Hierarchical path planning for situated agents in informed virtual geographic environments

Multi-Agent Geo-Simulation (MAGS) is a modelling and simulation paradigm which involves a large number of autonomous situated agents of various extents evolving in, and interacting with, an explicit description of a geographic environment called a Virtual Geographic Environment (VGE). One of the most important skills of autonomous situated agents is their ability to navigate and plan a path inside a VGE. Path planning in MAGS has to be solved in real time, often under constraints of limited memory and CPU resources. Moreover, the computational cost of path planing increases in complex and large-scale VGEs. In addition, most current planners only provide agents with obstaclefree paths and do not take into account the environments' topologic and semantic characteristics nor the agents' capabilities. In this paper, we extend the automated approach to build a semantically-enhanced and geometrically-accurate VGE called an Informed VGE (IVGE) that we proposed in [21]. Then, we propose our Hierarchical Path Planning (HPP) algorithm which relies on the topologic graph of the IVGE, and takes advantage of this IVGE's semantically-enriched description in order to provide autonomous situated agents with optimised paths with respect to both the environment's and the agents' characteristics.