Efficient C-Space and Cost Function Updates in 3D for Unmanned Aerial Vehicles

When operating in partially-known environments, autonomous vehicles must constantly update their maps and plans based on new sensor information. Much focus has been placed on developing efficient incremental planning algorithms that are able to efficiently replan when the map and associated cost function changes. However, much less attention has been placed on efficiently updating the cost function used by these planners, which can represent a significant portion of the time spent replanning. In this paper, we present the limited incremental distance transform algorithm, which can be used to efficiently update the cost function used for planning when changes in the environment are observed. Using this algorithm it is possible to plan paths in a completely incremental way starting from a list of changed obstacle classifications. We present results comparing the algorithm to the Euclidean distance transform and a mask-based incremental distance transform algorithm. Computation time is reduced by an order of magnitude for a UAV application. We also provide example results from an autonomous micro aerial vehicle with on-board sensing and computing.