Optimized stochastic policies for task allocation in swarms of robots

Spring Berman, Ádám Halász, M. Ani Hsieh, Vijay Kumar

Research output: Contribution to journalArticlepeer-review

204 Scopus citations


We present a scalable approach to dynamically allocating a swarm of homogeneous robots to multiple tasks, which are to be performed in parallel, following a desired distribution. We employ a decentralized strategy that requires no communication among robots. It is based on the development of a continuous abstraction of the swarm obtained by modeling population fractions and defining the task allocation problem as the selection of rates of robot ingress and egress to and from each task. These rates are used to determine probabilities that define stochastic control policies for individual robots, which, in turn, produce the desired collective behavior. We address the problem of computing rates to achieve fast redistribution of the swarm subject to constraint(s) on switching between tasks at equilibrium. We present several formulations of this optimization problem that vary in the precedence constraints between tasks and in their dependence on the initial robot distribution. We use each formulation to optimize the rates for a scenario with four tasks and compare the resulting control policies using a simulation in which 250 robots redistribute themselves among four buildings to survey the perimeters.

Original languageEnglish (US)
Pages (from-to)927-937
Number of pages11
JournalIEEE Transactions on Robotics
Issue number4
StatePublished - 2009
Externally publishedYes


  • Distributed control
  • Markov processes
  • Optimization
  • Stochastic systems
  • Swarm robotics
  • Task allocation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


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