Search and Escape in “The Snorks World”

Guillaume PoncinGregory Kron

Monday, June 9th 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/r2) Repulsion: they do not want to be too close (1/r7) Fear: they are afraid of the Sglurlbsch (1/r2) Obstacle: they do not go towards obstacles (1/r7) 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…

Snapshot…