The Automated Rendezvous and Docking system (ARAD) is composed of two parts. The first part is the sensor which consists of a video camera ringed with two wavelengths of laser diode. The second part is a standard Remote Manipulator System (RMS) target used on the Orbiter that has been modified with three circular pieces of retro-reflective tape covered by optical filters which correspond to one of the wavelengths of laser diode. The sensor is on the chase vehicle and the target is on the target vehicle. The ARAD system works by pulsing one wavelength laser diodes and taking a picture. Then the second wavelength laser diodes are pulsed and a second picture is taken. One picture is subtracted from the other and the resultant picture is thresholded. All adjacent pixels above threshold are blobbed together (X and Y centroids calculated). All blob centroids are checked to recognize the target out of noise. Then the three target spots are windowed and tracked. The three target spot centroids are used to evaluate the roll, yaw, pitch, range, azimuth, and elevation. From that a guidance routine can guide the chase vehicle to dock with the target vehicle with the correct orientation

Book, Michael L.

Bryan, Thomas C.

Dabney, Richard W.

Howard, Richard T.

English

NASA Technical Reports Server

vN93-11954
AUTOGUIDANCE VIDEO SENSOR FOR DOCKING
by Michael L. Book( EB24 ),
Thomas C. Bryan( EB24 ),
Richard T. Howard( EB24 ),
and Richard W. Dabney( EDI3 )
Marshall Space Flight Center NASA
Huntsville, Alabama 35812
The Automated Rendezvous and
Docking system is composed of two
parts. The first part is the sensor
which consists of a video camera ringed
with two wavelengths of laser diode.
The second part is a standard Remote
Manipulator System( RMS ) target used
on the Orbiter that has been modified
with three circular pieces of retro-
reflective tape covered by optical
filters which correspond to one of the
wavelengths of laser diode. The sensor
is on the chase vehicle and the target
is on the target vehicle. The ARAD
system works by pulsing one wavelength
laser diodes and taking a picture. Then
the second wavelength laser diodes are
pulsed and a second picture is taken.
One picture is subtracted from the
other and the resultant picture is
thresholded. All adjacent pixels above
threshold are blobbed together( X and Y
centroids calculated ). All blob
centroids are checked to recognize the
target out of noise. Then the three
target spots are windowed and tracked.
The three target spot centroids are
used to evaluate the roll, yaw, pitch,
range, azimuth, and elevation. From
that a guidance routine can guide the
chase vehicle to dock with the target
vehicle with the correct orientation.
BACKGROUND INTRODUCTION
Past efforts in the rendezvous and
docking area have been directed towards
remotely piloted man-in-the-loop
systems( OMV ) or man directed systems
( Orbiter ). These video or visual
based systems require a very large data
stream to the ground operator or a man
located on the rendezvous vehicle. To
efficiently operate unmanned vehicles,
minimize risk for hazardous or remote
operations and reduce workload,
automated rendezvous and docking
( ARAD ) techniques should be developed
that will have application to the Cargo
Transfer Vehicle( CTV ), the Mars/Lunar
Mission Vehicles and potentially the
Shuttle Orbiter.
Recently, docking systems have
been under investigation which use
transponders and reflectors on the
target vehicle and laser, radar and
various computer based sensors have
been used on the chaser vehicle for on-
board range, range rate, and attitude
determinations to support the ARAD
function. Much work in this field has
reached the level of real time system
simulations and sensor testing at
various NASA centers including the
Marshall Space Flight Center( MSFC )
and Johnson Space Flight Center( JSC ).
To define, develop, test, and evaluate
various ARAD systems, a coordinated
NASA effort is required. This effort
will produce an implementation of an
ARAD system that will have application
to the Cargo Transfer Vehicle( CTV ),
Lunar/Mars Mission vehicles, and remote
satellite servicing.
SYSTEM DESCRIPTION
There are five major components in
any docking autoguidance system. They
are: the autopilot, the control system,
the docking latches, the sensor, and
the docking target. The autopilot
generates vehicle commands from the
autoguidance sensor outputs. The
vehicle control system executes the
commands through control system
thrusters for a spacecraft or joint
motors of a robot arm. The docking
latches could be a three point docking
mechanism or a more complex mechanism
to lock the chase and target vehicles
together. This report concentrates only
on the autoguidance video sensor
development using simple retro-
reflectors for a target. This report is
divided into two sections: the present
system and new developments.
214
https://ntrs.nasa.gov/search.jsp?R=19930002766 2020-03-17T09:49:34+00:00Z
The video docking sensor concept
consists of five main components: laser
illuminators, retro-reflective targets,
a video camera, a frame grabber board,
and a microprocessor. The laser
