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SCRAM: Scalable Collision-avoiding Role Assignment with Minimal-makespan for Formational Positioning.
Patrick
MacAlpine, Eric Price, and Peter
Stone.
In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI), January 2015.
Accompanying videos at http://www.cs.utexas.edu/~AustinVilla/sim/3dsimulation/AustinVilla3DSimulationFiles/2013/html/scram.html
[PDF]260.3kB [postscript]676.5kB [slides.pdf]40.3MB
Teams of mobile robots often need to divide up subtasks efficiently. In spatial domains, a key criterion for doing so may depend on distances between robots and the subtasks' locations. This paper considers a specific such criterion, namely how to assign interchangeable robots, represented as point masses, to a set of target goal locations within an open two dimensional space such that the makespan (time for all robots to reach their target locations) isminimized while also preventing collisions among robots. We present scaleable (computable in polynomial time) role assignment algorithms that we classify as being SCRAM (Scalable Collision-avoiding Role Assignment with Minimal-makespan).SCRAM role assignment algorithms use a graph theoretic approach to map agents to target goal locations such that our objectives for both minimizing the makespan and avoiding agent collisions are met. A system using SCRAM role assignment was originally designed to allow for decentralized coordination among physically realistic simulated humanoid soccer playing robots in the partially observable, non-deterministic, noisy, dynamic, and limited communication setting of the RoboCup 3D simulation league. In its current form,SCRAM role assignment generalizes well to many realistic and real-world multiagent systems, and scales to thousands of agents.
@InProceedings{AAAI15-MacAlpine,
author = {Patrick MacAlpine and Eric Price and Peter Stone},
title = {{SCRAM}: Scalable Collision-avoiding Role Assignment with Minimal-makespan for Formational Positioning},
booktitle = {Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI)},
location = {Austin, Texas, USA},
month = {January},
year = {2015},
abstract={
Teams of mobile robots often need to divide up subtasks efficiently. In
spatial domains, a key criterion for doing so may depend on distances between
robots and the subtasks' locations. This paper considers a specific such
criterion, namely how to assign interchangeable robots, represented as point
masses, to a set of target goal locations within an open two dimensional space
such that the makespan (time for all robots to reach their target locations) is
minimized while also preventing collisions among robots. We present scaleable
(computable in polynomial time) role assignment algorithms that we classify as
being SCRAM (Scalable Collision-avoiding Role Assignment with Minimal-makespan).
SCRAM role assignment algorithms use a graph theoretic approach to map agents
to target goal locations such that our objectives for both minimizing the
makespan and avoiding agent collisions are met. A system using SCRAM role
assignment was originally designed to allow for decentralized coordination
among physically realistic simulated humanoid soccer playing robots in the
partially observable, non-deterministic, noisy, dynamic, and limited
communication setting of the RoboCup 3D simulation league. In its current form,
SCRAM role assignment generalizes well to many realistic and real-world
multiagent systems, and scales to thousands of agents.
},
wwwnote={Accompanying videos at <a href="http://www.cs.utexas.edu/~AustinVilla/sim/3dsimulation/AustinVilla3DSimulationFiles/2013/html/scram.html">http://www.cs.utexas.edu/~AustinVilla/sim/3dsimulation/AustinVilla3DSimulationFiles/2013/html/scram.html</a>},
}
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