Scientific research associates advanced robotic system (SRAARS) is an intelligent robotic system which has autonomous learning capability in geometric reasoning. The system is equipped with one global intelligence center (GIC) and eight local intelligence centers (LICs). It controls mainly sixteen links with fourteen active joints, which constitute two articulated arms, an extensible lower body, a vision system with two CCD cameras and a mobile base. The on-board knowledge-based system supports the learning controller with model representations of both the robot and the working environment. By consecutive verifying and planning procedures, hypothesis-and-test routines and learning-by-analogy paradigm, the system would autonomously build up its own understanding of the relationship between itself (i.e., the robot) and the focused environment for the purposes of collision avoidance, motion analysis and object manipulation. The intelligence of SRAARS presents a valuable technical advantage to implement robotic systems for space exploration and space station operations

Chen, Alexander Y.

English

NASA Technical Reports Server

N90-27312
FIFTH CONFERENCE ON ARTIFICIAL INTELLIGENCE FOR SPACE APPLICATIONS
TITLE:
AUTHOR:
COMPANY:
ADDRESS:
THE REAL-TIME LEARNING MECHANISM OF THE SCIENTIFIC RESEARCH
ASSOCIATES ADVANCED ROBOTIC SYSTEM (SRAARS)
ALEXANDER Y. CHEN
SCIENTIFIC RESEARCH ASSOCIATES, INC.
50 NYE RD., P.O. BOX 1058, GLASTONBURY, CT 06033
ABSTRACT
SRAARS is an intelligent robotic system which has autonomous learning
capability in geometric reasoning. The system is equipped with one Global
Intelligence Center (GIC) and eight Local Intelligence Centers (LICs). It
controls mainly sixteen links with fourteen active joints, which constitute
two articulated arms, an extensible lower body, a vision system with two
CCD cameras and a mobile base. The on-board knowledge-based system
supports the learning controller with model representations of both the
robot and the working environment. By consecutive verifying and planning
procedures, hypothesis-and-test routines and learning-by-analogy paradigm,
the system would autonomously build up its own understanding of the
relationship between itself (i.e. the robot) and the focused environment
for the purposes of collision avoidance, motion analysis and object
manipulation. The intelligence of SRAARS presents a valuable technical
advantage to implement robotic systems for space exploration and space
station operations.
I. INTRODUCTION
While the control engineers are concentrating on the relationship
between actions and responses, the artificial intelligence discipline is
emphasizing mainly on understanding the processes and the descriptions of
the knowledge formation. Learning is one of the major items within the
overlapped area of these two disciplines. As mentioned in Reference 2,
learning can be divided into a high-level symbolic category and a
lower-level numeric variety. However, a complete learning mechanism will
employ symbolic knowledge to instruct the numerical control system, and the
information of sensor inputs will be processed to update or upgrade the
symbolic knowledge base. The integrated structure of both quantitative
descriptions and qualitative descriptions stands for a more appropriate
interpretation of the human learning function.
In order to maintain the learning procedure as a progressively
improving process, the parallel coherency of both the short term memory
(STM) and the long term memory (LTM) is necessary. There are several
approaches in maintaining this kind of operation, e.g. using neural
networks (Ref. 5), or employing schemata (Ref. 6). Furthermore, for a
real-time expert controlled system, STM should directly interact with the
control scheme and LTM would serve as the supervisor (or teacher) (Ref. 3).
Then, the resulting learning mechanism can fulfill the six-step knowledge
acquisition procedure: perception, strategy, decision making, execution,
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result and evaluation. As shown in Figure i, this closed-loop operation
would eventually develop a certain extent of understanding which can be
expressed as the integral of perceptions and the corresponded evaluations.
That is,
UNDERSTANDING = f PERCEPTION A EVALUATION (I)
The perceptions can be regarded as the qualitative descriptions of
the event, and the evaluations would be the quantitative interpretations of
the system's states. Therefore, a generic knowledge representation can be
expressed as a set of tuples. That is,
KNOWLEDGE = {(Qualifier[i], Quantifier[i]), i - 1,2 ..... } (2)
For a specific section of knowledge (e.g. automobile repair), the
formation may not be unique, but the utilization of the knowledge base
would have an objective standard to qualify the specific knowledge
representation as an expert system. This concept is adopted as SRAA S'
fundamental principle of establishing the real-time learning mechanism.
