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


  • Patrick MacAlpine University of Texas at Austin
  • Eric Price University of Texas at Austin
  • Peter Stone University of Texas at Austin



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.




How to Cite

MacAlpine, P., Price, E., & Stone, P. (2015). SCRAM: Scalable Collision-avoiding Role Assignment with Minimal-Makespan for Formational Positioning. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1).



AAAI Technical Track: Multiagent Systems