illuminators are wide-angle laser
diodes at two different wavelengths,
780nm and 830nm. The diodes are
arranged such that they cover a 30-by-
30 degree field-of-view. The target is
composed of three circular pieces of
retro-reflective tape each covered by
830nm filters that are mounted on an
RMS target in a line with the center
reflector on the center pole and the
two outer reflectors equally spaced
from the pole. This configuration is
much more sensitive for yaw and pitch
at zero degrees than a four corner flat
target would be. The target is only
14.5 inches wide and the pole is four
inches high. The sensor takes a picture
with the 830nm laser diodes on and then
the sensor takes another picture with
the 780nm laser diodes on. The second
picture is subtracted from the first
and then a threshold is subtracted from
the resultant image to give the
reflector positions along with some
noise( see figure i: Range, azimuth,
and elevation can be converted to X, Y,
and Z ranges ).
83Ohm Laser
Diodes
1 sf Digitized
Picture
78Onto Laser
Diodes
2nd Digitized
Picture
Threshold subtracted
from differential image
Centroids found
for each spot
Tracking windows
established
DISPLACEMENT
X-Y-Z
Roll-Pitch-Yaw
Relative positions
and attitudes calculated
Position and
attitude information
sent to guidance
algorithm
Image 2 subtracted
from Image 1
Figure i: Steps to the Overall System
Operation.
215
The software consists of two
parts: initial target acquisition and
reflector tracking.
The initial acquisition loop has
three parts to it and they are: erosion
and dilation, picking out bright spots,
and identifying target reflectors out
of noise spots.
Erosion and dilation are simply
zeroing out the edge of all bright
spots and then recreating the edge on
all remaining bright spots. This
elliminates all single point noise
spots. Also, there is an edge created
by pipes on target satellites which
this elliminates along with any other
edge effect. The target spots can not
be too small or they will disappear.
There are two steps to picking out
the bright spots and they are: grouping
all adjacent pixels into spots, and
calculating the X and Y centroids of
each spot from the pixel data. The
sensor scans the pixel data from left
to right, top to bottom keeping track
of the X coordinate( horizontal ) and
the Y coordinate( vertical ) positions
at all times. When a pixel is found
with the intensity above the threshold,
all adjacent pixels are checked to see
if they are above the threshold. Then
adjacencies to that layer of pixels are
checked. All pixels above the threshold
are zeroed out in the image as they are
found so that they are not counted more
than once. Adjacencies are checked for
layer of pixels after layer of pixels
until the spot is completely zeroed out
of the image and stored with the X and
Y coordinates. Then the scan continues
where the first pixel for the spot was
found. The data, X coordinates, and Y
coordinates are then used to find the X
and Y centroids of each spot.
After the X and Y centroids for
all the spots are stored, the three
target reflectors need to be identified
from the noise spots. There has to be a
minimum of three spots to even start to
recognize the target. If there are
three or more spots, these spot
locations have to correspond to
possible yaw and pitch target
configurations to be identified as the
reflectors. There is a triple nested
loop for this section of code which
examines three spots at any one time
( any possible three spots is called a
triplet ). To reduce processing time,
only non-redundant triplets are
examined.
There are several steps inside the
innermost loop needed to see if a
triplet is the recognized reflector
target. The first step is to calculate
the length squared of each side of a
triangle where the corners correspond
to the triplet X and Y centroid
locations. The second step is to find
the base length or maximum length among
the three lengths, which is the
possible pixel length between the two
outer reflectors. If there are two
lengths of the same magnitude with one
length shorter than the first two or if
all three lengths are the same
magnitude then there is no base length
and the triplet is not the reflector
target. The third step is to calculate
the angle of the base length with
respect to the horizontal( roll angle
of possible reflector target ). The
fourth step is to use the roll angle to
calculate an X and Y coordinate axis
shift so that no matter how the target
is rolled, accurate yaw and pitch
target criteria can be used. These are
the axis tranformation equations:
........<: .: ...................:)Third..X C_{r old'F f tit X Cent told
[xclpz 90 4egrm, AII_ ¢_:old IPe lortl4.
Ce_ter_X- 0.5"{ O_t*r L*et_Spot_X_C_tro_
_ter IT_it_$po{ X E_troid }
Tr_slit I__X__hi f t = Cent ir X Sp_t_C_t roid- C_t e_ X
( Transmit f_____hirtlCo_( Rot[ ) )
The reason for the axis tranformation
is simple. There is a region centered
between the two outer spots that is
shaped like an hourglass with curved
sides. The curved sides are
approximated by four linear equations
( see figure 2 ). This region
corresponds to where the center spot