Not every quantitative representation has its corresponded wualita-
tive description; also, not every qualitative description can be expressed
in quantity. However, it is perceived that those situations which can not
be depicted as a tuple of qualifier and quantifier are normally handled
without explicit knowledge. In other words, they are termed as unreason-
able or illogical such that improving the system performance with learning
is uncertain. Therefore, excluding those cases will not affect the
generality of the learning mechanism. It is then assumed that every piece
of meaningful knowledge can be expressed in a tuple of (Qualifier,
Quantifier). When the process of understanding reaches the predetermined
level, the evaluated system performance would converge to the designated
standard and the machine intelligence will be regarded as an expert. Then
the system behavior under the expert controller without closed-loop
updating will be just as good as that of the feedback closed-loop in Figure
i. For safety reasons, the feedback closed-loop may still need to be
maintained for responding to the potentially changing environment.
However, the frequency of processing the understanding procedure can be
reduced from real-time to on-line supervising.
The main objective of the real-time learning of SRAA S is geometric
reasoning. Therefore, the perception is the geometric interpretation. The
strategy is to move the robotic system in an effective way such that the
unwanted collision can be avoided and the assigned object manipulation can
be achieved. Decision making would determine the strategy selection based
upon given knowledge of the physical correlation of the environment and the
robot. The execution would then carry out the decision in a model-
referenced adaptive control. The result would decide the termination of
the action; and the evaluation will praise the system performance based
upon the chosen criteria such as the difference between the desired design
and the actual accomplishment, the execution time and the repeatability,
etc. In the next section, these functions will be explained in detail.
The third section will introduce the actual implementation plan. A
prototype of SRAARS is under development. The anticipated results are
discussed in the final conclusion section.
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II. THE FRAMEWORK OF S_S INTELLIGENCE
SRAARS is a general purpose mobile manipulator. Its real-time
learning mechanism can be applied to any specific target domain. The only
limitation is the memory size. Three sets of representation elements would
require user input for qualitative expression, quantitative description and
relational specification, respectively. The knowledge system has a library
of inference tools (e.g. forward chaining and backward chaining, induction
and deduction) and a set of strategies for heuristics and hypothesizing.
The knowledge base is designed with the basic format of Equation 2. The
overall learning/control system of SRAARS is illustrated in Figure 2.
The user input set of qualitative expressions would be the entire set
of vocabulary which the system can accept and utilize. The current level
of the system intelligence does not have the capability of generating any
new expressions by itself. The set of quantitative descriptions has two
subsets: one contains the symbolic specifications of variables, parameters
and constants, such as ranges (e.g. minimum, maximum), types (e.g. integer,
real, or boolean), and formats (e.g. scalar, vector or matrix); another is
comprised of the corresponded numerical values of each entity specified in
the previous subset. Every symbolic entity can represent only one item
throughout the entire learning/control process to eliminate unnecessary
internal conflict. The third input set of relational specification would
provide the initial system knowledge in linking and grouping all the
available qualifiers and quantifiers. Some examples of relational
specifications are formula or equations among symbolic quantities, rules of
thumb among qualitative terms or certain production rules which relate both
qualitative and quantitative items.
II.i KNOWLEDGE UTILIZATION
Most currently available expert systems require a certain extent of
human involvement (Ref. 7). Ranging from off-line query format man-machine
interface to on-line process advisory, the developed knowledge systems are
designated as an auxiliary component in the real world operations, which is
because the knowledge programming is still confined to the level of
managing the expressiveness of various knowledge representations, and
the methodology for actually utilizing the digested information is not
available. The knowledge evolution is basically a dynamic process. Since
there are quite a few methods to express the understanding of one
particular issue, and a major proportion of knowledge of understanding does
not have a direct connection to the necessary action. Therefore, knowledge
utilization itself becomes a rather ambiguous phenomenon. Fortunately, it
is less ambiguous in those occasions of skill learning (Ref. 2). The
knowledge utilization of SRAARS will serve two purposes: improving the
understanding of the working environment and employing the updated
geometric knowledge of both the robot and its surroundings to execute the
given assignment.
In the field of robotics, problems such as the reachability analysis
of articulated mechanical systems with redundancy, the collision avoidance
problem of mobile robots and the object extraction of robot vision are very
difficult to solve in a general platform. The human approach of dealing
with these problems is through a long period of learning. Therefore, the
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knowledge system of SRAARSis regarded as the core solution of mobile
manipulation of the robot in general environments.
