Robot Motion Planning Project Matrix: Saving Neo From Mr. Smiths Joohwi Lee(joohwi@cs.unc.edu) Sang Woo Lee(leejswo@cs.unc.edu)

Scenario Crowd of pedestrian are heading to each end of road. Small numbers of Smith are trying to find Neo based on their vision and communicate each other. Neo is trying to escape from Smith and heading for specific target. Mr. Smiths may fire a gun, then crowds start to evacuate. 2018-12-25

Goals Simulate crowded environment Agents path planning with dynamic target Planning based on local information Different behavior of agents and crowd Cooperating agents Feedback simulation between agents and crowd 2018-12-25

Motivation Crowds have many interesting behavior of humans. Many crowd simulations have simple and similar goals such as exit finding. By assigning special missions to some agents, we hope to observe interaction among them and test their path plans. We choose ‘local approach’ for agents to distinguish agents from crowds and to create a realistic simulation. Cooperation and feedback can introduce a variety of interesting situations. Such simulation has many applications: finding lost child, chasing criminals, military mission, counter terrorism, … etc. 2018-12-25

Tasks Realistic crowd simulation (hopefully based on other works) Agents path planning Mr. Smith Cooperative roadmap building with their vision Dynamic path planning targeted Neo Neo Cognitive escaping pattern Crowds and agents interaction Evacuating behavior simulation Demonstration Crowd simulation Agents that chase and escape among crowds Evacuating or cooperating behavior simulation 2018-12-25

Prior Works Reciprocal Velocity Obstacles (1/4) Jur van den Berg’s work Path planning against moving obstacles based on their velocity Real-time and scalable performance Safe and oscillation free Assumption that other agents should make a similar collision-avoidance reasoning Local deadlocks 2018-12-25

Prior Works Real-time path planning with virtual agent (2/4) Avneesh Sud’s work Compute 2nd order voronoi diagram for path planning Simultaneous proximity calculation between agents Interactive global path planning and local collision avoidance with distinct goals 2018-12-25

Prior Works Continuum Crowd (3/4) Continuum Crowd by Treuille, A. Cooper Compute crowds using fluid dynamics Planning under continuum perspective rather than per-agent dynamics Smoother motion Able to capture an emergent phenomena Easy to simulate hybrid model 2018-12-25

Prior Works Cognitive Modeling (4/4) J. Funge introduce cognitive aspect to multi agent-based dynamics, to make more intelligent agent 2018-12-25

References J. V. D. Berg, M. C. Lin, D. Manocha. Reciprocal Velocity Obstacles for Real-Time Multi-Agent Navigation, To appear ICRA 2008 A. Sud, E. Andersen, S. Curtis, M. C. Lin, D. Manocha. Real-time Path Planning for Virtual Agents in Dynamic Environments, IEEE Virtual Reality, 2007 Treuille, A. Cooper, S. Popović, Z. Continuum Crowds. ACM Transactions on Graphics 25(3) (SIGGRAPH 2006) J. Funge, X. Yu, D. Terzopoulos, Cognitive Modeling J. Pettré, H. Grillon and D. Thalmann, Crowds of Moving Objects: Navigation Planning and Simulation. ICRA, Rome, 14-17 April 2007 2018-12-25