Better Group Behaviors in Complex Environments using Global

Roadmaps

O. Burchan Bayazit, Jyh-Ming Lien and Nancy M. Amato

Andreas Edlund <andreas.edlund@stanford.edu>

Introduction

● Flocks and crowds.● Craig Raynolds' “boids”, SIGGRAPH'87

– Presented a distributed approach to simulate flocks of individuals.

So what's it used for?

● Artificial life.– Explores how various lifeforms behave in larger

groups.● Animation.

– Used in movies and computer games.– Tim Burton's film “Batman Returns” used a modified

version of Raynolds' boids to simulate a swarm of bats and a flock of penguins.

This paper

● Behaviour:– Homing Behaviour.– Goal Searching Behaviour.– Narrow Passage Behaviour.– Shepherding Behaviour.

● Approaches:– Basic potential field.– Grid based A*.– Rule based roadmap.

Boids

● Individuals use “boid”-behaviour.– Avoid collision with flockmates.– Match velocity with flockmates.– Stay close to flockmates.

Separation Alignment Cohesion

Global behaviour

● Global behaviour is simulated using a potential field. Two force vectors used:– Towards the goal.– Away from obstacles.

Goal

Boid

Various approaches

● Problem with local minima.● Two methods to solve this problem:

– Grid based A* search.● Finds shortest paths and is relatively fast.● However, we need to recompute a new path every time we

have a new goal.

– Roadmap.● Precompute a roadmap for the environment and use it for

all the queries.

Homing Behaviour

● Search the roadmap to find a path to the goal.● Each node on this path is considered a subgoal.● The flock is attracted to the next subgoal instead

of the final goal.

Goal Searching Behaviour

● Environment is known, the goal is not.● Objective is to find the goal and get everyone to

it.● Tries to duplicate ant behaviour.

– Ants drop pheromone on paths to indicate the importance of that particular path.

– More ants will walk down paths that are considered more important.

Goal Searching Behaviour

Ants

Goal

Goal Searching Behaviour

Goal Searching Behaviour

Narrow Passage Behaviour

● A naive way is to simply use the homing behaviour.

Narrow Passage Behaviour

● We'll get problems with congestion though.● It would be better if the ants formed some kind of

queue.

Narrow Passage Behaviour

● The paper proposes a “follow-the-leader” strategy:– Move to the passage using the homing behaviour.– At the entrance node select the ant closest to the

entrance and designate that ant the “leader”. The other ants are “followers”.

– The leader's subgoal is the next node in the narrow path.

– The other ants line up behind each other and uses the ant in front of him as his subgoal.

Narrow Passage Behaviour

Narrow Passage Behaviour

● Select a leader.

Narrow Passage Behaviour

● Select the first follower.

Narrow Passage Behaviour

● Select the the next follower.

Narrow Passage Behaviour

● And so on ...

Shepherding Behaviour

● The sheep have boid behaviour.● The sheep dog repels the sheep by a certain

amount of force.

Goal

Sheep

Dog

Shepherding Behaviour

● The herd is continuously grouped into subgroups based on the sheep's positions.

Subgroup

Another subgroup

Shepherding Behaviour

● Dog always herds the subgroup that is the farthest away from the subgoal.

Subgoal

Shepherding Behaviour

● Algorithm based on an experiment with actual geese.

● From Richard Vaughan, 2000.

Experimental Results

● Homing behaviour:– Basic versus grid based A* versus MAPRM.– 301 random obstacles.– 30 s runtime.

Method #flockmates reaching goalBasic 10Roadmap 40A* search 40

Experimental Results

● Homing behaviour:

Local minimaMethod Init time (s) Find path time (s) # Escape (s)

Roadmap 0.88 0.65 255 22.99A* search 6.02 5.76 2005 1035.43

Experimental Results

● Goal Searching behaviour:– 16 obstacles occupies 24 % of the environment.– 50 flock members.– Sensory radius: 5 m.– 80 x 100 m environment.

Experimental Results

● Narrow passage behaviour:– Naive homing behaviour versus follow-the-leader.– 50 flock members.– One narrow passage between two mountains.

Experimental Results

● Narrow passage behaviour:

Experimental Results

● Shepherd behaviour:– Grid based A* versus roadmap.– 30 sheep.

Method Init time (s) #steps #local min.Roadmap 0.88 2348.17 7.8A* search 6.02 10612.08 32.2

Experimental Results

● Shepherd behaviour:– Comparison between different strength of the sheep

dog's repulsive force.

Conclusions and rants

● Roadmap is better than basic and A* (what a surprise).– Faster and few local mimima.

● Rants:– Algorithms poorly described.– What's up with the narrow passage experiment?– Escape from local minima?

Further reading

● Boids– Craig Raynolds, “Flocks, Herds, and Schools: A

Distributed Behavioral Model”, SIGGRAPH'87● Shepherding

– Richard Vaughan, Neil Sumpter, Jane Henderson, Andy Frost and Stephen Cameron, “Experiments in automatic flock control”, 2000