This week I put in 16 extra hours of work on my project total. On the first day of class this week, I worked with Daniel to get pictures of the main room of the energy Lab with the drone. This was successful and I got over 600 quality images. I then uploaded them to the Hydra and then to AgiSoft. I then told the program to detect points, and started placing markers on common points throughout the 600+ photographs. I made 16 markers total, and had to go through every picture individually and assign all the markers shown to their common points. This took me the next two class periods to complete, plus all the time outside class that I put in. I may still go back through the first photos because I created points as I went, and may have missed some markers that I had created later. Below is a video of the drone as it was taking pictures of the elab.
This week was very productive! I began my free trial with AgiSoft. Pikoi took new pictures of the elab with his phone (we couldn't find the batteries for the Nikon camera). I think he took about 600 pictures of the main room. He and I went up to the elab on Wednesday after workshops and worked for around six hours. We were lucky because we were the only ones allowed in the energy lab at the time; security unlocked the doors for us, and then locked them behind us (big shoutout to Dr. Bill for giving us permission to work in the energy lab at the time). This was incredibly important because the main room always has people in it, so we never get the chance to take still shots of it.
On Thursday, I wanted to work with Ilan and the indoor drone to get some aerial footage of the elab in hopes of compiling a vr model of it, but SOMEONE had forgotten to charge the drone battery, so we didn't get to do that. I also spent a long time building the preliminary mesh—which was assembled without setting reference points—and eventually left to load overnight. It did not turn out too well.I am home on Kauai for the weekend to run a half marathon (because I'm Ultra Masochistic), and I realized that I should probably dedicate more time to the project on Friday, since I wouldn't have a chance to for the next three days. I went up before the extended period and worked through lunch. I established 13 reference points in all 600 photos and built the mesh, although I did not get a chance to actually see the final product. I am hoping it turns out more accurate than the original!
Eliminating Vehicle-Related Nēnē Death: A Robotics Approach
Zoë McGinnis, 2017
Purpose and objective
If nēnē geese are fitted with an affordable, Arduino-compatible Bluetooth module, then the module can be programmed to trigger a warning sign near problem roads to warn drivers of nēnē proximity. Therefore, vehicle-related nēnē deaths will be prevented, thus helping to eliminate man-made threats to nēnē population.
One of the most imminent threats to the nēnē goose population is roadside and traffic-related injury and death of chicks and adults within the breeding age. In December, 2016, The Department of Land and Natural Resources (DLNR) reported in The Garden Island newspaper and KHON2 news that over 50 nēnē had been killed by cars in the past two years. The most publicized recent nēnē deaths occurred in January, 2017, on Kauai, near the Hanalei bridge; two goslings were hit by cars. DLNR reports that although vehicular nēnē deaths happen on neighboring islands, Kauai’s roadways tend to be the most deadly (The Garden Island, 2017). To make these phenomena even more alarming, Kauai is home to around 65% of the nēnē population, and only 10% of female nēnē are estimated to breed naturally outside of Kauai (ICNU Red List of Threatened Species, 2017). Thus, the death of Kauai nēnē geese in vehicular incidents is impacting both the general nēnē population and the breeding population.
Plan of action
The HC-05 Wireless Bluetooth module—priced at $8.99 retail on Amazon—in operation with the Adafruit Trinket—priced at $6.95 retail on Amazon—can be used as a leg tag for nēnē geese. Given that the module is 3 ounces and 1.1 x 0.6 x 0.1 inches (and can likely be reduced in size through modification), it will be non-invasive in leg tag form, and will be waterproof and weather-resistant. The module is configured with a Bluetooth interface, and programmed to respond to connection initiation by a stationary Arduino module within a 500 foot line-of-sight range and a 200 foot (estimated) proximity to the stationary module. The stationary module—which will initiate the connection—will be positioned in problem areas, where vehicle-related nēnē deaths are frequent. The stationary module will be connected to a power supply (a battery, perhaps); it will be configured as to send out invitations to connect to Bluetooth modules in the tag. Once a connection has been successfully initiated (meaning a tagged nēnē is within 200 feet of the stationary module), flashing LED lights will be triggered. The flashing lights will be on a roadside sign reading something along the lines of “BEWARE: NĒNĒ WITHIN 200 FEET OF ROAD WHEN LIGHTS FLASH.” Thus, drivers will be presented with an eye-catching display that warns them not only of possible nēnē crossings, but also immediate nēnē proximity.
BirdLife International. 2017. Branta sandvicensis. (amended version published in 2016) The IUCN Red List of Threatened Species 2017: e.T22679929A112386209. http://www.iucnredlist.org/details/22679929/0. Most recent date of access: 28 July 2017.