parameter search to compare the performance of the proposed SP‐search algorithm for path planning problems as a baseline. The experimental results show that the SP‐Search algorithm finds better shape parameter values with a considerably small number of searched points.
The classifiers K-nearest neighbor (KNN), Multiclass support vector machine (MSVM), Decision Tree (DT), Discriminate Analysis (DA), Naive Bayes (NB), Random Forest (RF), and Ensemble Tree (ET) are the most well-known methods in machine learning. They are used in many fields like pattern recognition, medical disease analysis, user smartphone classification, text classification, etc. This paper presents a new framework for 3D surface point type classification using the most known classification methods in machine learning and the principal curvatures, the binormal vector, the cosine value of the angle between the normal vector and binormal vectors. The purpose of this study is to classify data points according to their developability. Also, the comparison between these methods is given to measure developability based on the accuracy and the processing time using several 3D surface examples.
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