Fast and optimal pathfinding




Harabor, Daniel Damir

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Pathfinding (or navigating) from A to B is a common problem in Computer Science with broad practical applications in areas as diverse as digital entertainment, logistics and robotics. Pathfinding is made difficult when many variations, or symmetries, of the same path exist. Symmetry slows down search by forcing otherwise performant algorithms to waste time considering many equivalent states. We speed things up by developing new online and offline symmetry-breaking techniques that eliminate a large number of symmetric states. Our contributions are optimality preserving, memory efficient and can have a dramatic positive effect on algorithmic performance They are especially well suited to speeding up pathfinding search on grid-maps of the type widely employed in computer games and robotics. Moreover, our work is largely orthogonal with a wide range of efficiency-improving techniques that have been previously described in the academic literature. We investigate a number of novel symmetry breaking approaches. Rectangular Symmetry Reduction identifies symmetric path segments during an offline pre-processing step. This approach is optimal, requires very little overhead (usually a few seconds of up-front time and a linear amount of memory) and it can improve the performance of classical pathfinding algorithms such as A{*} by several factors. Our second contribution, Jump Point Search (JPS), significantly improves on the performance of RSR and currently represents the state of the art for pathfinding on grid-map domains. In its online form JPS requires zero preprocessing, zero additional memory and always finds the shortest path. Our experiments show that JPS can consistently improve the performance of A{*} search by over one order of magnitude and more. In its offline form JPS reformulates the search space to achieve even better performance but requires an up-front investment of time. The algorithm has zero memory overheads when applied to graphs that are stored as an adjacency list. When applied to graphs stored as an adjacency matrix, the algorithm introduces a linear-sized memory overhead. In addition to RSR and JPS we study the related any-angle pathfinding problem. Recently formulated but nevertheless well studied, this problem involves finding a shortest path in a grid-map domain but asks that the path is not constrained to the points of the grid. Though a range of approaches have been suggested there are no effective techniques, to date, that are both optimal and online. As a final contribution we give a first algorithm that can provably compute solutions having both of these desirable characteristics.






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