Autonomous Exploration: Driven by Uncertainty

Authors: Peter Whaite and Frank P. Ferrie
File name: whaite-autonomous-exploration.ps.gz

Abstract:

Passively accepting measurements of the world is not enough, as the data we
obtain is always incomplete, and the inferences made from it uncertain to a
degree which is often unacceptable.  If we are to build machines that operate
autonomously they will always be faced with this dilemma, and can only be
successful if they play a much more active role.  This paper presents such a
machine.  It deliberately seeks out those parts of the world which maximize
the fidelity of its internal representations, and keeps searching until those
representations are acceptable.  We call this paradigm autonomous exploration,
and the machine an autonomous explorer.

This paper has two major contributions.  The first is a theory that tells us
how to explore, and which confirms the intuitive ideas we have put forward
previously.  The second is an implementation of that theory.  In our
laboratory we have constructed a working autonomous explorer and here for the
first time show it in action.  The system is entirely bottom-up and does not
depend on any a priori knowledge of the environment.  To our knowledge it is
the first to have successfully closed the loop between gaze planning and the
inference of complex 3D models.