Adrian cooke Presentation Mechatronics 2012

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Agile software development methods are used extensively in the software industry. This paper describes an argument to explain why these methods can be used within a multi-disciplinary project and provides a concrete description on how to implement such a method, using a case-study to support the rationale. The SOFIE (Intelligent Assisted Bicycle) project was created to develop mechatronic appliances to make bicycles more stable, i.e. safer. A bicycle stability test bench is created within this project and is used as the case study for this research. The relative complexity of the test bench development and partner structure within the SOFIE project has many similarities with large-scale complex projects found in industry. Thus it provides a good environment to research the application of Agile software methods to a multi-disciplinary project.

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Adrian cooke Presentation Mechatronics 2012

  1. 1. IntelligentAssisted Bicycles Agile development for a multi- disciplinary bicycle stability test bench Adrian Cooke(Presenter) September 24, 2012 G.M Bonnema, Wim Poelman Funded by PIDON (Overijssel, The Netherlands)
  2. 2. OverviewIntroductionSOFIEAgile SCRUMSCRUM MILESTONES* Case studyConclusion Multi-disciplinary Agile: Adrian Cooke 2 / 20
  3. 3. Agile Evolution 1. Lean and Agile Manufacturing Inspiration for Agile Software: -KANBAN -Pull-type systems 3 / 20
  4. 4. Agile Evolution3. Agile Software Methods Adaptations -Concrete guidelines created. -Advanced IT tools developed.2. Adapted to software. 1. Lean and Agile Manufacturing Inspiration for Agile Software: -KANBAN -Pull-type systems 3 / 20
  5. 5. Agile Evolution3. Agile Software Methods 4. Adapted Adaptations back to -Concrete guidelines created. multi- -Advanced IT tools developed. disciplinary systems.2. Adapted to software. 1. Lean and Agile Manufacturing Inspiration for Agile Software: -KANBAN -Pull-type systems 3 / 20
  6. 6. Agile Evolution3. Agile Software describesThis Presentation MethodsAgile in brief and provides a 4. Adapted Adaptations to apply the method back to Adaptation. -Concrete guidelines created. multi- -Advanced IT tools developed. disciplinary systems.2. Adapted to software. 1. Lean and Agile Manufacturing Inspiration for Agile Software: -KANBAN -Pull-type systems 3 / 20
  7. 7. OverviewIntroductionSOFIEAgile SCRUMSCRUM MILESTONES* Case studyConclusion Multi-disciplinary Agile: Adrian Cooke 4 / 20
  8. 8. Computer Model Experiments 14 13 11 10 12 16 2 2 15 4 4z 9 8 y 1 rx 6 3 7 1 5 5 z 3 y w c x 5 / 20
  9. 9. Computer Model Bicycle Stability Experiments Test Bench 14 13 11 10 12 16 2 2 15 4 4z 9 8 y 1 rx 6 3 7 1 5 5 z 3 y w c x 5 / 20
  10. 10. Computer Model Bicycle Stability Experiments Test Bench 14 13 11 10 12 16 2 2 15 4 4z 9 8 y 1 rx 6 3 7 1 5 5 z 3 y w c x 5 / 20
  11. 11. Computer Model Characteristics Bicycle Stability Experiments- concurrent engineering Test Bench- non-trivial technical 14 13 project 11 10 12 16 2 2- multi-disciplinary 15 4 4 z 9 8 y 1 r x 1- dispersed team 7 6 3 5 5 z 3 y w c x 5 / 20
  12. 12. Computer Model Characteristics Bicycle Stability Large-scale projects Experiments- concurrent engineering - Get Test Bench the points from- non-trivial technical 13 14 the paper. project 11 10 12 16 2 2- multi-disciplinary 15 4 4 z 9 8 y 1 r x 1- dispersed team 7 6 3 5 5 z 3 y w c x 5 / 20
  13. 13. Bicycle Stability Large-scale projects Experiments Computer Model Characteristics Comparable- concurrent engineering - Get Test Bench the points from- non-trivial technical 13 14 the paper. to large- project scale 11 10 12 16 2 2- multi-disciplinary 15 4 4 z 9 8 projects y 1 r x 1- dispersed team 7 6 3 5 5 z 3 y w c x 5 / 20
  14. 14. OverviewIntroductionSOFIEAgile SCRUMSCRUM MILESTONES* Case studyConclusion Multi-disciplinary Agile: Adrian Cooke 6 / 20
  15. 15. Agile Background The Agile software methods were designed to respond to changing environments and user requirements and they are a response to the heavy traditional software development methodologies. ◮ Just-In-Time planning. ◮ Rapid development iterations with working code (designs). ◮ Strong stakeholder interaction through demonstrations. ◮ Extreme Programming (best practices) and SCRUM. Multi-disciplinary Agile: Adrian Cooke 7 / 20
