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Real-time Navigation of Independent Agents Using Adaptive Roadmaps

Real-time Navigation of Independent Agents Using Adaptive Roadmaps. Avneesh Sud 1 , Russell Gayle 2 , Erik Andersen 2 , Stephen Guy 2 , Ming Lin 2 , Dinesh Manocha 2 1: Microsoft Corp 2: UNC Chapel Hill http://gamma.cs.unc.edu/crowd/aero. Motivation.

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Real-time Navigation of Independent Agents Using Adaptive Roadmaps

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  1. Real-time Navigation of Independent Agents Using Adaptive Roadmaps Avneesh Sud1, Russell Gayle2, Erik Andersen2, Stephen Guy2, Ming Lin2, Dinesh Manocha2 1: Microsoft Corp 2: UNC Chapel Hill http://gamma.cs.unc.edu/crowd/aero

  2. Motivation • Navigating to goal - important behavior in virtual agent simulation • Navigation requires path planning • Compute collision-free paths • Satisfy constraints on the path • Exhibit crowd dynamics

  3. ViCrowd [Musse & Thalmann01; EPFL] ABS [Tecchia et al.01; UCL] Virtual Iraq [ICT/USC 06] Motivation Simulation of Virtual Humans

  4. Second Life Assassin’s Creed Spore Motivation Interactive simulation of crowds/virtual agents in games

  5. Challenges • Path planning for multiple (thousands of) independent agents simultaneously • Each agent is a dynamic obstacle • Exact path planning for each agent in dynamic environments is P-space complete

  6. Goal • Real-time navigationfor multiple virtual agents • Independent behavior • Global path planning • Dynamic environments • Thousands of agents

  7. Applications • Crowd simulation • Multi-robot planning • Social engineering • Training and simulation • Exploration • Entertainment

  8. Main Results • Adaptive-Elastic ROadmaps(AERO): Graph structure for global navigation that adpats to dynamic environments • Augment global path planning with local dynamics model

  9. Results: Tradeshow Demo Simulation of 100 agents in an urban environment, 10fps

  10. Outline • Related Work • Our Approach • Results • Discussion and Conclusion

  11. Outline • Related Work • Our Approach • Results • Discussion and Conclusion

  12. Related Work • Multiple agent planning • Crowd dynamics

  13. Related Work • Multiple agent planning • Global path planning [Bayazit et al.02, Li & Chou03, Pettreet al.05] • Local methods [Khatib86] • Hybrid [Lamarche & Donikian04] • Dynamic environments [Quinlan & Kthaib93, Yang & Brock06, Gayle et al. 07, Li & Gupta07, Sud et al. 2007] • Crowd Simulation

  14. Related Work • Multiple agent planning • Crowd Simulation • Agent-based methods [Reynolds87, Musse & Thalmann97, Sung et al.04, Pelechano et al.07] • Cellular Automata [Hoogendoorn et al00, Loscos et al.03, Tu & Terzopoulos 93] • Particle Dynamics [Helbing03, Sugiyama et al. 01] • Continuous Methods [Helbing05, Treuille et al.06]

  15. Outline • Related Work • Our Approach • Overview • Adaptive Elastic Roadmaps (AERO) • Navigation using AERO • Results • Discussion and Conclusion

  16. Overview At each time step Adaptive Elastic Roadmap Environment (Static Obstacles, Dynamic Obstacles, and Agents) Scripted Behaviors Local Dynamics Collision Detection

  17. Overview At each time step Adaptive Elastic Roadmap Environment (Static Obstacles, Dynamic Obstacles, and Agents) Scripted Behaviors Local Dynamics Collision Detection

  18. Outline • Related Work • Our Approach • Overview • Adaptive Elastic Roadmaps (AERO) • Navigation using AERO • Results • Discussion and Conclusion

  19. Adaptive Elastic Roadmaps (AERO) • Global connectivity graph • Continuously adapts to dynamic obstacles • Physically-based updates • Localized roadmap deformations and maintenance • Advantage: Efficient to deform roadmap than recompute & replan

  20. AERO: Representation • Representation • Graph G = { M, L } • M = set of dynamic milestones • L = set of reactive links • lj(t) = [ p0(t) p1(t) p2(t) … pn(t) ] Where pk(t) is a dynamic particle

  21. AERO: Representation • Representation • Graph G = { M, L } • M = set of dynamic milestones • L = set of reactive links • lj(t) = [ p0(t) p1(t) p2(t) … pn(t) ] Where pk(t) is a dynamic particle

  22. AERO: Force Model • Applied forces influence roadmap behavior • Force on particle/milestone i: • Internal Forces • Prevent unnecessary link expansion • Prevent roadmap drift • External Forces • Respond to obstacle motion

  23. AERO: Force Model • Quasi-Static simulation • Considers particles at rest • Prevents undesirable link oscillations • Verlet integrator

  24. AERO: Maintenance • Roadmap maintenance • Link removal • Deformation energy • Prevent overly stretched links • Proximity to obstacles • Link insertion • Repair removed links • Explore for new path options

  25. AERO: Maintenance • Link insertion • Check removed links • Check disconnected components • Biased exploration toward the “wake” of moving obstacles

  26. AERO: Demo

  27. AERO: Link Bands • Region of free space closer to a link • Collision free zone in neighborhood of a link • Identify nearest link for each agent for path search

  28. AERO: Link Bands Link 2 Band 1 Link 1

  29. AERO: Link Bands Link 2

  30. AERO: Link Bands Band 1 Link 1

  31. Outline • Related Work • Our Approach • Overview • Adaptive Elastic Roadmaps (AERO) • Navigation using AERO • Results • Discussion and Conclusion

  32. Navigation: Path Planning • Source link  link band containing agent • Goal link  link band containing goal • Link weights • Path length • Link band width • Agent density

  33. Navigation: Local Dynamics Generalized force model of pedestrian dynamics [Helbing 2003] Emergent crowd behavior at varying densities

  34. Navigation: Local Dynamics Fsoc : Social repulsive force among agents Fatt : Attractive force among agents Fobs : Repulsive force from obstacles Fr : Roadmap force

  35. Navigation: Local Dynamics Fsoc : Social repulsive force among agents Fatt : Attractive force among agents Fobs : Repulsive force from obstacles Fr : Roadmap force

  36. Overview At each time step Adaptive Elastic Roadmap Environment (Static Obstacles, Dynamic Obstacles, and Agents) Scripted Behaviors Local Dynamics Collision Detection

  37. Outline • Related Work • Our Approach • Results • Discussion and Conclusion

  38. Demos Maze Tradeshow City

  39. Demos: Maze

  40. Demos: City

  41. Demos: Tradeshow

  42. Timings

  43. Outline • Related Work • Our Approach • Results • Discussion and Conclusion

  44. Conclusions • Physically-based, adapting roadmap AERO • Adapts to motion of obstacles • Handle changes in free space connectivity • Combine with a local dynamics model using link bands • Efficient localized updates • No assumptions on motion

  45. Limitations Unrealistic high-DoF human motion Computed paths may not be optimal Lacks convergence guarantees

  46. Future Work • Develop multi-resolution techniques • Exploit natural grouping behavior • Higher DoF articulated models for more realistic motions • Example / Learning based methods to guide simulation [Lerner2007]

  47. Acknowledgements • UNC GAMMA Group • Anonymous reviewers • Funding organizations • ARO • ONR • NSF • DARPA / RDECOM • Intel Corp • Microsoft Corp

  48. Questions? http://gamma.cs.unc.edu/crowd/aero avneesh.sud@microsoft.com

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