Infrastructure to support robots: a practical, scalable model for comparative evaluation of design choices

Abstract

It is easier to program effective robots when they inhabit highly structured environments. The growing literature on methods to aid robot design has given comparatively little consideration to elements external to the robot itself, yet such elements can encode or enhance information (to improve perception), can alter the effects or costs of actions (to help control), and can provide regularity by imposing constraints. External elements have the potential to be shared, to scale elastically, and to spread both benefits and installation/operating costs. These are traits of infrastructure in support of robots. We introduce a basic but flexible mathematical model –via the MDP framework– for rational evaluation of proposed additions and changes to environments, including where infrastructure may improve precision or performance of either perception or actuation. Through it, one can assess the numbers of agents needed for infrastructure investment to be economical, determine when installation costs would be recouped, and evaluate the effect of behavior changes as responses to environmental modifications. To demonstrate how the model can be instantiated, four simple but practical case studies are presented.