Support Vector Path Planning

This paper describes a unique approach of applying a pattern classification technique to robot path planning. A collision-free path connecting a start and a goal point provides information on the division of the space. In the case of 2D path planning, for example, the path divides the space into two regions. This suggests a dual problem of first dividing the whole space into such two regions and then picking up the boundary as a path. We develop a method of solving this dual problem using support vector machine (SVM). SVM generates a nonlinear separating surface based on the margin maximization principle. This property is suitable for the purpose of usual path planning problems, that is, generating a safe and smooth path. The details of the path planning methods in 2D and 3D spaces are described with several planning results. Future possibilities of combining the proposed concept with other path planning methodologies are also discussed