This journal is maintained by Winlab summer interns Vamsi and Aniket, who are working on robotmobility. Unless otherwise noted, the entries are typed up by me (Aniket) and I refer to myself in first person for simplicity.

Week1 Week2 Week3

6/25/07 - Day 16

We setup an node to act as an access point. Then we tested our algorithms to see if they could find the node. Unfortunately, they were not completely successful.

Problems with Vamsi's code:

• Initially the error "local variable cannot be referenced before assignment" was complaining. This has to something with local and global variables. The parallel class might need to pass the necessary variables in order for it work properly. Temporarily all variables are assigned in their needed methods. Therefore, this error no longer occurs

• Ultimately, the robot roam around in ~10X10 (in) square that is near to the correct location. Due to fluctuations in signal strength, this method does not come as close as we would like it

6/26/07 - Day 17

Today was spent tweaking our algorithms for the presentation tomorrow. We got a little bit of time on the grid, which we used to calibrate the distance the robot chooses to travel based on its measurements.

Updates have been slow lately because most of our time has been spent perfecting our algorithms. The presentation is tomorrow, we will describe the final methods in full detail then.

I'm not sure if I've done this before, but I will describe the challenge again. One of the nodes in orbit is made to be an Access Point. The robots connect to that access point, and then based solely on the strength of the signal they are receiving attempt to travel towards it. For now we are neglecting obstacles, so avoidance is not an issue. Coding is done in python.

One method I have ruled out is the Nelder-Mead algorithm, which is a popular choice in optimization but ill-suited for our particular purpose. The reason is that the method does not take into account the random fluctuations of our signal. One of the characteristics of the Nelder-Mead algorithm is to perpetually remember the "best" point it ever finds. Due to random fluctuations in Oribt, often the "best" point located is found to be such because of circumstance rather than proximity to the access point. I've attempted a variation of the Nelder-Mead which "forgets" its best point after a number of iterations go by, but I never was able to use the method successfully.

One thing that has become clear is that many measurements must be made to smooth out the noise.

Another somewhat unusual characteristic of the problem (unusual compared to most optimization problems)… TBC

6/27/07 - Day 18

Today was spent mostly on changing our existing code to make our algorithms better. As mentioned before, although our programs eventually reach a "good" distance from the access point, there are always fluctuations in signal strenght that cause the program to be inconsistent.

Ivan suggested a using a statistical method to fit data so that our programs will function more efficeintly. The following week is going to be spent working on that.