This presentation shows how Klepsydra (www.klepsydra.com) can increase up to 20% data processing in Space on-board computers with limited resources, like those for CubeSats. Not only that, Klepsydra can also substantially increase determinism for Space applications.

1. ONBOARD HIGH RATE
DATA PROCESSING
USING CANOPEN
PROTOCOL
pablo.ghiglino@klepsydra.org
www.klepsydra.com

2. 01:
Introduction
•Introduction
•Use case
•ROS Benchmark
•Klepsydra Space
•The product
•The company
•Conclusions

3. Embedded systems
today
Embedded autonomous systems is a fast
growing market with as fast growing challenges:
• Increasing demand in complexity:
• Large data processing on-board (sensor data,
communications with other systems, etc)
• Heavy algorithms: filtering, FDIR, optimal
control, vision navigation, etc.
• Hardware has improved substantially:
• Better on-board computers
• Improved sensors (more and faster data)

4. Software fo
embedded systems
Software challenges:
• Lower priority.
• Lack of modern techniques.
• Lack of highly skilled software
engineering
• This translate into:
• Error prompt software
• Limited functionality
• Delays in delivery
• Suboptimal resource utilisation

5. Parallel data
processing
• The majority of embedded software problems are related to data processing.
•The financial sector has known this for a long time! This is where
Klepsydra come into place

6. High Frequency Trading
• Thousands of transactions per
second.
• Require access to real time
data.
• Delays in processing might
result in millions in loses.
Innovation

7. 02:
Use case
•Introduction
•Use case
•ROS Benchmark
•Klepsydra Space
•The product
•The company
•Conclusions

8. Use case: windmill inspections
Client Requirements
•Onboard capture, video-stream, process and
record high resolution images at 20 frames per
second(FPS).
•Fly as fast and close as possible to the blades of
the windmill and do it very robustly.
•Vision-based navigation.
Situation before Klepsydra
•Only low resolution images and low FPS.
•Error prone and slow and inaccurate navigation.

9. Event Loop
Sensor Multiplexer
Two main architectural
approaches
Sensor
Consumer 1 Consumer 2
Sensor 2
Sensor 3
Processor
Sensor 1

10. CAPTURE IMAGE
PROCESSING
FILTERING IMAGE
PROCESSING
(Slam, robotics)
LIVE
STREAM
IMAGE
COMPRESSION
(Low priority)
1 Camera,
3 real-time uses
• One big producer of data.
Usually large data (image,
point cloud, etc)
• Several independent
consumers.
• Each can have different
rates.

11. Use case: the solution
With Klepsydra:
High resolution camera
Without Klepsydra:
Low resolution camera
High CPU Consumption
Low CPU Consumption

12. Event Loop
Sensor Multiplexer
Two main architectural
approaches
Sensor
Consumer 1 Consumer 2
Sensor 2
Sensor 3
Processor
Sensor 1

13. Conclusion
Now Alerion, offers the most advanced
autonomous drones for windmill inspections. They
produce the highest resolution images and
closest captured in the market.
This extremely complex application was much
easier with Klepsydra solutions.
They are now the official inspection provider for
Siemens-Gamesa. Second biggest windmill
manufacturer in the world with 15’000 windmills
worldwide.

14. 03:
ROS Benchmark
•Introduction
•Use case
•ROS Benchmark
•Klepsydra Space
•The product
•The company
•Conclusions

15. The Robot
Operating System
• Benchmarking for ROS and
ROS with Klepsydra
• Test for high rate sensor
data as per expected in the
vision based navigation.
• ROS is an asynchronous
middleware.

16. ROS in Space
• Used by Universities for simulation of Space
scenarios and applications
• Used by companies and organisation to
simulate space robotics scenarios

17. Parallel data
processing solutions
Middleware
Single Thread Spin
Application
Middleware
Multi Thread Spin
Application +
concurrency contention
Middleware
Multi Thread Spin
Application
Klepsydra
Single Thread Solution Multi Thread Solution
Klepsydra Solution

18. Benchmarking
SENSOR FUSION BENCHMARKING APPLICATION
Server Application
Producer 2
Producer 8
Client Application
Consumer 2
Consumer 8
Producer 1 Consumer 1

19. Processing Rate Comparison
SensorDataProcessingRate
(Hz)
750
1'200
1'650
2'100
2'550
3'000
Sensor Data Production Rate (Hz)
750 1'313 1'875 2'438 3'000
ROS ST ROS MT Klepsydra + ROS
• Pure ROS starts
loosing data at 1KHz.
• Klepsydra does not
loose any data and
remains close to ideal
curve.
DATA
LOSS

20. Conclusion
+ =

21. Contents
Klepsydra Space
•Introduction
•Use case
•ROS Benchmark
•Klepsydra Space
•The product
•The company
•Conclusions

22. Space systems today
In Space applications there is a growing need of
high rate data processing:
• Constellations for:
• Fast communications (LeoSat),
• Internet providers (OneWeb), or
• IoT (LaserLight/Xenesis)
1000’s requests per second from Earth, plus routing
and position coordination. etc.
• Space robotics, GNC, rendezvous, planetary
landing.
These systems need to process data as fast as
possible in order to perform the mission correctly.

23. Sources of data
events
NETWORK
Application
SENSORS RADIO EARTH COMMS
Internal: FPGA,
FDIR, OS, etc.

24. Benchmarking
SENSOR FUSION BENCHMARKING APPLICATION
Server Application
Producer 2
Producer 8
Client Application
Consumer 2
Consumer 8
Producer 1 Consumer 1
• Combination of: SDO,
PDO and SDO block
communications.
• Two implementations:
Klepsydra and
traditional Multi-
thread
• Variable 5-20 parallel
processes
• Tested in the
Zedboard..

