A Fast Randomized Method to Find Homotopy Classes for Socially-Aware Navigation
This work addresses path planning for socially-aware navigation, likely for robotics or autonomous systems, but appears incremental as it builds on existing methods like Yen's algorithm and the social force model.
The paper tackles the problem of finding multiple distinct homotopy classes for socially-aware navigation by introducing a fast randomized method based on a Voronoi diagram graph and a social force model cost function. The result shows that this approach is faster than Yen's algorithm for finding a subset of homotopy classes, computes more diverse paths with negligible loss in quality, as evidenced by empirical comparisons.
We introduce and show preliminary results of a fast randomized method that finds a set of K paths lying in distinct homotopy classes. We frame the path planning task as a graph search problem, where the navigation graph is based on a Voronoi diagram. The search is biased by a cost function derived from the social force model that is used to generate and select the paths. We compare our method to Yen's algorithm, and empirically show that our approach is faster to find a subset of homotopy classes. Furthermore our approach computes a set of more diverse paths with respect to the baseline while obtaining a negligible loss in path quality.