Path planning on a compressed terrain

We present a better algorithm for path planning on complex terrain in the presence of observers and define several metrics related to path planning to evaluate the quality of various terrain compression strategies. The path-planning algorithm simulates a smugglers and border guards scenario. First, we place observers on a terrain so as to optimize their visible coverage area. Next, we compute a path that a smuggler would take to minimize detection by an observer, path length, and uphill movement. The smuggler is allowed the full range of Euclidean motion on the 2-dimensional plane, unlike alternate path planning schemes that strictly avoid obstacles. We use two runs of the A* algorithm to efficiently compute this path. We introduce new application-specific error metrics for evaluating lossy terrain compression. The target terrain applications are the optimal placement of observers on a landscape and the navigation through the terrain by smugglers. The error metrics compare the observer visibility and the cost of the optimal smuggler's route on the reconstructed terrain to the original terrain.