# Progress for week 42 (2018)

(Difference between revisions)
 Revision as of 23:06, 18 October 2018 (view source)Davidko (Talk | contribs) (→Accounting)← Older edit Revision as of 07:08, 19 October 2018 (view source)Anderon (Talk | contribs) Newer edit → Line 20: Line 20: ** Found out you can calculate inertia tensors with MeshLab ** Found out you can calculate inertia tensors with MeshLab * Created CameraObjectLocalizer class, but have not compiled with D2CO yet. * Created CameraObjectLocalizer class, but have not compiled with D2CO yet. + + + == Anders Rønningstad == + === Budget === + * Read about and understand how to use mathematical optimization packages in python. + * Implement mathematical optimization in program. + ** Hopefully start to run tests. + + === Accounting === + * Found out how to implement the optimization. + ** Ran some tests, with bad results. + * Tried to implement the optimization using bruteforce, but computation was to slow for it to be useful. + * Started to implement a self made method, which combines bruteforce and some assumptions. + ** Ran som tests using a simple version of the new method, with some better results.

## Jonas S. Waaler

### Budget

• Continue training dense neural networks of different sizes
• Develop convolutional neural networks and train
• Compare dense and convolutional, and write about it

## David Kolden

### Budget

• Implement D2CO algorithm in a ROS node.
• Create Gazebo simulation model of a camera.
• Find a way to create a Gazebo model of a part that should be registered by the algorithm.
• Find a way to calculate inertia from a CAD file.
• Finish introduction of the essay.
• Finish the robotic assembly section of essay.

### Accounting

• Personal page created.
• Camera model implemented in ROS/Gazebo
• Imported a part object into ROS/Gazebo from CAD file.
• Found out you can calculate inertia tensors with MeshLab
• Created CameraObjectLocalizer class, but have not compiled with D2CO yet.

### Budget

• Read about and understand how to use mathematical optimization packages in python.
• Implement mathematical optimization in program.
• Hopefully start to run tests.

### Accounting

• Found out how to implement the optimization.
• Ran some tests, with bad results.
• Tried to implement the optimization using bruteforce, but computation was to slow for it to be useful.
• Started to implement a self made method, which combines bruteforce and some assumptions.
• Ran som tests using a simple version of the new method, with some better results.