Pictures by NASA, ESA, and The Hubble Heritage Team (STScI

Pictures by NASA, ESA, and The
Hubble Heritage Team (STScI/AURA)
1
2
3
Picture by NASA
4
Sensors for On-Orbit Docking
Stephen Granade
[email protected]
5
What Do You Need to Measure?
6
Make a Relative Measurement of Six-DoF
y
Orientation (roll, pitch, yaw)
y
yaw
roll
z
pitch
z
x
Position (x, y, z)
7
x
How Far Away is Hubble?
Picture by NASA
8
Ways of Measuring
Position and
Orientation
A Tale of Two
Approaches
9
Direct vs. Indirect Measurement
Direct
Indirect
Picture by Arild Storaas
10
Types of Direct and Indirect Measurement
 Direct
 Measure distance directly using light or radar pulses
 Indirect
 Measure x and y position by where the spacecraft is
in an image
 Measure distance (z) by looking at how big the
spacecraft appears in the image
 Measure angles (roll, pitch, yaw) by looking at the
spacecraft’s shape or the relative location of points
on the spacecraft
11
Direct: Radar or Laser Rangefinding
y
z
0.3 m/ns
12
x
Indirect: Use a Camera to Estimate 6DoF
Position: x, y
Original pictures by NASA
Size: z (distance)
13
Shapes or Points:
Orientation
Look At How Shapes Are Distorted to Get Angles
14
Apparent Distortion is Related to Angles
15
Measure Relative Location of Known Features
16
Measure Relative Location of Known Features
1
3
1
4
5
2
4
2
17
3
5
A Camera Makes a 2D Projection of a 3D Scene
Field of View
Lens
Focal Point
Imager (Focal Plane Array)
Warning: Not to scale
18
Pixels Correspond To Angular Location
Lens
Imager (Focal Plane Array)
19
Perspective Lets You Calculate Angles
Spacecraft
Spacecraft
Camera
Camera
20
Shuttle Dockings: Direct & Indirect Measurements
Pictures by NASA
21
A Closer Look at the ISS Target
Picture by Advanced Optical Systems
22
Locating
Features on
Spacecraft
A Tale of Two Types of
Spacecraft
23
Cooperative vs. Uncooperative Spacecraft
Uncooperative
Pictures by NASA
Cooperative
24
Cooperative Targets Are (Relatively) Easy
Pictures by DARPA (left) and AOS (right)
Hubble’s Soft Capture Mechanism Included Targets
Pictures by NASA and AOS (inset picture)
26
Uncooperative Targets Require Image Recognition
AOS Space Vision
Tracking System
Boeing Vis-STAR
27
What Makes For Good Features?
 Individual feature’s
sizes
 Distribution of features
 Widely-spaced side to
side
 Spaced front to back
 Contrast
 Spatially distinctive
 Corners, edges, color
changes
28
Choose Your Sensors to Match Your Targets
 Sensors can make direct or indirect
measurements
 Get distance directly using rangefinding with light or
radar
 Get distance indirectly by looking at size of objects
on an image or distance between points on a
spacecraft
 Get orientation indirectly by looking at spacecraft
shape or relative configuration of features (points) on
the spacecraft
 Targets can be cooperative or uncooperative
 Cooperative spacecraft have distinctive features that
are easy to find
 Uncooperative ones require image processing
techniques to recognize features
29
Docking Sensors
That Have Flown
30
30
IGLA and KURS
 Russian system
 Multiple radar antennas
 Multi-stage process
 Acquisition: target
spacecraft broadcasts
beacon signal
 Tracking: target spacecraft
re-broadcasts what the
Progress sends
 Works over tens of km
 Most flown relative
navigation system
Pictures by NASA
31
Advanced Video Guidance Sensor (AVGS)
 NASA/AOS/Orbital
Sciences system
 Laser-based
 Camera images spots of
light reflected from targets
 Processes spot locations to
determine target’s position
and orientation
 Works to 1 km / 300 m
 Flown on DART and Orbital
Express missions
Pictures by Orbital Sciences/AOS (top),
AOS (inset), and NASA (bottom)
32
ARCSS
 Boeing system
 Visible-light and IR
cameras
 Images (including
silhouette and edgeenhanced) correlated
against library images
 Uses features at short
ranges
 Works to 200 km / 60 m
 Flown on Orbital Express
Pictures by Boeing
33
Relative Navigation Sensor System
 NASA and AOS system
 Three visible-light cameras
 Matched features in edgeenhanced image against a
model (NASA GNIFR) or
correlated features against
a library (AOS ULTOR)
 Worked to 150 m
 Flown on Hubble Servicing
Mission 4
Pictures by NASA (top and
bottom left) and AOS (bottom right)
34
TriDAR
 Neptec system
 Laser rangefinding, laser
triangulation, and an IR
camera
 3D point cloud compared
to models
 Uses IR camera for long
ranges
 Works to ~40 km / 400 m
 Flown on three Shuttle
missions and Cygnus
Pictures by NASA (top) and Neptec (bottom)
35
Vision Navigation Sensor (VNS)
 NASA/Ball/Lockheed
Martin system
 Flash LIDAR and visiblelight camera
 Range measurements to
reflective targets
 Visible-light camera for
situational awareness
 Works to 200 km / 60 m
 Flown on a Shuttle mission
Pictures by Ball Aerospace
36
Why So Many? Because There’s No “Best” Solution
 Cooperative gives you the most control but
requires modifying the spacecraft you want to
dock with
 Uncooperative is the most flexible, but much
harder to get right
 There is no one image processing solution that works
in all situations
 Because space is a low-volume business, we’re
not seeing big jumps in technology powered by
commercial demand
37
Note: Autonomous On-Orbit Docking is Hard
 IGLA: Three failures on orbit, one due to a trash
bag
 KURS: Multiple failures, including one that led to
a Progress hitting Mir, and another that left a
Progress adrift near the ISS
 DART: Failed to turn on AVGS, and hit MUBLCOM
 Orbital Express: Had to adjust ARCSS object
recognition settings on orbit
 Orbital Express and Japan’s ETS-VII: Target
satellite got lost and had to be recovered
 Hubble RNS: GNIFR tracked only at first, ULTOR
not at all while on orbit
38
Where Next?
 ISS resupply craft are using docking sensors
 Progress-M: Kurs
 ESA ATV and JAXA HTV: Scanning LIDAR looks for
reflections from targets
 Orbital Express Cygnus: TriDAR
 SpaceX Dragon: developing the DragonEye flash LIDAR
 Orion’s Vision Navigation Sensor is still in the wings
 GSFC RAVEN
 DARPA Phoenix program for satellite servicing
 A few commercial efforts
39
Sensors for On-Orbit Docking
Stephen Granade
[email protected]
40