# Progress for week 42 (2018)

### From Robin

## Contents |

## 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

### Accounting

- Dense neural networks and convolutional neural networks are trained with various parameters
- Results are OK
- Need huge amount of trainable parameters to get OK results using Dense neural networks (ca. 33 times more compared to convolutional)

## 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.

## 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.

## Malin Aandahl

### Budget

- Get Gazebo working
- Make first experiment
- Budget for next week is to either get something up and running on my own computer(get Linux) or a computer from Robin

### Accounting

- Lot of debugging to try to get something working - but failed.
- Had a meeting with Kyrre