Shortest Gently Descending Paths
WALCOM '09 Proceedings of the 3rd International Workshop on Algorithms and Computation
On finding approximate optimal paths in weighted regions
Journal of Algorithms
Approximation algorithms for shortest descending paths in terrains
Journal of Discrete Algorithms
Efficient computation of minimum exposure paths in a sensor network field
DCOSS'07 Proceedings of the 3rd IEEE international conference on Distributed computing in sensor systems
Approximation algorithms for computing minimum exposure paths in a sensor field
ACM Transactions on Sensor Networks (TOSN)
Field D* path-finding on weighted triangulated and tetrahedral meshes
Autonomous Agents and Multi-Agent Systems
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In this paper we define piecewise pseudo-Euclidean optimal path problems, where each region has a distinct cost metric of a class we call pseudo-Euclidean, that allows the path cost to possibly vary within the region in a predictable and efficiently computable way. This pseudo-Euclidean class of costs allows us to model a wide variety of various geographical features. We provide an approximation algorithm named BUSHWHACK that efficiently solves these piecewise pseudo-Euclidean optimal path problems. BUSHWHACK uses a previously known technique of dynamically generating a discretization in progress. However, it combines with this technique a "lazy" and best-first path propagation scheme so that fewer edges need to be added into the discretization. We show both analytically and experimentally that BUSHWHACK is more efficient than approximation algorithms based on Dijkstra's algorithm.