The previous generation of robotic vehicles and drones was designed for a specific task, with limited flexibility in executing their mission. This limited flexibility arises because the robotic vehicles do not possess the intelligence and knowledge upon which to make significant tactical decisions. Current development of robotic vehicles is toward increased intelligence and capabilities, adapting to a changing environment and altering mission objectives. The latest techniques in artificial intelligence (AI) are being employed to increase the robotic vehicle's intelligent decision-making capabilities. This document describes the design of the SARA spatial database tool, which is composed of request parser, reasoning, computations, and database modules that collectively manage and derive information useful for robotic vehicles

Interrante, Mark

Scott, Steven

English

NASA Technical Reports Server

N B  8-  1727 8 
AN EFFICIENT REPRESENTATION OF 
SPATIAL INFORMATION FOR EXPERT 
REASONING IN ROBOTIC VEHICLES 
by Steven Scott 
and Mark Interrante 
Texas Instruments 
Corporate Artificial Intelligence Lab 
Dallas, Texas 
ABSTRACT 
The previous generation 'of robotic 
vehicles and drones were designed for a 
specific task, with limited flexibility 
in executing their mission. This 
limited flexibility arises because the 
robotic vehicles do not possess the 
intelligence and knowledge upon which to 
make significant tactical decisions. 
Current development of robotic vehicles 
is toward increased intelligence and 
capabilities, adapting to a changing 
environment and altering mission 
objectives. The latest techniques in 
Artificial Intelligence (AI) are being 
employed to increase the robotic 
vehicle's intelligent decision making 
capabilities. This document describes 
the design of the SARA spatial database 
tool at Texas Instruments, which is 
composed of request parser, reasoning, 
computational, and database modules that 
collectively manage and derive 
information useful for robotic vehicles. 
1. INTRODUCTION 
In order for future autonomous systems 
to efficiently reason about their 
environment it is necessary for them to 
maintain a consistent world model. Such 
models impose increasing demands for the 
supply, storage, and maintenance of 
large knowledge or information bases. 
While conventional database management 
systems (DBMS) perform much of the 
storage and retrieval of information, 
they are generally not powerful enough 
to deal with multi-dimensional 
(N-dimensional) world data or 
intelligently reason about the data they 
contain. 
In support of Texas Instrument's work in 
avionic expert systems, a tool is being 
built to support the construction, 
maintainence, and usage of world models. 
The SARA spatial database retrieves, 
analyzes, abstracts, and maintains large 
multi-dimensional information bases that 
require efficient retrieval based of the 
spatial location of the data objects. 
The system manipulates both factual 
data, such as "the location of an 
airport", and derived abstractions, such 
as "areas of danger to low flying slow 
aircraft". Also provided is inferencing 
based on changing database values and 
the ability to perform complex 
algorithmic computations on the data. 
As the SARA tool continues to develop, 
the bonds between databases, algorithmic 
computations, and expert system 
reasoning will certainly be strengthened 
and more closely unified. 
The examples are from a sample 
application of SARA in the airspace 
environment. The system is quite 
modular and flexible, allowing easy 
customization to specific applications. 
2. SYSTEM ARCHITECTURE OVERVIEW 
The SARA tool is composed of four 
the request parser, modules : 
triggering, algorithmic computation, and 
database. Each of these modules contain 
components for module communication and 
request management. Figure-I shows the 
four SARA modules and how application 
programs using SARA communicate directly 
and only with the request parser. 
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2.1. REQUEST PROCESSING 
The SARA system has an integrated syntax 
with nesting of event triggering, 
computation, and database operations. 
Each module has an associated request 
queue manager that is responsible for 
regulating its work flow and keeping 
track of paperwork. The module itself 
is allowed to process operations 
uninterrupted by the hassles of task 
management. This flexibility has led to 
the design of the SARA system shown in 
f igure-2. 
