Unmanned Aircraft Vehicle Path Planning Based on SVM Algorithm

摘要

This paper describes an approach of using image processing and patters classification techniques for navigating the unmanned aircraft vehicle in known irregular environment. In the case of 2D path planning, a feasible flight path connecting the start and goal point can be regarded as a separating surface that 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 use support vector machine to solve this dual problem. SVM can generate a nonlinear separating surface based on the margin maximization principle. First, we generate a novel search space which contains flyable and no-fly regions from 3D surface of minimum risk and pick up key obstacle points as samples. Second, a safe and smooth path is generated through SVM. Results from simulations show that the path planner is able to plan an optimal path efficiently due to the simplicity of the search space.