Peter Stone's Selected Publications

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SCRAM: Scalable Collision-avoiding Role Assignment with Minimal-makespan for Formational Positioning

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

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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.

BibTeX Entry

@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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