25. OpenMCT
Performance Monitoring
• Telemetry monitoring
tool developed by
NASA.
• Used in real missions.
• Web-based,
lightweight.
• Klepsydra uses for
performance
monitoring

26. Benchmarking
Data Processing Rate Comparison
DataProcessingRate(Hz)
2300
2400
2500
2600
2700
2800
2900
3000
3100
Time (s)
0 34 66 98 126 150 184 212 238 260 290 316 354
Without Klepsydra (Multithread) With Klepsydra
• At 3KHz, Klepsydra is
at the ideal point
• Traditional methods
degrades 15%
• Klepsydra has
negligible variability,
while traditional
approach does not.

27. CPU Usage Comparison
ProcessCPU(%)
58
63
68
73
78
83
Data Event Rate (Hz)
280 784 1288 1792 2296 2800
Multi THread Klepsydra
Up to 10% less
CPU Usage
•Better CPU usage
from the beginning.
•For the same rate of
data processing, the
gain is much bigger
(15-20%)

28. Data Processing Rate
Comparison
ProcessingRate(Hz)
700.00
1120.00
1540.00
1960.00
2380.00
2800.00
Data Event Rate (Hz)
700 1120 1540 1960 2380 2800
Multi THread Klepsydra
15% event
lost without
Klepsydra
•At 700Hz the
difference become
noticeable
•Klepsydra remains
close to the ideal
processing curve.

29. Variability of processingStandarDev(%)
0.00
4.60
9.20
13.80
18.40
23.00
Data Event Rate (Hz)
280 784 1288 1792 2296 2800
Multi THread Klepsydra
•Klepsydra’s std dev in
processing is constant
•Means predictable
system, which in turn
means more reliability.

30. • Negligible latency
• ‘Absorbs’ parallel
processing complexity
and therefore, makes
software development
much easier
• Predictable, faster and
reliable behaviour
•No data losses!!!
Embedded
Application
EVENT
LOOP
Conclusion

31. Conclusion
Processing faster and more data on-board has the following benefits:
• Constellations
• Process more requests from Earth (= $$$)
• Process more constellation position/coordination data (= larger
constellations, and longer service life)
• Low latency processing (= faster Earth-to-Earth communications)
• Space robotics, GNC, rendezvous, planetary landing.
• Low latency processing (faster response to sensor data)
• Process more sensor data (higher accuracy of GNC)
• Predictability (increase reliability in any conditions)
Increase economical benefit and chances of mission success!!

32. Our Vision
ESA OBDP Workshop 2019. Talk by Cornelius J. Dennehy from
NASA about GN&C software:
“There is a critical need to modernise on-board computing
capabilities to support higher levels of GN&C Autonomy. In our
view improved spaceflight computing means not only enhanced
computational performance, energy efficiency, fault tolerance,
but also ease of programming, affordability, reconfigurability
and availability”
This is exactly in line with out vision and philosophy!!!

33. Contents
The product
•Introduction
•Use case
•ROS Benchmark
•Klepsydra Space
•The product
•The company
•Conclusions

34. Technical description
DevelopmentTools
Matlab
Integration
CORE
High performance module
Robotics / Aerospace
Add-ons
Image data
processing
Autopilots
Integration
Space Add-ons
Space comms
connectors
On-board
computer
optimisation
Real time
operating
Systems
Robotics
Framework
integration
AutoCoding
• ‘Miniaturisation’ of High frequency
trading techniques.
• General purpose library for
embedded systems: Robotics,
Space, Aerospace, IoT and Self-
driving cars.
• Platform independent.

35. Development tools
Run it in most Linux and RTOS
distributions. Integrate our product
with add-ons for CAN and FPGA
boards.
Adoption and integration into existing
applications with very small effort.
Efficient and reliable autocoding
code without manual
modifications.
Accelerate development with our
enhanced processor-in-the-loop
simulations.
MATLAB/Simulink Direct programming

36. Contents
The company
•Introduction
•Use case
•ROS Benchmark
•Klepsydra Space
•The product
•The company
•Conclusions

37. Team
•Dr. Pablo Ghiglino. Founder and CEO.
•Isabel del Castillo. Admin management
•Dr. Mandar Harshe. Senior C++ developer.
•Franco Bugnano. Part-time software developer (space)
•Dr. Francisco Sanchez. Part-time C++ developer (core and
aerospace)
•Javier Aldazabal. Part-time C++ developer (aerospace)
•Hervé Flutto. Business development Europe.

38. Semi-finalist in the ESA
Space Masters,
Luxembourg 2017
Finalist in the Digital Energy
Forum, Munich. 2017
Finalist in the BSH
Venture Forum,
Munich 2017
Selected by ESA to be
one of the top space
startups to present at
IAC 2018.
Awards
Finalist in the NASA iTech
Colorado Spring 2019

39. Contents
The company
• Summary
• The product
• High performance
• Sensor fusion
• Agile development
• Competitors
• The company

40. Next steps
• We are currently doing projects with companies in the
‘NewSpace’ sector and Universities.
• Klepsydra Space is available for trial:
• Klepsydra + CANOpen add-on.
• MATLAB add-on for beta testing.

41. Q & A
Thanks
pablo.ghiglino@klepsydra.org
+41786931544
www.klepsydra.com
linkedin.com/company/klepsydra-technologies