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Klepsydra Technical Presentation


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Presentation about Klepsydra technical and business details

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Klepsydra Technical Presentation

  1. 1. Context
  2. 2. Spin-off of Alerion Technologies
  3. 3. Error fixing costs • Cost of fixing software errors grows exponentially with time. • In the case of robotics software the curve is even more steep.
  4. 4. Multiple Middleware
  5. 5. Embedded Image processing
  6. 6. Our Solution
  7. 7. Concept • Transparent to applications • No connection to the middleware from application Klepsydra Abstraction Layer Application 1 Application 2
  8. 8. Innovation of Klepsydra
  9. 9. Result • Klepsydra ‘absorbs’ and delivers all messages from middleware. • Performance can significantly increase Klepsydra Abstraction Layer Application 1 Application 2 Blocking queues Disruptor
  10. 10. One step further: unit testing framework Mock Middleware Klepsydra Abstraction Layer Application 1
  11. 11. Code Generation Tool • Generates POCOs (Plan Old C++ Object, which are classes with no dependencies to any library) to be used in Klepsydra • Generates the mapping classes between Klepsydra and all middleware (ROS, DDS, ZMQ)
  12. 12. Performance Monitoring Tools Klepsydra Abstraction Layer Application 1 Application 2 Telemetry UI Statistics Monitoring of publishers, subscribers and services REST API Monitoring
  13. 13. How does it work
  14. 14. The Application API • Typical async API: • publish, registerListener, etc. • Plus some ‘sugar’ • registerOnce, callback, etc • And some performance • publishNoCopy, Process and forward, etc. Klepsydra Abstraction Layer Application 1
  15. 15. Assembling the Application Kl. Abs. Lay.Klepsydra Abstraction Layer Application 1 Application 2 Application 4Application 3 Blocking queues Kl. Abs. Lay. Disruptor
  16. 16. The assembly API • Creation wizard for • eventloop, blocking queues, disruptor • Including performance param like object pool, etc. • Connection wizard to middleware: • ROS, DDS, ZMQ • Including performance param like object pool, etc. • Also, assembly with event loop, blocking queues and disruptor
  17. 17. The benefits
  18. 18. •Faster and higher resolution image processing. •Requirements for hardware are less demanding as resources are optimised. •Hardware costs are then reduced as well. •Longer life of satellites and space robots 1. Performance On-board Hardware resources High performance tuning & Image processing module + = On-board Hardware resources
  19. 19. Example Before Klepsydra: Low resolution camera After Klepsydra: high resolution camera Low resource consumption High resource consumption
  20. 20. Performance Monitoring in Real Time
  21. 21. • The code is thoroughly tested and has higher quality. • Less maintenance costs and • Safer and reliable continuous deployments. 2. Testing and CI Source code Target
  22. 22. Example • Real example of Gamesa/Siemens Drones • 4 Testing Phases • With this approach, more than 95% of bugs are found before reaching production. 1. Unit Tests 2. Integration Tests 3. Simulation 4. Real scenario Tests
  23. 23. Conclusions The adoption of Klepsydra as development framework for embedded software leads not only to high performance and quality, but more importantly to system predictability which a key feature in robotics and aerospace sectors. High performance Predictablity +
  24. 24. The Market
  25. 25. Market of complex embedded software Image processing High performance Application Integration Machine Learning
  26. 26. Sectors Targeted by Klepsydra Aerospace Autonomous Cars Defence Drones Robotics
  27. 27. Licensing Model
  28. 28. Product Licensing Model The business model is 'Freemium' based: • Free version: free for prototype and non- commercial development, research centres and universities. • Reduced pricing model for startups. • Commercial version: development license based on number of developer stations.
  29. 29. Klepsydra Future
  30. 30. Future features Distributed computing Telemetry UI Klepsydra Core API Cloud Computing Administration REST API Real-time OSBinding to High performance Middleware Basic Middleware Image processing performance module
  31. 31. Thanks! Q & A