Better Group Behaviors in Complex Environments using Global Roadmaps O. Burchan Bayazit, Jyh-Ming Lien and Nancy M. Amato Andreas Edlund

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

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

● 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.

Experimental Results ● Homing behaviour:

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.

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