The path planning is developed on the subject which aim to guide a walking robot from a starting point forwards a goal. The paper presents a now model merging the optimization of support vector machine (SVM) into the artificial potential field path planning. Using the path planning, robots can estimate a free smooth walking path of obstacles. Based on the statistical learning theory, the SVM can be used to optimize a zero-potential decision boundary in the 2-dimemsional map with a large margin. The idea of large margin implies that a wide path can be obtained with the employment of the SVM. With the RBF kernel, the presented method produces a 2-dimemsional potential-field map. In the map, obstacles are modeled as the sum of various parametric Gaussian distributions. As known, a map composed with the superposition of 2-dimemsional smooth Gaussian functions can also achieve the walking path smooth. Upon this, potential-field or road map based robot navigation can easily be applied to achieve the path smoother. The proposed model provides a way to search a wide smooth road for the robot. Detailed experiments and discussions are included in the paper.

Date Published: 31 December 2008
PDF: 6 pages
Proc. SPIE 7130, Fourth International Symposium on Precision Mechanical Measurements, 71304J (31 December 2008); doi: 10.1117/12.819723

Published in SPIE Proceedings Vol. 7130:
Fourth International Symposium on Precision Mechanical Measurements
Yetai Fei; Kuang-Chao Fan; Rongsheng Lu, Editor(s)