Local multiresolution path planning

Abstract

Grid-based methods for finding cost optimal robot paths around obstacles are popular because of their flexibility and simple implementation. However, their computational complexity becomes unfeasible for real-time path planning if the resolution of the grid is high. These methods assume complete knowledge about the world, but in dynamic environments where sensing is done on board the robot, less is known about far-away obstacles than about the ones in close proximity. The paper proposes to utilize this observation by employing a grid of variable resolution. The resolution is high next to the robot and becomes lower with increasing distance. This results in huge savings in computational costs while the initial parts of the paths are still planned with high accuracy. The same principle is applied to the time-axis, allowing for planning paths around moving obstacles with only a moderate increase in computational costs.