High Level Path Planning with Uncertainty
This work addresses path planning under uncertainty, which is a common practical issue, but appears incremental as it builds on existing graph-based methods.
The paper tackles the problem of high-level path planning in uncertain environments by proposing U-graphs, an extension of distance graphs that handle uncertainty, and defines an optimality criterion and algorithm for computing optimal navigation plans.
For high level path planning, environments are usually modeled as distance graphs, and path planning problems are reduced to computing the shortest path in distance graphs. One major drawback of this modeling is the inability to model uncertainties, which are often encountered in practice. In this paper, a new tool, called U-yraph, is proposed for environment modeling. A U-graph is an extension of distance graphs with the ability to handle a kind of uncertainty. By modeling an uncertain environment as a U-graph, and a navigation problem as a Markovian decision process, we can precisely define a new optimality criterion for navigation plans, and more importantly, we can come up with a general algorithm for computing optimal plans for navigation tasks.