Dynamically Pruned A* for re-planning in navigation meshes

Modern simulations feature crowds of AI-controlled agents moving through dynamic environments, with obstacles appearing or disappearing at run-time. A dynamic navigation mesh can represent the traversable space of such environments. The A* algorithm computes optimal paths through the dual graph of this mesh. When an obstacle is inserted or deleted, the mesh changes and agents should re-plan their paths. Many existing re-planning algorithms are too memory-intensive for crowds, or they cannot easily be used on graphs where vertices and edges are added or removed. In this paper, we present Dynamically Pruned A* (DPA*), an extension of A* for re-planning optimal paths in dynamic navigation meshes. DPA* has similarities to adaptive algorithms that make the A* heuristic more informed based on previous queries. However, DPA* prunes the search using only the previous path and its relation to the dynamic event. We describe the four re-planning scenarios that can occur; DPA* uses different rules in each scenario. Our algorithm is memory-friendly and robust against structural changes in the graph, which makes it suitable for crowds in dynamic navigation meshes. Experiments show that DPA* performs particularly well in complex environments and when the dynamic event is visible to the agent. We integrate the algorithm into crowd simulation software to model large crowds in dynamic environments in real-time.

[1]  Subir Kumar Ghosh,et al.  Visibility Algorithms in the Plane , 2007 .

[2]  Nuria Pelechano,et al.  NEOGEN: Near optimal generator of navigation meshes for 3D multi-layered environments , 2013, Comput. Graph..

[3]  Marcelo Kallmann.  Navigation Queries from Triangular Meshes , 2010, MIG.

[4]  Jur P. van den Berg,et al.  Anytime path planning and replanning in dynamic environments , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[5]  Pedro Meseguer,et al.  Tree Adaptive A , 2011, AAMAS.

[6]  Franco P. Preparata,et al.  The Medial Axis of a Simple Polygon , 1977, MFCS.

[7]  Maxim Likhachev,et al.  D*lite , 2002, AAAI/IAAI.

[8]  Dirk Helbing,et al.  How simple rules determine pedestrian behavior and crowd disasters , 2011, Proceedings of the National Academy of Sciences.

[9]  Dinesh Manocha,et al.  Reciprocal n-Body Collision Avoidance , 2011, ISRR.

[10]  Mark H. Overmars,et al.  Indicative routes for path planning and crowd simulation , 2009, FDG.

[11]  Roland Geraerts,et al.  Real‐time path planning in heterogeneous environments , 2013, Comput. Animat. Virtual Worlds.

[12]  Craig W. Reynolds.  Steering Behaviors For Autonomous Characters , 1999 .

[13]  Helbing,et al.  Social force model for pedestrian dynamics. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[14]  Jur P. van den Berg,et al.  The visibility--voronoi complex and its applications , 2005, EuroCG.

[15]  David Furcy,et al.  Lifelong Planning A , 2004, Artif. Intell..

[16]  Sven Koenig,et al.  Generalized Adaptive A* , 2008, AAMAS.

[17]  Dinesh Manocha,et al.  Real-time navigation of independent agents using adaptive roadmaps , 2007, VRST '07.

[18]  D. Hunter Hale,et al.  Dynamic Updating of Navigation Meshes in Response to Changes in a Game World , 2009, FLAIRS Conference.

[19]  Roland Geraerts,et al.  Towards Believable Crowds : A Generic Multi-Level Framework for Agent Navigation , 2015 .

[20]  Sebastian Thrun,et al.  Anytime Dynamic A*: An Anytime, Replanning Algorithm , 2005, ICAPS.

[21]  Maxim Likhachev,et al.  Anytime Truncated D* : Anytime Replanning with Truncation , 2013, SOCS.

[22]  B. Faverjon,et al.  Probabilistic Roadmaps for Path Planning in High-Dimensional Con(cid:12)guration Spaces , 1996 .

[23]  Jorge A. Baier,et al.  Reusing cost-minimal paths for goal-directed navigation in partially known terrains , 2014, Autonomous Agents and Multi-Agent Systems.

[24]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[25]  Steven M. LaValle,et al.  Planning algorithms , 2006 .

[26]  Ramon Lawrence,et al.  Fast Grid-Based Path Finding for Video Games , 2013, Canadian Conference on AI.

[27]  Norman I. Badler,et al.  Multi-domain real-time planning in dynamic environments , 2013, SCA '13.

[28]  Roland Geraerts,et al.  Navigation meshes for realistic multi-layered environments , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[29]  Jorge A. Baier,et al.  Making A* Run Faster than D*-Lite for Path-Planning in Partially Known Terrain , 2014, ICAPS.

[30]  Maxim Likhachev,et al.  Anytime incremental planning with E-Graphs , 2013, 2013 IEEE International Conference on Robotics and Automation.

[31]  Mark H. Overmars,et al.  A Velocity-Based Approach for Simulating Human Collision Avoidance , 2010, IVA.

[32]  Roland Geraerts,et al.  Planning short paths with clearance using explicit corridors , 2010, 2010 IEEE International Conference on Robotics and Automation.

[33]  Roland Geraerts,et al.  A navigation mesh for dynamic environments , 2012, Comput. Animat. Virtual Worlds.

[34]  D. Thalmann,et al.  A navigation graph for real-time crowd animation on multilayered and uneven terrain , 2005 .

[35]  Sven Koenig,et al.  The Fringe-Saving A* Search Algorithm - A Feasibility Study , 2007, IJCAI.

[36]  Maja J. Mataric,et al.  Motion planning using dynamic roadmaps , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.