Autonomous Observation


      			       Tarek M. Sobh



 
                                UUCS-92-041

                               December, 1992


                       Department of Computer Science
                              University of Utah
                       Salt Lake City, UT 84112 USA



                                  Abstract

We address the problem of observing an agent. We advocate a modeling
approach for the visual system and its observer, where a  discrete event
dynamic system (DEDS) framework is developed and ``events'' are defined as
ranges on parameter subsets. The dynamic recursive context for finite
state machines (DRFSM) is described with some applications in the
inspection and reverse engineering domain. We propose a system for observing
a manipulation process, where a robot hand manipulates an object. We recognize
the hand/object interaction over time and a stabilizing observer is 
constructed. Low-level modules are developed for recognizing the
events that causes state transitions within the dynamic manipulation system.
The work examines closely the possibilities for errors, mistakes
and uncertainties in the manipulation system, observer construction process
and event identification mechanisms. The DRFSM DEDS systems utilizes
different tracking techniques in order to observe and recognize tasks
and agents in an {\em active}, {\em adaptive} and {\em goal-directed} manner.


_____________________________________________________________________

This work was supported in part by DARPA grant N00014-91-J-4123 and NSF grant
CDA 9024721; Air Force AFOSR Grants 88-0244, 88-0296; Army/DAAL Grant
03-89-C-0031PRI; NSF Grants CISE/CDA 88-22719, IRI 89-06770; DARPA Grant
N0014-88-0630 and DuPont Corporation. All opinions, findings, conclusions
or recommendations expressed in this document are those of the author and
do not necessarily reflect the views of the sponsoring agencies.