Using model-based diagnosis to build hypotheses about spatial environments a response to a technical challenge

Abstract

We present a method to build a hypothesis on the condition of the environment in which a robotic multi-agent team moves. Initially the robots have a default assumption about the conditions of the floor and on how moving under these condition works. For certain parts of the environment however, the default assumption may be wrong and moving around does not work in the expected way. Now the robotic team builds a hypothesis on the conditions of the yet unvisited part of the environment in a way similar to computing a diagnosis for electrical circuits. Resources can be saved by avoiding areas that possibly also contain obstacles.