Euclidean Distance-Optimal Post-Processing of Grid-Based Paths
This addresses path optimization in robotics or navigation by providing a guaranteed improvement method, though it is incremental as it builds on existing post-processing techniques.
The paper tackles the problem of suboptimal grid-based paths with unnecessary turns by proposing a post-processing technique called Homotopic Visibility Graph Planning (HVG), which guarantees to shorten the path to be at least as short as the provably shortest path within the same topological class, as demonstrated through experimental comparisons.
Paths planned over grids can often be suboptimal in an Euclidean space and contain a large number of unnecessary turns. Consequently, researchers have looked into post-processing techniques to improve the paths after they are planned. In this paper, we propose a novel post-processing technique, called Homotopic Visibility Graph Planning (HVG) which differentiates itself from existing post-processing methods in that it is guaranteed to shorten the path such that it is at least as short as the provably shortest path that lies within the same topological class as the initially computed path. We propose the algorithm, provide proofs and compare it experimentally against other post-processing methods and any-angle planning algorithms.