databases for robotics applications

Databases for Robotics Applications

Thomas Young

Presentation Outline

Introduction Database Solutions Spatio Temporal Databases TinyDB

Introduction

Various types of Database Applications Bipedal Robot Research Obstacle Database Sensor Networks Moving Objects Database

Bipedal Robot Research

Training a bipedal robot to walk


Components Servo Motors Sensors PD Motor Control Neural Net Database


Learning Process

Obstacle Database

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

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

Database of objects that change position in time and space

Tracking of vehicles, assets, people, animals Fleet tracking Scientific research Surveillance


Vehicular Traffic


Firefighting Assets


Ground Forces

Database Solutions

NOSQL TinyDB Spatio Temporal Databases

NOSQL

No ACID guarantee Distributed fault tolerant architecture Do not follow a fixed schema Performance and scalability

TinyDB

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

(a) (b) a moving point a moving and shrinking region

y

t

x

y

t

x


Query Examples 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


R Trees


Historical R Tree

x

t t1 t3 t2

y

o2

o3

Query region Q

o1

t4 tnow


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


Trees at consecutive timestamps may share branches to save space


Trees at consecutive timestamps may share branches to save space.


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: 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


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

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

http://www.informatik.uni-trier.de/~ley/db/conf/vldb/vldb2001.html#PapadiasT01

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