Identifying a route that avoids obstacles in continuous space is important for infrastructure alignment, robotic travel, and virtual object path planning, among others, because movement through space is not restricted to a predefined road or other network. Vector and raster GIS (geographic information system) solution approaches have been developed to find good/efficient routes. On the vector side, recent solution approaches exploit spatial knowledge and utilize GIS functionality, offering significant computational advantages in finding an optimal solution to this path routing problem. Raster-based shortest path techniques are widely applied in route planning for wayfinding, corridor alignment, robotics and video gaming to derive an obstacle avoiding path, but represent an approximation approach for solving this problem. This research compares vector and raster approaches for identifying obstacle-avoiding shortest paths/routes. Empirical assessment is carried out for a number of planning applications, highlighting representational issues, computational requirements and resulting path efficiency.
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