In the SARA system, requests arrive from 
the application program and conform to 
the SARA grammar. Requests enter the 
system through the request parser. 
After the request parser validates the 
syntax of the request, it ships the 
request to its request queue manager. 
The request queue manager then splits 
the requests into tasks for specific 
modules to handle. Tasks may be nested, 
requiring the services of another 
module. When the request queue manager 
of the intended module (i.e. the 
triggering module) receives tasks from 
the request queue manager of another 
module (i .e. the request parser), it 
schedules time with its module for 
completion of the task. At this point, 
the task does not contain uncompleted 
tasks for other modules. This task is 
atomic for the module and referred to as 
an operation. 
2.1.1 REQUEST PARSER MODULE 
The request parser module verifies the 
request syntax , initially splitting the 
request into tasks, and forwarding the 
tasks to the appropriate module for 
processing. After validating request 
syntax and contracting out the tasks to 
the other modules, the request parser 
continues with the next request. When 
the final result from the tasks is 
produced, the request parser responds to 
the application program with the result. 
2.2 ALGORITHMIC COMPUTATION MODULE 
The computation module is designed to 
contain compute bound algorithms which 
may benefit from the use of special 
numerical hardware. Depending on the 
application program, these algorithms 
could be short-term or background low 
priority processing. Since the SARA 
system is highly modular, the 
computation module could reside on a 
different machine running whatever 
language is available for the machine. 
Additionally, multiple computation 
modules could be established, each 
performing a specific range of tasks and 
thus giving the user the ability to 
configure-a SARA system to solve the 
application problem. 
2.3 SPATIAL DATABASE MODULE 
The spatial database (SDB) provides 
rapid processing of multi-dimensional 
data within a relational framework. 
Applications needing spatial retrieval 
of information range from VLSI computer 
aided design to robotics. The SDB 
module accepts a SQL-like grammar 
sentence as input and translates it into 
syntax for the Texas Instrument’s 
Relational Table Management System 
CRTMS). The SARA SDB approach is based 
on manipulating entire objects rather 
than breaking the spatial representation 
down, as in quad-trees. This processing 
is supported by the spatial index, which 
is a variation of the R-tree CInterrante 
871. The spatial index provides for 
efficient retrieval of information or 
objects possessing clustering 
characteristics that, in theory, would 
yeild less page faults and better 
performance than traditional database 
indexes. The SDB module consists of 
four components: the machine specific 
database tool (RTMS), the SQL to RTMS 
syntax translator, boundary data types 
and operators, and the spatial index. 
2.3.1 RELATIONAL TABLE MANAGEMENT SYSTEM 
The Texas Instrument’s Relational Table 
Management System (RTMS) is a relational 
database for the Explorer lisp machine. 
Since RTYS is built on top of the lisp 
environment, any predicate lisp 
expression can be used in the where 
clause of a query as a condition for 
tuple selection. RTMS has been enhanced 
with special purpose data types and 
predicates for the spatial database. 
The following RTMS query produces the 
airplanes at Ohara airport. In other 
words, retrieve the airplanes whose 
position is enclosed by the boundaries 
of Ohara airport. 
(RETRIEVE airplanes WHERE 
(enclose 
(RETRIEVE airports 
aircraf t-position) ) 
PROJECT (boundaries) 
WHERE <= airport-id wOharan>> 
528 
APPLICATION PROGRRU  
I REQUEST PRRSER I 
request  s 
RLGORIT H n l  C SPRT 1 RL 
COflPUTRTION DATRBRSE 
flODULE flO0ULE 
EVENT 
TRIGGERING 
UOOULE 
1 
F I G U R E  1. O u e r v i e H  o f  t h e  SARA S y s t e m  A r c h i t e c t u r e .  
REQUEST 
PARSER U 
I I UOOULE EVENT 1 R 1  GCERl NG 
ISRRR Cornput +a t  i on y z  
MANRGER flRNRCER 
I RLGORITHnIC  SPRT I RL COI1PUTRT 1 ON I I DRTABRSE I UODULE I1ODULE --
FIGURE 2. I n t e r n a l  C o m p o n e n t s  o f  SARA S h o w i n g  How t h e  
S e n t e n c e  C o m p o s i t i o n  C h a n g e s  D u r i n g  t h e  L i f e  o f  
a R e q u e s t .  
529 
2.3.2 BOUNDARY DATA TYF'ES AND OPERATORS 
In addition to the the usual database 
data types, a set of data types 
multi-dimensional objects have been 