would be if it were within 40 degees in
yaw and pitch for the triplet to be the
reflector target. The transformations
are designed for the left-to-right
_ncreasing X coordinates and top-to-
bottom increasing Y coordinates. Range
is used as a target determining test
for a reacquire. The final test is to
compare the number of pixels in each
spot to each other and if the numbers
are close then accept the triplet as
the target.
216
T A_ls
\ \°
0 238"X )-( Y intercept*Pixel Length Of Target )
(1) Y = ( -0 238"X )-( Y intsrcept,PixeY_Length_Of_Target !
(2) Y ( -0"238,X )+( y-intercept*Pi×elLength_0f_TargeL, J
(3) Y ( 0 238"X )+( Y _ntercept*PixelLength_Of_Target J(4) Y (
(5) X = -X Intercep t*Pixel-Length-Of-Target
(6) X = X_Intercep t*Pixel-Length-Of-Target
X Intercept - 0.22375 for small target
¥_Intercept = 0.17141 Eor small target
Figure 2: Hourglass Shaped Region
Between Outer Spots.
Reflector tracking is done at two
times per second using one frame
grabber. First a frame is taken with
the 830nm laser diodes active. Next, a
frame is taken with the 780nm laser
diodes active. The frame grabber
subtracts the 780nm frame from the
830nm frame. Then the computer draws
windows around the three reflectors and
calculates the centroids of the
windows. Range is used to calculate
window size and window threshold( only
pixel intensities above the threshold
are included in the X and Y centroid
calculations ). The tracking itself
( where to place next windows ) is done
by subtracting the the present X and Y
centroids from the previous X and Y
centroids to get the distances that are
added to the present X and Y centroids
to predict where the next windows
should be. This works well for a
constant rotational or translational
velocity. At long range any chase
vehicle yaw or pitch can shift the
field-of-view enough for the reflectors
to slip out of the windows because of
the change in velocity. The same is
true for close range translations. To
counter this, translational and
rotational accelerations should be sent
from the control system Kalman filter
back to the sensor. Then the X and Y
centroids are used to calculate the
roll, yaw, pitch, range, azimuth, and
elevation ( see appendix ) which are
sent to the control system.
The method to calculate the
orientation information in the appendix
is to first calculate roll using the
equation from the acquisition section
of this report. Then calculate Max_
Length between the two outer
reflectors. Next, calculate the
Rotational_( X and Y )_Shifts using
equations from the acquisition section
of this report. After that, use that
information to calculate the yaw and
pitch simultaneously. Finally, use the
yaw to calculate range, azimuth, and
elevation. Yaw, pitch, range, azimuth,
and elevation all need to be calibrated
for each lens used in any video auto-
guidance system. Calibrated yaw should
be used to calculate pitch, range,
azimuth, and elevation. Calibrated
range should be used to calculate
azimuth and elevation.
PERFORMANCE/STATUS
The present system works fairly
well within twelve meters of the
docking target. With the new geometry
algorithm( see appendix ) the range
accuracy has been calibrated to one
percent or below. The yaw, pitch,
azimuth, and elevation have been
calibrated to below one degree in
accuracy. Dynamically, the system
performs docking maneuvers with a
reasonable degree of reliability using
the air-bearing vehicle on the MSFC
flatfloor facility, bringing the three
point docking mechanism into latch
position smoothly. Continuing testing
with the Dynamic Overhead Target
Simulator duplicating the full dynamic
motion of a free flyer( like CTV )
showed good correlation of sensor
outputs and realtime relative
positions. The autoguidance video
sensor will be used to measure and
guide the Space Station Freedom common
berthing mechanism test article during
dynamic berthing tests and to control
the latching sequence.
NEW DEVELOPMENTS/ENHANCEMENTS
The current sensor system has some
limitations and its performance can be
enhanced through some new hardware
improvements, some of which are now
commercially off the shelf. Other
changes will require some development
along with continued testing and
integration with more of the full
system.
To increase the range of the
system, two targets may be used. The
large target would be three corner cube
reflectors or three circular pieces of
microprism. Both of these reflector
217
types return a much larger signal to
the camera and the larger the target
the more the resolution at range.
Corner cubes have been used at 40
meters away. Corner cubes have one
problem and that is, a limited target
yaw or pitch angle before the spot size
is significantly reduced. Microprism
may overcome this problem. The small
target would be the present target used
now for close range because a large
target image would grow too large for
the camera. Using a two target system
could help determine roll ambiguity
because the offset between the two
targets is known. Otherwise one of the
outer reflectors would have to be
larger than the other to distinguish
between the two.
Another target concept is the
