1. Robot Motion Planning Project
Matrix: Saving Neo From Mr. Smiths
Joohwi
Sang Woo

2. 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.

3. 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

4. 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.

5. 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

6. 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

7. 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

8. 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

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

10. 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, April 2007