Local Path Planning for an Unmanned Ground Vehicle Based on SVM

Abstract

To resolve the local path generating problem for unmanned ground vehicles (UGV) in unstructured environments, a method combining a basic path subdivision method for topological maps of local environments and a Support Vector Machine (SVM) is proposed in this paper. Based on the basic path subdivision method, topological maps of local environments can be extracted with little expanded nodes, without the constraints of obstacle representation, so meeting the need for autonomous navigation in unstructured environments. Next, to optimize the candidate routes in topological maps and generate a smoother path, an SVM is introduced. The candidate routes boundary points are defined as positive and negative samples, and SVMs are employed to train the separating surface. An original SVM is extended to satisfy extra constraints such as vehicle position and heading constraints. Experimental results show the effectiveness and advantages of the proposed method.