Joseph:
This week I was able to connect make a connection between the UI and the robot. I am able to send coordinates to the raspberry pi via a script that sends commands through ssh. I run the movement script with coordinates as the inputs and the bot is capable of moving.
Next week I hope to have some form of picture transfer from the robot to the laptop working. I will also work with Karen to fine tune movements as well as installing the ultrasonic sensors for obstacle avoidance.
Karen:
This week was mostly spent fine tuning the movement of the bot again. I have noticed that there seems to be an accumulation of error in the angle when we use the turn_angle function from the pycreate module. Because of this, usually our first two coordinates are accurate, but when the bot re-orients itself, it seems to overshoot. The offset is not consistent so we cannot adjust accordingly. I am now thinking of creating my own turn_angle function that will instead use time to control the angle of rotation. Hopefully this will give us more accurate results. However, because we have given ourselves a margin for error in the accuracy of movement, I do not anticipate this being an incredible issue. I have also basically finalized my portion of the movement script with localization and will hand it off to Joseph for him to incorporate sensors.
This week we will be doing our demo. By Wednesday I hope to have the UI completely connected to our script so that when points are clicked on the GUI, this transfers to the command line and runs the movement script. Then, the bot will go to those points and rotate in a manner that if a camera were attached, a picture could be taken. I hope to finalize movement and help Joseph with the sensors this week as well.
Manini
This week I pivoted from the CPU version of the Faster RCNN model to a GPU based version. I was running into too many issues with the integration set up and module builds. I decided that the time I was spending on getting this version of the code to work was not worth it. I therefore launched an AWS instance with GPU capabilities and a Deep Learning AMI. I finally got the new GPU based Faster RCNN up and running and ran multiple tests using both VGG 16 and ResNet101 architectures. I then had to re-add the bias with the new model and figure out how to switch the model from multi-way classification to binary classification. Over the weekend I will be training this new modified model so that for the demo we can hopefully use this new version. Now that the inference will be completed on AWS, we will also need to create a script to send the images from the pi to AWS. This pivot put me slightly behind schedule, but I hope I can get back on track in the upcoming week by putting in more hours outside of class.
This weekend I also helped Karen with the bot’s movement. We ran multiple tests and recognized a fundamental inconsistency with the roomba’s angle turns. In order to accomplish accuracy with the bot’s basic movements, we will need to utilize a different approach (time-based turning) to enable the roomba to turn the amount we would like.
TEAM STATUS:
- What are the most significant risks that could jeopardize the success of the project? How are these risks being managed? What contingency plans are ready?
The current risk is that we are slightly behind in schedule. We do have a slack week that we are currently using up but it would be preferable if we could somehow catch up back on schedule incase any other issues arise. Our contingency plan is to meet more outside of class work on the project
- Were any changes made to the existing design of the system (requirements, block diagram, system spec, etc)? Why was this change necessary, what costs does the change incur, and how will these costs be mitigated going forward?
Instead of running inference of the model on the local laptop, we are going to send the images to AWS as we were unable to get the CPU version of the model working properly. This requires us to write a script to send and receive images from the cloud but overall it is not a huge change.
C9