Long-range route planning is an important component in the intelligent control system of an autonomous agent. Most attempts to solve it with map data rely on applying simple search strategies to high-resolution, node-and-link representations of the map. These techniques have several disadvantages including large time and space requirements. We present an alternative which utilizes a more intelligent representation of the problem environment. Topographical features are represented as homogeneous-cost regions, greatly reducing storage requirements. Then, the A* search strategy is applied to a dynamically created graph, constructed according to Snell's law. Testing has shown significant speed improvements over competing techniques.

Mobile autonomous vehicles require the capability of planning routes over ranges that are too great to be characterized by local sensor systems. Completion of this task requires some form of map data. Much work has been done concerning planning paths through local areas, those which can be scanned by on-board sensor systems. However, planning paths based on long range map data is a very different problem. Extant solution techniques require the search of discrete, node and link representations which characterize continuous, two dimensional problem environments. The authors assume the availability of topographic data organized into regions of homogenous traversal cost. Given this, they present a solution technique for the long range planning problem which relies on a Snell's Law heuristic to limit a graph search for the optimal solution