Abstract

To enable the successful deployment of task-achieving multi-robot systems (MRS), the interactions must be coordinated among the robots within the MRS and between the robots and the task environment. There have been a number of experimentally demonstrated coordinated MRS; however, most have been designed through ad hoc procedures, typically providing task-specific, empirical insights with few contributions toward general-purpose, principled design methods.
This proposal presents a formal MRS design methodology applicable to homogeneous, distributed MRS performing sequential tasks in a Markovian world. We introduce a suite of systematic methods for synthesizing satisficing controllers for robots constituting a MRS. Each of these methods synthesizes a MRS that achieves system-level, task-directed coordination through the use of a variety of local control features: inter-robot communication, the maintenance of internal state, and both deterministic and probabilistic action selection. Complimentary to the synthesis methods, we present two MRS modeling approaches that share a common formal foundation with the MRS synthesis methods. The first modeling approach is a Bayesian macroscopic model and the second is a probabilistic microscopic model. Both models are capable of quantitatively predicting the task performance of a given MRS. Together, the unified synthesis and analysis methods provide more than just pragmatic design tools. Based on their common formal foundations and integrated nature, they provide a platform from which to formally characterize some relationships and dependencies among MRS task …

Date

January 1, 1970

Authors

Christopher Vernon Jones, M Mataric, G Sukhatme, K Lerman, S Koenig, U Mitra

Journal

USC Viterbi Robotics Labs, Ph. D. Thesis