  In this paper, we present algorithms for synthesizing controllers to
distribute a group (possibly swarms) of homogeneous robots (agents) over
heterogeneous tasks which are operated in parallel. We present algorithms as
well as analysis for global and local-feedback-based controller for the swarms.
Using ergodicity property of irreducible Markov chains, we design a controller
for global swarm control. Furthermore, to provide some degree of autonomy to
the agents, we augment this global controller by a local feedback-based
controller using Language measure theory. We provide analysis of the proposed
algorithms to show their correctness. Numerical experiments are shown to
illustrate the performance of the proposed algorithms.