11.2 STRATEGYGENERATION
The system is initiated with knowledge of models of both the robot
and the environment. The strategy at the beginning would be assuming these
models are correct and plan the action accordingly to perform the given
task. The decision maker then takes this strategy to execute the user
commands. Somepossible commandsare "find object A in location C", "find
object B in location D", "insert object A into object B, then place the
sub-assembly on the conveyer belt", etc. The user commandswould be
translated into a sequenceof actions for the controller to execute. The
controller would employ a model-referenced adaptive schemeto plan the
motion trajectory based upon the models and the assignment. Then, the
actuating systems will be directed to realize the planned actions.
At the sametime, an initial sensing action with default frequency
is issued to monitor the robot motion and the changing environment.
Therefore, both the vision system and other sensors such as ultrasonic
sensors, proximity sensors or laser range finders, are sending data back to
the brain. The data processor then converts the data into two formats. A
direct conversion of input data to the values of system variables will be
sent to the controller to serve as the output feedback of the control law.
It will still maintain the data format. Another more complicated
conversion will examine the input data with the existing knowledge base.
The result is converted into an information format. The result will then
be processed through the inference engine to conclude as knowledge
verification and validation.
If there is a certain unacceptable inconsistency detected, then other
strategy generation will be activated. The activation and the termination
of different strategies are embeddedin the kernel of the knowledge base
which the user will not be able to access. However, users can change the
level of acceptable inconsistency which triggers the activation and the
termination of certain strategies, and increase or alter the strategy
library. There are essentially two types of alternative strategies:
heuristic approaches or hypothesis-and-test. The heuristic strategies are
particularly suitable for object searching. Whena segmentof the
environment model is detected to be incorrect (e.g. the original model does
not have an object in that segment and current information indicates there
is something in that area, or the previous model shows that there should be
an object in that segmentbut the current sensor information cannot verify
the existence of the object), a heuristic search strategy can be issued to
take certain actions to modify the current model.
The hypothesis-and-test strategy can be utilized to resolve the
potential reachability problem of the robot. The relationship between the
robot model and the environment model can be very delicate when certain
compliance activity is required. The articulated robot with many redundant
degrees of freedom has a better chance to perform difficult interior
operations. However, a general solution mayrequire substantial
computational effort (Ref. 4). Employing the knowledge engineering
technique with an adequate hypothesis-and-test strategy is a practical
334
solution to resolve these types of problems. For this particular
situation, there may not be any inconsistencies in modeling; instead, the
maneuverability of the robot is the main focal point. If the robot has n
degrees of freedom, then the projection of n dimensional joint space to the
three-dlmensional world coordinates present a very complicated
combinatorial problem, especially when the topological structure contains
tree branches or closed-loops. This type of knowledge system application
will be the first step of utilizing artificial intelligence to solve some
problems which even human beings cannot provide a definite solution.
III. THE SYSTEM DESIGN OF SRAARS
The realization of SRAARS emphasizes more on the system feasibility
than on the utilization of the state-of-the-art microprocessor technology.
It is fully aware that an adequate integration of both CISC and RISC
architectures, such as Intel 80486 and 80860, Motorola 68040 and 88000
series, may provide a more powerful platform within a miniature form factor
for intelligent robot development. However, this technology is not yet
matured. It is therefore considered as the system design platform of the
next generation of SRAARS. Intel 80386 is selected as the main processor
for current SRAARS development. The major bus connection between GIC and
LICs is determined to be VME bus.
The Global Intelligence Center is regarded as the brain and the
central neural system of SRAARS. It has the following major features:
- Multitasking with virtual 8086 mode;
- Asynchronous processing of memory management, computation,
inference and I/O interface;
- Hierarchical interrupt control; and
- Real-time kernel with concurrent programming constructs.
