Progress for week 49 (2018)

From Robin

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* Cleaned up in code for better overview, and making it easier to simulate.
* Cleaned up in code for better overview, and making it easier to simulate.
* Created new value function wich I will use to evaluate the methods.
* Created new value function wich I will use to evaluate the methods.
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* Updated the objective funciton in the mathematical optimization method
+
* Updated the objective funciton in the mathematical optimization method.

Current revision as of 11:34, 7 December 2018

Contents

David Kolden

Budget

  • Get an overview of already implemented solutions.
  • Implement FDCM.
  • Write background of FDCM.
  • Play around.

Accounting

  • FDCM with D2CO now finds pose of an object.
  • Created simulation model of gripper.
  • Created simulation model of wrist force/torque sensor, but will not simulate its functionality because this is poorly supported by ROS at the moment.
  • Wrote some background
  • General system clean up


Malin Aandahl

Budget

  • Implement new fitness function.
  • Find out where the position to the robot is calculated(and change to be the back instead of the front of the robot)
  • Finish tutorials about Docker.
  • If time set up Docker or implement function to find position.

Accounting

  • Worked more with Docker. Currently working on finding environmental variables and putting them in the Dockerfile.
  • Made new fitness function, but something might be wrong.
  • (Reading to my exam)

Jonas S. Waaler

Budget

  • Finish implementing the new baselines to the experiments
  • Tune data to fit the classification task (experiment 2)
  • Construct algorithms/architectures to solve the classification task


Anders Rønningstad

Budget

  • General code cleanup (Structure change)
  • Find and implement better value functions for the optimization methods.
  • Start looking at swarm optimization

Accounting

  • Cleaned up in code for better overview, and making it easier to simulate.
  • Created new value function wich I will use to evaluate the methods.
  • Updated the objective funciton in the mathematical optimization method.
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