Distributed Task Allocation in Homogeneous Swarms Using Language Measure Theory
This addresses task allocation in multi-robot systems, but appears incremental as it builds on existing methods like Language Measure Theory.
The paper tackles the problem of distributing homogeneous robot swarms over heterogeneous tasks by synthesizing controllers using global and local-feedback approaches based on Language Measure Theory and Markov chains, with numerical experiments illustrating performance.
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.