Search and Escape in “The Snorks World” Guillaume Poncin Gregory Kron Monday, June 9 th 2003

Overview Storyline Implementation The World The Snorks The Sglurlbsch Demonstration Testing

Once upon a time… The Snorks moved out of the sea, into a rectangular world with polygonal obstacles (it seemed more sunny) They do not know the place very well so they really want to stay with one another However they are faced with a new kind of predator, the Sglurlbsch, which they badly fear. Sglurlbsch’s life is much more interesting since these Snorks came: he wants to track them down and bring all of them at once in his cave to store food for the winter. But Sglurlbsch is really dumb. We have to help him out or it will have to eat vegetables all winter long…

Implementation – The World 3D world where agents move in 2D Rectangle with polygonal obstacles Loaded from text file with coordinates of vertexes Using OpenGL for display

Implementation – The Snorks Their move is determined by 5 forces: Attraction: they want to be together (1/r 2 ) Repulsion: they do not want to be too close (1/r 7 ) Fear: they are afraid of the Sglurlbsch (1/r 2 ) Obstacle: they do not go towards obstacles (1/r 7 ) Randomness: part of their move is random Optimization: use a precalculated grid of the closest edges for each cell in the world to speed up the computation of obstacle distances.

Implementation – The Snorks Parameters of forces Weight factor Characteristic radius Maximum radius of influence Randomness angle Other parameters Number of Snorks Starting position Speed

Implementation – The Sglurlbsch The Path-Planner: PRM Randomly places milestones in free space Search for the best path using A* with the Euclidean distance to the goal as heuristic Smoothing of the path

Implementation – The Sglurlbsch We decided to decouple the search Group the Snorks together Pick the closest Snork (or group) and go towards it Then make it join the second closest Snork (or group) Make the flock reach the cave A time is allocated with each mission before a new plan is issued (~0.5 sec)

Implementation – The Sglurlbsch Pay attention to: Go around the Snorks when needed Not go too fast on the PRM to keep the Snorks in a group Sglurlbsch snorks Goal

Implementation – The Sglurlbsch Parameters for the search: Is the closest snork far ? Has the group joined the other one ? Is the group flocking ? Time before a new mission is issued Distance of a milestone for bypassing

Visibility-limited Sglurlbsch The Sglurlbsch only knows milestones that it has already seen When it does not know how to reach its goal, it explores by going to a random distant milestone. It knows where the Snorks are thanks to their smell.

Multiple Sglurlbsch Each of them has an autonomous plan They cooperate during the flocking phase, without explicitly coded behavior

Results & Experiments Real time simulation with high resolution display Good time performance on relatively complex worlds Manages to move a large number of actors to the goal even if slightly inhomogeneous in speed The speed of the Sglurlbsch can be smaller or greater No collision with obstacles even though no explicit test for the Sglurlbsch

Limitations No local planning to anticipate the movements of the Snorks No explicit cooperation when more than one follower Precompute the PRM, may result in slow initialization for complex worlds Only one color of Snorks…

Snapshot…