[Robotgroup] Triangulation using a Rich Skyline Telescope

[Clendon Gibson]

This is much like the 'navigation' that was used for the Voyager space probes. I put navigation in quotes because they didn't really do course changes so much as use a precise knowledge of position and orientation to aim the camera and other sensors. 

Basically V-GER always knew roughly where certain stars where. When it needed to check position it would swing the camera around to look at them and make any fine adjustments needed. 

As a refinement to the rich skyline telescope, make some of the lights different colors. 

----- Original Message ----
From: Bruce Waters <biwaters at austin.rr.com>
To: robotgroup at puremagic.com
Sent: Saturday, February 2, 2008 7:05:58 AM
Subject: [Robotgroup] Triangulation using a Rich Skyline Telescope


I 
agree 
with 
others 
who 
have 
mentioned 
the 
difficulty
of 
achieving 
the 
accuracy 
(centimeters) 
at 
the 
costs
allowed 
using 
classical 
RF 
triangulation.  
 
If 
you 
are
willing 
to 
consider 
a 
rather 
dramatic 
modification 
of 
the 
beacons, 
you 
might 
find 
meeting 
those 
specs 
more 
realistic.  
 
My 
approach 
would 
be 
to 
use 
a 
large 
number 
of 
led's 
(eg. 
Christmas 
light 
strings) 
nonuniformly 
arranged 
around 
the 
boundary(or 
elsewhere) 
to 
provide 
a 
high 
angular 
resolution 
"skyline".  
 
To 
reduce
ambiguity 
it 
is 
important 
to 
insure 
that 
the 
interlight
spacing 
not 
be 
excessively 
uniform.

I 
would 
use 
a 
cheap 
telescope 
(with 
a 
right/left 
pair 
of 
light 
edge 
sensors 
at 
the 
focus) 
at 
the 
same 
height 
as 
the 
led's.  
I 
would 
sweep 
or 
rotate 
the 
telescope.  
I 
would 
train 
the 
robot 
on 
the 
spacing 
of 
the 
led's 
from 
some 
known 
near-central 
point.  
 
I 
would 
move 
the 
robot 
directly 
at 
some 
specific 
led 
beacon(a 
non-rotating 
second 
telescope 
might 
facilitate 
this) 
and 
develop 
a 
mathematical 
(initially 
tabular, 
then 
add 
sophistication) 
model 
of 
the 
variations 
in 
angles 
to 
the 
rest 
of 
the 
skyline.  
  
 
I 
would 
return 
to 
the 
central 
point 
and 
pick 
some 
number 
of 
additional 
headings 
to 
train 
the 
bot.  
Consider 
mounting 
the 
telescope 
low 
and 
using 
software 
techniques 
to 
ignore 
the 
wheels.

Learning 
the 
skyline 
allows 
very 
low 
precision(just 
string 
chunks 
of 
it 
around 
the 
boundary) 
initial
placement 
the 
skyline.  
It 
allows 
software 
improvements 
to 
accuracy 
and 
data 
volume 
requirements.  
It 
provides 
an 
angular 
resolution 
related 
to 
the 
telescope 
power 
and 
the 
number 
of 
led's 
in 
the 
skyline.  
 
It 
allows 
local 
incremental 
improvement 
of 
the 
resolution 
by 
adding 
skyline 
in 
appropriate 
places.  
 
It 
is 
an 
interesting 
continuing 
project 
to 
improve 
the 
performance 
without 
spending 
more 
on 
hardware.  
 
It 
can 
be 
wonderfully 
cheap 
for 
the 
performance 
delivered.

A 
valuable 
improvement 
you 
should 
consider 
is 
an 
elevation 
tracking 
gimbal 
mount 
for 
the 
telescope 
to 
reduce 
the 
exaggerated 
sensitivity 
to 
tilt 
of 
the 
robot 
and 
vertical 
variations 
in 
the 
skyline 
as 
the 
telescope 
rotates.  
 
The 
light 
sensor 
for 
this 
improvement 
should 
be 
upgraded 
to 
at 
least 
four 
quadrant 
and 
could 
go 
(fast, 
low 
res)
video.  
Note 
that 
the 
skyline 
does 
not 
have 
to 
be 
continuous 
but 
that 
precision 
does 
require 
that 
adjacent 
beacons 
be 
visible 
at 
angles 
that 
are 
not 
too 
acute 
in 
several 
major 
compass 
headings.

Just 
like 
radio 
beacons, 
chance 
light 
sources 
in 
the
skyline 
may 
be 
used 
as 
beacons 
in 
some 
cases 
so
your 
robot 
may 
function 
adequately 
in 
some
environments 
without 
additional 
beacons.  
The
elevation 
control 
and 
perhaps 
a 
zoom 
control
on 
the 
telescope 
can 
greatly 
enhance 
such 
serendipitous 
operation.  
 
The 
flexibility 
and
adaptability 
of 
this 
approach 
is 
outstanding.
It 
is 
a 
wonderful 
platform 
to 
explore 
fuzzy
logic, 
data 
volume 
reduction 
techniques, 
and 
high 
reliability 
software 
concepts.

Bruce 
Waters
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