Visualization and Intelligent Systems Laboratory
VISLab

 

 

Contact Information

VISLab
Winston Chung Hall Room 216
University of California, Riverside
900 University Avenue
Riverside, CA 92521-0425


Tel: (951)-827-3954

CRIS
Bourns College of Engineering
UCR
NSF IGERT on Video Bioinformatics

UCR Collaborators:
CSE
ECE
ME
STAT
PSYC
ENTM
BIOL
BPSC
ECON
MATH
BIOENG
MGNT

Other Collaborators:
Keio University

Other Activities:
IEEE Biometrics Workshop 2014
IEEE Biometrics Workshop 2013
Worshop on DVSN 2009
Multibiometrics Book

Webmaster Contact Information:
Alex Shin
wshin@ece.ucr.edu

Last updated: July 1, 2017

 

 

Sensing and Control

CAOS: A Hierarchical Robot Control System

Control systems which enabled robots to behave intelligently was a major issue in the process for automating factories. A hierarchical robot control system, termed CAOS for Control using Action Oriented Schemas with ideas taken from the neurosciences is presented. We used action oriented schemas (called neuroschemas) as the basic building blocks in a hierarchical control structure which was being implemented on a BBN Butterfly Parallel Processor. Serial versions in C and LISP are presented with examples showing how CAOS achieved the goals of recognizing three dimensional polyhedral objects.

Knowledge Based Robot Control on a Multiprocessor in a Multisensor Environment

Knowledge based robot control for automatic inspection, manipulation, and assembly of objects was projected to be a common denominator in highly automated factories. These tasks were to be handled routinely by intelligent, computer-controlled robots with multiprocessing and multi-sensor features which contribute to flexibility and adaptability. Discussed is the work with CAOS which was a knowledge based robot control system. The structure and components of CAOS were modeled after the human brain using neuroschemata at the basic building blocks which incorporated parallel processing, hierarchical, and heterarchical control.

Hierarchical Robot Control in a Multi-Sensor Environment

Automatic recognition, inspection, manipulation, and assembly of objects was projected to be a common denominator in highly automated factories. These tasks were to be handled routinely by intelligent, computer-controlled robots with multiprocessing and multi-sensor features which contribute to flexibility and adaptability.The control of a robot in such a multisensor environment became of crucial importance as the complexity of the problem grew exponentially with the number of sensors, tasks, commands, and objects. An approach which uses CAD (Computer Aided Design) based geometric and functional models of objects together with action oriented neuroschemas to recognize and manipulate objects by a robot in a multisensor environment is presented.

The Specification of Distributed Sensing and Control

Logical Sensor System Specification (LSS) had been introduced as a convenient means for specifying multi-sensor systems and their implementations. We demonstrated how control issues could be handled in the context of LSS. In particular, the Logical Sensor Specification was extended to include a control mechanism which permitted control information to flow from more centralized processing to more peripheral processes and be generated locally in the logical sensor by means of a micro-expert system specific to the interface represented by the given logical sensor.

A Framework for Distributed Sensing and Control

Logical Sensor Specification (LSS) was introduced as a convenient means for specifying multi-sensor systems and their implementations. We demonstrated how control issues could be handled in the context of LSS. In particular the Logical Sensor Specification was extended to include a control mechanism which permitted control information to flow from more centralized processing to more peripheral processes, and be generated locally in the logical sensor by means of a micro-expert system specific to the interface represented by the given logical sensor.

The Synthesis of Logical Sensor Specifications

A coherent automated manufacturing system needed to include CAD/CAM, computer vision, and object manipulation but most systems which supported CAD/CAM did not provide for vision or manipulation and similarly, vision and manipulation systems incorporated no explicit relation to CAD/CAM models. CAD/CAM systems emerged which allowed the designer to conceive and model an object and automatically manufacture the object to the prescribed specifications. If recognition or manipulation was to be performed, existing vision systems relied on models generated in an ad hoc manner for the vision or recognition process. Although both Vision and CAD/CAM systems relied on models of the objects involved, different modeling schemes were used in each case. A more unified system allowed vision models to be generated from the CAD database. The model generation was guided by the class of objects being constructed, the constraints imposed by the robotic workcell environment (fixtures, sensors, manipulators, and effectors). We proposed a framework in which objects were designed using an existing CAGD system and logical sensor specifications were automatically synthesized and used for visual recognition and manipulation.

ASP: An Algorithm and Sensor Performance Evaluation System

Described is a methodology which permitted the precise characterization of sensors, the specification of algorithms which transformed the sensor data, and the quantitative analysis of combinations of algorithms and sensors. Such analysis made it possible to determine appropriate sensor/algorithm combinations subject to a wide variety of criteria including: performance, computational complexity (both space and time), possibility for concurrency, modularization, and the use of multi-sensor systems for greater fault tolerance and reliability.