The functional diagram of SRAARS's brain is shown in Figure 3. Each
individual modular functionality is listed as follows:
C3BUS:
- Mother-daughter communication handshake
- Command send out to designated LICs
- Receive status reports from LICs
- Network management and control
- Record update and archive
RVR:
Image coprocessor communication
Frame buffer management
Image analysis, including edge detection, motion detection and
object extraction
SANE :
Sensor sampling and data acquisition control
- Sensor/actuator network management
Device-independent database management
Input buffer update
Limited sensor fusion and impulsive reactions
335
EAN-
Environment model formulation and update
- Consistency analysis
Map formulation, revision and interpretation
KBL:
Knowledge base formulation
Knowledge utilization including rule extraction, rule-data
association, and strategy generation
Knowledge base/data base integration
Knowledge input processing
MSM:
Memory management among various devices (e.g. ROM, RAM, or disks)
Memory initialization and reboot sequence
Memory swap, cache and page
Backup routine
Memory status update
SPE:
Model-based control monitoring
Self-correction and self-calibration
System power monitoring
System performance record
MMI:
Human operator input interrupt handling
Panel-driven task assignment input
Task analysis
Task recording and retrieving
Task planning
Task status update
There are eight Local Intelligence Centers. Six of them are employed
for controlling the mechanical linkage manipulation. Every one of the six
LICs would control two to three degrees of freedom of the robot. The
decentralized control systems would carry out the spatial planning commands
under AISP (Ref. I). The seventh LIC is a dedicated machine vision system
which would control two CCD cameras and two to three degrees of freedom of
motion to manipulate the orientations of cameras. Lower level image
processing will be accomplished in this LIC as well. The eighth LIC is
utilized to control the mobility of the base which includes the driving,
steering, braking and backup operations. Some major features of LICs are
listed below:
Mother-daughter interactive communication
Local trajectory planning and execution
Status report back to GIC
Impulsive response override
The connection between GIC and LICs basically follows the VME
specification. GIC and Four LICs, including vision and base control, are
located at the mobile base compartment. The rest of LICs will be located
at the central portion of the lower body. There is a dedicated cable to
336
connect the "head", which contains cameras and the corresponded positioning
mechanism, to the seventh LIC situated at the base. A general mockup of
SRAARS is shown in Figure 4.
IV. THE CONCLUSION
The real-time learning mechanism of SRAARS is one of the innovative
ideas which are incorporated into the development of SRAARS. It takes a
plain and straightforward approach to integrate the knowledge system into
the real world applications. The developed learning capability will move
the knowledge engineering into the central arena of operating a complex
dynamic system and perform as a decision maker. It would be interesting to
see the comparative analysis with other conventional approaches if the
explicit general solution may one day become available.
ACKNOWLEDGEMENTS
This research and development is supported by Contract NAS7-1012 of
the NASA SBIR Program, from the NASA Resident Office at Jet Propulsion
Laboratories, Pasadena, CA.
REFERENCES:
. Alexander, Y.K. Chen, "AISP-A Robot Intelligence System in Spatial
Manipulation," at ROBEXS '89, The Annual Workshop on Robotics and
Expert Systems, Palo Alto, CA, August 2-4, 1989.
.
Anderson, R.L., "A Robot Ping-Pong Player: Experiment in Real-Time
Intelligence Control," The MIT Press, Cambridge, MA, 1988.
° Fu, K.-S., "Learning Control Systems," Reprinted in IEEE Trans.
Pattern Analysis and Machine Intelligence, Vol. PAMI-8, No. 3, May
1986, pp. 327-342.
. Goldenburg, A.A., Benhahib, B. and Fenton, R.G., "A Complete
Generalized Solution to the Inverse Kinematics of Robots," IEEE J.
Robotics and Automation, VoI._RA-I, No. i, pp. 14-20, March 1985.
. Grossberg, S., "Neural Networks and Natural Intelligence," Bradford
Books, MIT Press, Cambridge, MA, 1988.
, Hanson, A.R. and Riseman, E.M., " VISIONS: A Computer Vision System
for Interpretation of Natural Scenes," in Computer Vision Systems,
(Hanson, A.R. and Riseman, E.M. eds.), Academic Press, New York, NY,
1978, pp. 303-334.
. Hayes-Roth, F., Waterman, D.A. and Lenat, D.B. eds., "Building Expert
Systems," Addison-Wesley Publishing Co., Reading, MA, 1983.
337
UNDERSTANDING
I EXECUTION
I _°'_ t_
I_w'°_'°Nt
Figure I. The Evolution Process of Understanding.
KNOW-
LEDGE
INPUT J KNOWLEDGE INFERENCE
"7 BASE
t
COMMAND
ENGINE '_
KNOWLEDGE
r
GENERATOR
DATA
PROCESSOR
DATA
DATA
CONTROLLER
w_ DECISIONMAKER
SENSOR
INPUTS
ACTIONS_ /
!
ENVIRONMENT
Figure 2. The Functional Diagram of the Learning Control System of SRAARS.
338
Figure 3. Functional Diagram of SRAARS's Brain.
339
Figure 4. SRAARS Mockup
34O


The real-time learning mechanism of the Scientific Research Associates Advanced Robotic System (SRAARS)

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