optical collector. The entire target
could be made into an optical collector
that brings all the light to a central
point where optical fibers send the
light back out to the three light
output positions( where the reflectors
were ). All three points would have the
same intensity no matter how close the
target is to the camera. There are no
glass filters to cause any starring
( glass reflects both wavelengths at
just the right incident angle causing
spot loss in the differenced image ).
The intensity would be far brighter
because all the light on the entire
target is being sent back at three
points instead of just the light
hitting the reflectors which means that
this target could be seen from a much
farther range.
In order to significantly improve
the speed of the sensor system to
fifteen readouts per second, two frame
grabbers that are pre-programmable are
required. In such a system, the two
frame grabbers would have the same
program loaded in before the docking
run by the 386-based computer. One
frame grabber grabs a frame with the
830nm laser diodes active. Then it
grabs a frame with the 780nm laser
diodes active. Next, it subtracts the
780nm frame from the 830nm frame( only
in the three reflector tracking
windows ). Finally, it centroids the
windows, sends the centroids to the
computer, and receives the next window
locations from the computer. The other
frame grabber follows the same program
steps. While one frame grabber is
grabbing two frames the other frame
grabber is performing the subtraction,
centroiding, and window updates. The
computer takes the centroids and
calculates the yaw, roll, pitch, range,
azimuth, and elevation and sends this
information to the guidance system. The
computer also controls the laser diodes
and calculates the windows to send to
the frame grabbers.
Improving the camera optics can
improve the performance of the system.
One improvement would be to control the
integration time of the camera CCD. In
sunlight the CCD may be saturated in
the brightest parts( noise spots and
reflectors ). In some instants the
noise from sunlight shining on mylar
may be much brighter than the
reflectors. A threshold above the
reflector brightness may be set up as
well as a threshold below the reflector
brightness. But if the camera is
saturated, this information is lost.
Another way of improving the
optics is a concept put forth by a
company called TRW. In this concept
there are two cameras one with an 830nm
optical filter covering and one with a
780nm optical filter covering. At the
entrance to the sensor there is a lens
and a beam spliter. Ringing the lens
are the output optical fibers. Each
input to these fibers is a laser diode
( 830nm or 780nm ). In this concept
both wavelengths of laser diode are
active at the same time. The returned
signal goes to both cameras through the
beam spliter at the same time( both
cameras and frame grabber are
synchronized to the same clock ). The
frame from the 780nm camera is
subtracted from the frame from the
830nm camera ( only in the reflector
readout windows ). The remaining steps
are just like the present system. In
this way image differences that
translate into noise on the resultant
image are completely absent. If a
camera malfunctions there is a backup.
Both cameras can have a filter with a
wide enough bandwidth to allow both
wavelengths through that can be snapped
into place where the tighter filter was
if there is a camera malfunction. Then
this system would operate just like the
present system. If this system were
implemented along with the two pre-
programmable frame grabbers then the
resultant system could possibly produce
a thirty readout per second data rate.
Another way to improve the sensor
system is to have more than one camera
each with a different lens and a
different set of laser diodes. Each
camera setup would be for a different
range. At short range a wide field-of-
view lens with laser diodes would be
used. At longer range a narrow field-
of-view lens with beam collimators to
narrow the laser wide beam half power
angle would be used with the laser
diodes. The chase spacecraft would
approach the target spacecraft with the
longer range camera setups switching to
the shorter range camera setups at the
switchover ranges. This would greatly
increase the range of the overall
218
system if implemented.
CONCLUSION
The current autoguidance video
sensor, by acquiring and tracking three
simple retroreflectors, provides
accurate range and angular measurements
for realtime docking of spacecraft. Any
number of combinations of the above ..................
improvements may be implemented in the
final system to create a highly
reliable docking system with a
reasonably good range from acquisition
to the final docking. Other systems
such as radar, laser rangers, GPS, etc
will add autonomous rendezvous
capability to the advanced docking
system to provide fully Automated
Rendezvous and Docking to support CTV,
Mars/Moon missions and remote satellite
servicing.
APPENDIX
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pth
219


Autoguidance video sensor for docking

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