Joseph:
This week I worked with Karen to fine tune the robots movements as well as implement the picture taking. We are able to take multiple pictures at each interest point although we are finding that they do not give full coverage of the room that we are in. I was also able to implement software that is able to take the pictures taken and display them on the UI.
Next week I plan on implementing the sensors as well as work with Karen to finish obstacle avoidance. I will also be working with Manu when the model is ready to implement that with the UI
Karen:
As explained above, this week I focused on the image taking portion of the bot. We are able to connect the camera and incorporate the commands need so that a still picture could be taken. Originally, we had specified that 3 images would be taken- however it seems that this does not provide full coverage. So I decreased the turn angle and am now taking six images of the room. The taken images are saved to the raspberry pi Desktop, allowing the remote UI to access the files. The next step regarding the images is to mount a pole of some sorts and place the camera module on this, so that the images taken are elevated to account for varying human heights.
Next week, I would like to aid Joseph in incorporating the sensors on the bot and testing the obstacle detection script. Ideally we would like to get obstacle detection completed by next week so that the following week we can fine tune obstacle avoidance.
Manini
This week I was able to download all of the required COCO data (train, validation) to the ec2 instance. I was also able to modify the script to only identify humans and background classes (binary classification). I also was able to train the model for one epoch. This weekend I will complete the training for all epochs and evaluate the performance of the model and retrain the model with the bias.
One issue I ran into this week was with the memory capacity of my ec2 instance and the compute time required to retrain the entire model. In order to train the model with 8 GPU’s and have enough memory per GPU for the given model and data size I need to upgrade to a P3.xlarge. However the cost for this machine is very expensive. I am currently doing some calculations to identify a way to serve our needs while staying within budget.
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 most significant risk currently is ensuring that the trained model only identifies human objects and that we make good headway on obstacle avoidance. We are working extra hours outside of class and with regards to the model training, we have spoken to Nikhil who helped us out a lot with this. Another risk is that the multiple pictures we take do not give us full coverage. This can be fixed by just taking more pictures, or at least enough such that no human is missed.
- 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?
There has been no changes since last update.