Lab Papers (GRASP)

Document Type

Journal Article

Date of this Version



Copyright 2009 IEEE.
Reprinted from:
Berman, S.; Halasz, A.; Hsieh, M.A.; Kumar, V., "Optimized Stochastic Policies for Task Allocation in Swarms of Robots," Robotics, IEEE Transactions on , vol.25, no.4, pp.927-937, Aug. 2009

This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to By choosing to view this document, you agree to all provisions of the copyright laws protecting it.


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.


Markov processes, distributed control, multi-robot systems, optimisation, stochastic systems, Markov processes, decentralized strategy, distributed control, homogeneous swarm robots, optimization problem, optimized stochastic policies, stochastic systems, task allocation, Distributed control, Markov processes, optimization, stochastic systems, swarm robotics, task allocation



Date Posted: 30 September 2009

This document has been peer reviewed.