Principles of artificial intelligence
Principles of artificial intelligence
Real-time robot motion planning using rasterizing computer graphics hardware
SIGGRAPH '90 Proceedings of the 17th annual conference on Computer graphics and interactive techniques
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
VUEMS: A Virtual Urban Environment Modeling System
CGI '97 Proceedings of the 1997 Conference on Computer Graphics International
Remote Interactive Walkthrough of City Models
PG '03 Proceedings of the 11th Pacific Conference on Computer Graphics and Applications
A Geometric Interpretation of Weighted Normal Vectors and Its Improvements
CGIV '05 Proceedings of the International Conference on Computer Graphics, Imaging and Visualization
Planning Algorithms
Proceedings of the 2008 Spring simulation multiconference
Informed Virtual Geographic Environments: An Accurate Topological Approach
GEOWS '09 Proceedings of the 2009 International Conference on Advanced Geographic Information Systems & Web Services
Efficient triangulation-based pathfinding
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Heuristic techniques for accelerating hierarchical routing on road networks
IEEE Transactions on Intelligent Transportation Systems
Navigation queries from triangular meshes
MIG'10 Proceedings of the Third international conference on Motion in games
Automatic generation of suboptimal navmeshes
MIG'11 Proceedings of the 4th international conference on Motion in Games
From geometry to spatial reasoning: automatic structuring of 3d virtual environments
MIG'11 Proceedings of the 4th international conference on Motion in Games
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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.