Landmarks are ideal wayfinding tools to guide a person from A to B as they allow fast reasoning and efficient communication. However, very few path-finding algorithms start from the availability of landmarks to generate a path. In this paper, which focuses on indoor wayfinding, a landmark-based path-finding algorithm is presented in which the endpoint partition is proposed as spatial model of the environment. In this model, the indoor environment is divided into convex sub-shapes, called e-spaces, that are stable with respect to the visual information provided by a person’s surroundings (e.g. walls, landmarks). The algorithm itself implements a breadth-first search on a graph in which mutually visible e-spaces suited for wayfinding are connected. The results of a case study, in which the calculated paths were compared with their corresponding shortest paths, show that the proposed algorithm is a valuable alternative for Dijkstra’s shortest path algorithm. It is able to calculate a path with a minimal amount of actions that are linked to landmarks, while the path length increase is comparable to the increase observed when applying other path algorithms that adhere to natural wayfinding behaviour. However, the practicability of the proposed algorithm is highly dependent on the availability of landmarks and on the spatial configuration of the building.
Optimized directed roadmap graph for multi-agent path finding using stochastic gradient descent
