Thorough robot navigation based on SVM local planning

A prerequisite for autonomous robot navigation is the extraction of a path that is both efficient and safe in terms of collision. Towards this end, the paper in hand presents a novel local path planning method, incorporating the support vector machines (SVM) theory. The original SVM based module exploits a 2D map of points which are considered to be obstacles, so as to culminate in a collision free path. A unique attribute of the proposed SVM based local path planning algorithm is that it considers the consecutive positions of the global path trajectory, the embodiment of the robot and clusters the obstacles accordingly. Thus, the derived trajectory is a physically constrained path inasmuch as it considers the maximum margin notion of the SVM theory. Instead of providing a purely theoretical approach for local planning assessed using only artificial data, we integrate our local planner into an autonomous navigation system which is evaluated in real-world scenarios in order to show its efficacy. The latter framework firstly constructs a global 3D metric map of the perceived environment and then it converts it into a 2D map upon which a global path planner unrolls. The global map grows incrementally, by registering the collected point clouds over the robot's route towards a goal position. Moreover, the navigation is supported by an obstacle detection strategy based on v-disparity images. The system-and, consequently, the presented local path planner-was evaluated in long range outdoors scenarios, navigating successfully within congestive environments.