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Patrick MacAlpine, Eric Price, and Peter
Stone. SCRAM: Scalable Collision-avoiding Role Assignment with Minimal-makespan for Formational Positioning. In
AAMAS Autonomous Robots and Multirobot Systems Workshop (ARMS 2014), May 2014.
Accompanying videos at http://www.cs.utexas.edu/~AustinVilla/sim/3dsimulation/AustinVilla3DSimulationFiles/2013/html/scram.html
[PDF]321.9kB [postscript]911.8kB
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 to a set of target goal locations such that the makespan (time for all robots to reach their target locations) is minimized while also preventing collisions among robots. We present a scalable multiagent dynamic role assignment system, used for formational positioning of mobile robots, known as SCRAM (Scalable Collision-avoiding Role Assignment with Minimal-makespan). SCRAM uses a graph theoretic approach to map robots to target goal locations such that our objectives for both minimizing the makespan and avoiding robot collisions are met. The system 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 generalizes well to many realistic and real-world multiagent systems, and scales to thousands of robots, as role assignment algorithms run in polynomial time.
@InProceedings{ARMS14-MacAlpine,
author = {Patrick MacAlpine and Eric Price and Peter Stone},
title = {{SCRAM}: Scalable Collision-avoiding Role Assignment with Minimal-makespan for Formational Positioning},
booktitle = {AAMAS Autonomous Robots and Multirobot Systems Workshop (ARMS 2014)},
location = {Paris, France},
month = {May},
year = {2014},
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 to a set of target goal
locations such that the makespan (time for all robots to reach their target
locations) is minimized while also preventing collisions among robots. We
present a scalable multiagent dynamic role assignment system, used for
formational positioning of mobile robots, known as SCRAM (Scalable
Collision-avoiding Role Assignment with Minimal-makespan). SCRAM uses a graph
theoretic approach to map robots to target goal locations such that our
objectives for both minimizing the makespan and avoiding robot collisions are
met. The system 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 generalizes well to many realistic and real-world
multiagent systems, and scales to thousands of robots, as role assignment
algorithms run in polynomial time.
},
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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