The following code fragment is taken from the example MRPT/samples/pathPlanning. The basic usage requires declaring the gridmap, the mrpt::nav::PlannerSimple2D object, setting the robot radius, and invoking mrpt::nav::PlannerSimple2D::computePath(). Occupancy grid mapping (OGM) is a popular mapping technique that discretizes the environment into cells (or voxels) and seeks to estimate the occupancy. For those cases, see the obstacle avoidance methods. Note that this is a very simple method, not suitable for robots with shapes very different from circular and/or moving in cluttered environmnets. The value iteration algorithm, starting at the source position, increase iteratively the area covered by shortest paths until the target cell is reached.This assure that just one single free cell is enough for the robot to move without collision. The aim of this work is to determine which approach to the robotic mapping problem imbues a mobile robot with the greatest ability to create an accurate representation of its operating environment. Growth of the obstacles by the robot radius. Abstract: In this paper a quantitative analysis of robotic mapping utilising the fields dominant paradigm, the occupancy grid, is presented.The basic value iteration algorithm for searching shortest paths is implemented in the MRPT for occupancy grids, and circular robots, in the class mrpt::nav::PlannerSimple2D.
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