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Transcript
Databases for Robotics
Applications
Thomas Young
Presentation Outline




Introduction
Database Solutions
Spatio Temporal Databases
TinyDB
Introduction
Various types of Database Applications


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Bipedal Robot Research
Obstacle Database
Sensor Networks
Moving Objects Database
Bipedal Robot Research
Training a bipedal robot to walk
Bipedal Robot Research
Bipedal Robot Research
Components


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Servo Motors
Sensors
PD Motor Control
Neural Net
Database
Bipedal Robot Research
Learning Process
Obstacle Database


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Database of natural obstacles
CFIT Problem in Aviation
Terrain Awareness System
Obstacle Database
Obstacle Database


eTAWS
Database of man made obstacles including bridges, towers,
overpasses, hydro lines, buildings
Obstacle Database



Standard relational database
Spatial representations stored as vectors or rasters
using an extended spatial type
Uses SQL queries
Sensor Networks

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Networks of either homogeneous or heterogenous
sensor types
Sensors characterized by power, computation,
communications
Networks characterized by configuration, types of
sensors
Harvard Motes
Smart Dust
Sensor Networks
Homogeneous Sensor Network
Moving Objects Database

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Database of objects that change position in time and
space
Tracking of vehicles, assets, people, animals
Fleet tracking
Scientific research
Surveillance
Moving Objects Database
Vehicular Traffic
Moving Objects Database
Firefighting Assets
Moving Objects Database
Ground Forces
Database Solutions



NOSQL
TinyDB
Spatio Temporal Databases
NOSQL

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No ACID guarantee
Distributed fault tolerant architecture
Do not follow a fixed schema
Performance and scalability
TinyDB



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Sensor Networks with nodes running TinyOS
Runs TinySQL (subset of SQL)
Extensible framework for attributes, commands, and
aggregates
Interacts with sensor network as a whole
Multiple concurrent queries
Entire sensor network is infinitely long table
Tuples consist of individual sensor and attributes
TinyDB
Energy Cost of a
query that
selects 100
tuples is less
than the cost
of a single
packet
transmission
Spatio Temporal Databases


Objects that move in space and time
Handle queries that index by an object, time or time interval,
physical location
t
t
y
y
x
x
(a)
a moving point
(b)
a moving and shrinking region
Spatio Temporal Databases
Query Examples
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Find all objects in a given area at a given time
Find all objects in a given area between these times
Find which object was closest to position X at time T
How many objects passed through area A at time T
Given spatio-temporal relationships R1 and R2, find
out which pairs intersected between T1 and T2
Spatio Temporal Databases
R Trees
Spatio Temporal Databases
Historical R Tree
y
o1
x
o3
o2
t1
t2
t3
t4
Query region Q
tnow
t
Spatio Temporal Databases


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How to store “now”? Use a large value…
Long lived objects will have very long MBRs,
difficult to cluster
Extensive overlap and empty space  bad
query performance for specific queries
Use partiallly persistent R-tree
Multi-version Binary Tree applied to R-tree
Spatio Temporal Databases
Trees at consecutive timestamps may share branches to save space
Spatio Temporal Databases
Trees at consecutive timestamps may share branches to save space.
Spatio Temporal Databases
HR-trees answer timestamp queries very efficiently.

A timestamp query degenerates into a spatial window query handled by
the corresponding R-tree at the query timestamp.
Not quite efficient:

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Expensive space consumption.
A node needs to be duplicated even when only one object moves.
Interval query processing is inefficient.
Although redundancy (from duplication) is necessary to maintain good
timestamp query performance, it is excessive in HR-trees
Spatio Temporal Databases
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What if you want to track only one object?
Use artificial deletes to get rid of others
Approximate the object using many small MBRs
This uses more space
Instead split the areas into minimum number of
MBRs that contain the objects that move the most
If object has constant velocity then equidistant splits
Given x splits the best splits can be determined in
O(xlogn) time
THE END
THANK YOU
References
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TinyDB Design Code and Implementations, Prakash Achutaramaiah
Implementation and Research Issues in Query Processing for Wireless Sensor Networks,
Wei Hong, Sam Madden
An On-Line Biped Mini-Robot Motion Learning Using Neural Network and Database
Management, Shih Fen Cheng, 2011 Seventh International Conference on Natural
Computing
Towards Sensor Database Systems, Bonnet Phillipe, Gehrke Johannes, Seshadri Praveen
Distributed Sensor Databases for Multi-robot Teams, Cowley Anthony, Hwa-Chow Hsu,
Camillo J, Taylor
Future Robotics Database Management System Alonw With Cloud TPS, Vijaykumar S,
Sarvanakumar S G, International Journal on Cloud Computing: Services and Architecture
(IJCCSA), Vol 1, No.3, Novermber 2011
[Tao & Papadias 01]:MV3R-Tree: A Spatio-Temporal Access Method for Timestamp and
Interval Queries. VLDB 2001: 431-440