defined specifically for the spatial 
database. The currently defined 
boupdary data types are: rectangular 
solid, point, and cylinder. Future 
releases of the SARA system plan on 
supporting trajectories, cones, 
polygons, and ellipsoids. The operators 
UNION, ENCLOSE and OVERLAP are defined 
on the boundary data types. These 
operators form the basic level of a 
spatial database query understood by 
SARA. 
representing the boundaries of 
2.3.3 SPATIAL INDEX 
The SARA database provides faster access 
of spatial objects through the spatial 
index, which clusters objects based on 
locality. The spatial index used is a 
modified R-Tree, which is comparable to 
a multi-dimensional B-Tree. The R-tree 
differs from a Quad tree in that, with 
the R-Tree, individual objects are not 
decomposed into smaller components 
CGuttman 831. In this implementation 
the actual shape of the object must be 
approximated by using one of the 
boundary data types. Support for 
modeling of objects at various levels of 
detail may be possible using persistent 
objects to organize the objects into a 
hierarchy CThatte 861. 
The spatial index provides partitioning 
mechanisms, allowing the relations to be 
clustered together based on similiar 
characteristics (i . e. static/dynamic 
and objects/regions>. These partitions, 
defined by the database administrator 
(DBA) when the database is created, only 
influence speed and not functionality. 
Partition can be based on how often an 
object is updated (static or dynamic) or 
how large the object boundaries are 
(objects or regions). The static, 
dynamic, object, and region 
classifications are only examples of the 
partitioning advantages, the definition 
and use being under full user control. 
2.4 EVENT TRIGGERING MODULE 
The event triggering module is 
responsible for setting up, maintaining, 
and checking conditions on which the 
application program has asked to be 
notified. The action taken when the 
event or condition is triggered can be a 
notification to the application program 
or some SARA task to perform. One 
action might be to reanalyze certain 
database information and derive data 
abstractions needed some time in the 
near future. Other conditions could 
require the application program to be 
periodically or continuously notified of 
the event. The triggering module is 
sufficiently general as to suit most 
event recognition situations. 
Triggers can be set to fire in four 
different ways. They can fire the first 
time a condition becomes true or every 
time the condition becomes true. In 
addition, triggers can fire continuously 
when a condition is true or they can 
fire only when the boolean condition 
changes value (i.e. from true to 
false) . 
Event triggering is implemented in a 
forward chaining rule based system with 
some support from the spatial database. 
Triggers are written as rules and may 
have embedded database and computation 
requests. Triggers that depend on 
spatial location are highly efficient 
because the database selectively 
postpones evaluation of the condition 
until it could possibly become true, 
thus eliminating unnec e s s ar y 
evaluations. 
Two of the main uses of triggers are in 
notifying the application user of 
significant changing events and the 
creation of data abstraction levels in 
the database. The creation of 
abstraction levels is a powerful aspect 
of triggers as new tables and objects in 
the database can be built with derived 
relationships and groupings of objects. 
Triggers can also update the 
abstractions as the significant events 
change. 
3. SUMMARY 
This paper described the SARA spatial 
database tool which is composed of 
request parser, database, reasoning, and 
530 
computational modules that collectively 
manage and derive large multi- 
dimensional information bases. 
Efficient retrieval of objects by 
spatial location is provided in the 
database. The system manipulates both 
the factual data and derived 
abstractions. Also provided is 
inferencing based on changing database 
values and the ability to perform 
complex algorithmic computations on the 
data. 
ACKNOWLEDGEMENTS 
The authors would like to thank all of 
those who have contributed to the SARA 
project at the Texas Instruments 
Computer Science Center, especially Karl 
Schricker, Trish Dutton and Mike Ernst. 
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An efficient representation of spatial information for expert reasoning in robotic vehicles

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