  16. 16. XP-Extreme programming ◮ The planning game: Plan before each release. ◮ Small releases: Useful releases. ◮ Metaphor: Coherent story wherein everyone can work. ◮ Simple design: Pass tests with no duplication. ◮ Testing: All codes should be unit tested. ◮ Refractoring: Reorganising the code/design. ◮ Pair programming: One computer two people. ◮ Collective ownership: Change partners and tasks. ◮ Continuous integration: Test all the time. ◮ Coding standards: Easier to understand. Multi-disciplinary Agile: Adrian Cooke 8 / 20
  17. 17. OverviewIntroductionSOFIEAgile SCRUMSCRUM MILESTONES* Case studyConclusion Multi-disciplinary Agile: Adrian Cooke 9 / 20
  18. 18. SCRUM PRODUCT BACKLOGFeatures / Tasks 10 / 20
  19. 19. SCRUM PRODUCT BACKLOGFeatures / Tasks SPRINT 10 / 20
  20. 20. SCRUM PRODUCT BACKLOG Features / Tasks SPRINTDEMONSTRATION 10 / 20
  21. 21. SCRUM PRODUCT BACKLOG Features / Tasks SPRINT DAILYSCRUMDEMONSTRATION 10 / 20
  22. 22. SCRUM PRODUCT BACKLOG Features / Tasks SPRINT DAILYSCRUM REVIEWDEMONSTRATION 10 / 20
  23. 23. OverviewIntroductionSOFIEAgile SCRUMSCRUM MILESTONES* Case studyConclusion Multi-disciplinary Agile: Adrian Cooke 11 / 20
  24. 24. SCRUM Milestones PB MINI DEMO SCRUM SPRINT 12 / 20
  25. 25. SCRUM Milestones PB MINI DEMO SCRUM SPRINT REVIEW PB MINI DEMO SCRUM REVIEW SPRINTMILESTONE 12 / 20
  26. 26. SCRUM Milestones PB MINI DEMO SCRUM SPRINT REVIEW PB MINI DEMO SCRUM REVIEW SPRINTMILESTONE PB MINI DEMO Scrum SPRINT REVIEW 12 / 20
  27. 27. OverviewIntroductionSOFIEAgile SCRUMSCRUM MILESTONES* Case studyConclusion Multi-disciplinary Agile: Adrian Cooke 13 / 20
  28. 28. Case Study - Data logger casingProblem definition: Design and build a casing for theSparkfun Logomatic v2 Serial SD Datalogger (Open sourceand available at http://mobilitylabtwente.nl/sofie). Multi-disciplinary Agile: Adrian Cooke 14 / 20
  29. 29. Case study MILESTONE 15 / 20
  30. 30. Case study MILESTONE DEMONSTRATION 15 / 20
  31. 31. Case study MILESTONE DEMONSTRATION 15 / 20
  32. 32. Case study MILESTONE REVIEW DEMONSTRATION 15 / 20
  33. 33. 16 / 20
  34. 34. OverviewIntroductionSOFIEAgile SCRUMSCRUM MILESTONES* Case studyConclusion Multi-disciplinary Agile: Adrian Cooke 17 / 20
  35. 35. Conclusion and Future work ◮ SOFIE Project has been described. ◮ Brief description of Agile given. ◮ Agile used extensively in embedded systems development. ◮ Case study shown and Agile continuously used within the SOFIE project. ◮ XP provides relevant best practices for multi-disciplinary projects. ◮ Agile is a different paradigm for project development. ◮ My question is: How do I develop this research to make Agile more useful for multi-disciplinary projects? Multi-disciplinary Agile: Adrian Cooke 18 / 20
  36. 36. IntelligentAssisted Bicycles Thank you for your at- tention. Adrian Cooke http://mobilitylabtwente.nl/sofie
  37. 37. Bicycle Stability Test Bench 20 / 20
  38. 38. Bicycle Stability Test Bench1 Dataprocessingback-bone 20 / 20
  39. 39. Bicycle Stability Test Bench 1 Data processing back-bone2Forward velocity and pedalling cadence Why? bicycle dynamics Technology commercial sensors, signal processing. 20 / 20
  40. 40. Bicycle Stability Test Bench 1 Data processing back-bone 3 Lean and steering angle2Forward velocity and Why? pedalling cadence Why? bicycle dynamics bicycle dynamics Technology Technology sensor development, 3D commercial sensors, mathematics, mechanical signal processing. mounts, data-logging 20 / 20
  41. 41. Bicycle Stability Test Bench 4 Rider behaviour and kinematics Why? rider behaviour and dynamics Technology 3D mathematics, sensor development, rider perception analysis 1 Data processing back-bone 3 Lean and steering angle2Forward velocity and Why? pedalling cadence Why? bicycle dynamics bicycle dynamics Technology Technology sensor development, 3D commercial sensors, mathematics, mechanical signal processing. mounts, data-logging 20 / 20
  42. 42. Bicycle Stability Test Bench5 Rider bicycle interfaces and 4 Rider behaviour and rider dynamics kinematics Why? Why? rider control actions for rider behaviour and computer model dynamics Technology Technology sensor development, mechanical 3D mathematics, sensor mounts, signal processing. development, rider perception analysis 1 Data processing back-bone 3 Lean and steering angle2Forward velocity and Why? pedalling cadence Why? bicycle dynamics bicycle dynamics Technology Technology sensor development, 3D commercial sensors, mathematics, mechanical signal processing. mounts, data-logging 20 / 20

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