Joseph:
On Monday I worked on finalizing the Design Review Document. Afterwards I helped Manini evaluate two different GitHub implementations of Faster RCNN. Afterwards, since path planning is still being worked on and it would be inefficient to pass the Raspberry Pi back and forth between Karen and I, I made a shift in the schedule and began working on the user interface for the SOS bot. I have the design laid out and will be coding it out in the weeks to come.
After spring break, I hope to have the user interface completed or near completion. It will be able to upload maps and points of interest to the SOS bot as well as display the bounding boxes around images that the model evaluates. After the user interface is done, I will go back to working on the object detection portion of the model.
Karen:
This week was spent juggling between the Design Review Document and working with the path planning algorithm. The majority of Monday was spent finalizing the document so that we could submit at the midnight deadline. In terms of the script, I had originally started to manipulate basic movement by creating my own baseline functions that would require a lot of math. However, after researching I found some libraries that will aid with this process- such as PyCreate. So I am currently integrating these functions with my script so I do not have to fine tune the movement of the bot so much in order to avoid accumulation of error.
When we come back from Spring Break I would like to completely finish the path planning script and start unit testing with the bot. This will include moving to a single point of interest and then a series of points as will happen in the demo. I will also start aiding Joseph with the obstacle avoidance as I have also started researching into how to implement this with the sensors we have now received.
Manini
This week I was able to find a Faster RCNN implementation that supports CPU testing and have been working on getting it running on my laptop. I ran into multiple issues with integration, so a majority of my time has been spent debugging and figuring out the set up script included in the repo. A large portion of my time Monday was spent finishing up the design report.
In two weeks (the week after Spring break) the Faster RCNN model will have baseline results and the first experiment for biasing the model should be ready. In that way, I can use the week to train the model with the COCO sub-dataset.
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 biggest risk right now is still staying on schedule. Since Spring Break is next week we will have a one week gap before we can get baseline results for our model and begin retraining with the biasing cost function.
- 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?
The user interface is being worked on earlier while the object detection is being pushed back because path planning is still being worked on and it would be difficult to test both path planning and obstacle detection at the same time.
C9