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Better Group Behaviors in Complex Environments using Global
Roadmaps
O. Burchan Bayazit, Jyh-Ming Lien and Nancy M. Amato
Presented by Mohammad Irfan Rafiq 3/2/04 Using slides from Andreas Edlund(2003)
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Motivation
● In previous techniques behavior only depended on the local environment
● resulted in simplistic navigation and planning capabilities
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Goal and Approach
● More sophisticated flocking behaviors that supports global navigation and planning
● Integrated roadmap-based path planning with flocking techniques– provides global information in complex environments– adaptive roadmaps enable communication between
agents– Associating rules with roadmap nodes and edges
enables customization of behaviors
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Introduction
● Flocks and crowds.● Craig Raynolds' “boids”, SIGGRAPH'87
– Presented a distributed approach to simulate flocks of individuals.
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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.
– Stampede.mpeg
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This paper
● Behaviour:– Homing Behaviour.– Goal Searching Behaviour.– Narrow Passage Behaviour.– Shepherding Behaviour.
● Approaches:– Basic potential field.– Grid based A*.– Rule based roadmap.
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Boids
● Individuals use “boid”-behaviour.– Avoid collision with flockmates.– Match velocity with flockmates.– Stay close to flockmates.
Separation Alignment Cohesion
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Global behaviour
● Global behaviour is simulated using a potential field. Two force vectors used:– Towards the goal.– Away from obstacles.
Goal
Boid
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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.
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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.
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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.
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Goal Searching Behaviour
● Achieve this behavior using a roadmap graph with adaptive edge weights
● Each individual member behaves independently and uses the roadmap to wander around
● probabilistically choose a roadmap edge to follow based on the weight of the edge
● edge weights represent preferences for the current task
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Algorithm for Goal Searching
for (each flock member)
if (goal found)
increase edge weights on path to goal
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Algorithm for Goal Searching
for (each flock member)
if (goal found)
increase edge weights on path to goal
else if (dead end found)
pop stack until a new branch is found
decrease weight of edge corr. to popped node
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Algorithm for Goal Searchingfor (each flock member)
if (goal found)increase edge weights on path to goal
else if (dead end found)pop stack until a new branch is founddecrease weight of edge corr. to popped node
elseselect a neighboring node of the current nodepush this node onto the stack
endif
endfor
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Goal Searching Behaviour
Ants
Goal
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Goal Searching Behaviour
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Goal Searching Behaviour
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Narrow Passage Behaviour● A naive way is to simply use the homing behaviour.● 2 goal nodes – one at the entrance and the other at the
exit from the passage
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Narrow Passage Behaviour
● Will result in congestion● It would be better if the ants formed some kind of
queue.
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Narrow Passage Behaviour● The paper proposes a “follow-the-leader” strategy:
– Move to the passage using the homing behaviour.– The node at the entrance of the passage will have a rule
e.g :
WAIT FOR THE OTHERS, SELECT A LEADER FOLLOW THE LEADER
– 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.
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Narrow Passage Behaviour
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Narrow Passage Behaviour
● Select a leader.
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Narrow Passage Behaviour
● Select the first follower.
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Narrow Passage Behaviour
● Select the the next follower.
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Narrow Passage Behaviour
● And so on ...
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Shepherding Behaviour
● The sheep have boid behaviour.● The sheep dog repels the sheep by a certain
amount of force.
Goal
Sheep
Dog
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Shepherding Behaviour
● The herd is continuously grouped into subgroups based on the sheep's positions.
Subgroup
Another subgroup
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Shepherding Behaviour
● Dog always herds the subgroup that is the farthest away from the subgoal.
Subgoal
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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
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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
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Experimental Results
● Goal Searching behaviour:– 16 obstacles occupies 24 % of the environment.– 50 flock members.– Sensory radius: 5 m.– 80 x 100 m environment.
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Experimental Results
● Narrow passage behaviour:– Naive homing behaviour versus follow-the-leader.– 50 flock members.– One narrow passage between two mountains.
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Experimental Results
● Narrow passage behaviour:
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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
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Experimental Results
● Shepherd behaviour:– Comparison between different strength of the sheep
dog's repulsive force.
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Conclusions and Criticisms
● Roadmap is better than basic and A* – Faster and few local minima.– Can be used in more complex environments
● Criticisms:– Algorithms poorly described.
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Videos
● 2D – Homing● 3D – Homing● Goal Searching● Narrow Passage● Shepherding