The above image is a snip of a search on news.google.com for the word “drone”.

It’s important to know the political impact that the words you are using. Language shapes perception, and the overarching themes surrounding the word “Drone” revolve around the use of armed US Drones in military actions around the world. Whether you want to or not… whether you are an aerospace researcher developing systems for the US military, or you are environmental scientist using them to research plant health, you are conjuring up an imaginary hellfire missile when you use the word drone.

The implications this has, for everything from funding to public engagement, can be dire. The term provokes unwanted and unwarranted controversy, and invokes a political argument that is completely unrelated to the uses of unmanned aerial systems in science and entertainment. The term, “unmanned aerial systems” invokes none of the, in my opinion well placed, ire of those in opposition to US foreign policy with regards to drone strikes. It’s an accurate term that reflects the diversity of remote controlled, semi-autonomous and autonomous aircraft in use in higher education, research, conservation and entertainment sectors.

There has long been a philosophical debate about the inherent value of the progress of technology. Indeed, UAS and Drones seem to be the next big frontier in this argument… it’s a fact that increases in the available robotic and autonomous vehicle capabilities have greatly enhanced the ability of researchers to conduct research in a variety of settings and from unique perspectives. But to demonize the technology as opposed to the human decisions to use that technology we do two things: we take away any potential positive value that the technology can contribute to society, and we simultaneously remove the human decision making at heart of the issue of US drone strikes overseas. By making the issue one of technology, and not policy, we remove the burden of responsibility from the human agent of destruction for the choices they make which lead to death and mayhem.

So don’t call it a drone. Please.

As I talked about in the previous tear-down photo blog, the library quadcopter crash also took out a GoPro Hero 3+ Black edition.

Last night I completely disassembled the GoPro that was on the quadcopter when it crashed.

I just wanted to see what the inside of the camera looks like, and what the possibilities for modifying it are.

I took a couple of pictures of the parts, which I’m posting here.

Innovative work often goes in unplanned direction. One moment you’re working on a great idea… and the next you’re moving in a completely different direction than you started out… or even intended.

In certain situations this is more literal than others.

The GSU Library, along with the Anthropology Department and Geosciences Department have obtained small unmanned aerial systems from DJI called the DJI Phantom. More than a few of these systems have suffered from some unplanned innovation in the form of crashes.

These systems are specialized, and DJI ( based in china) doesn’t have the best tech support or service for US customers.

This post will essentially be a photo log of the tear down process for the DJI Phantom II owned by the library. The aircraft fell around 11 stories without power, crushing a gopro camera and it’s mount on the quad, as well as crushing the landing gear. It was also apparent that there would be internal damage to the craft as well as what was initially visible. In order to determine the extent of that damage I had to do a complete dis-assembly.

The first step in disassembling a DJI Phantom is removing the propellers and the landing gear.

Then the body shell must be disassembled

A this stage it becomes possible to begin testing components.

Once I have determined what parts look like they will need replacement, I proceed with removing the internals completely from the body shell

With the internals removed, I am ready to desolder the components which we will be replacing, and once the spares arrive put the whole thing back together.

All in all, the damage was not that bad to the quadcopter considering it fell 11 stories onto concrete. Two propellers were broken, and the body shell will need replacing. Also, the plug for the battery was shattered, and the main board, which all the components attach to, was slightly damaged, but may be repairable. The primary damage to was the gimbal and the GoPro Hero 3+ Black attached to it. This is the robotic arm that allows control of and stabilizes the camera while the aircraft is flying. Both the Gimbal and the GoPro were utterly destroyed. Because these are mounted on the bottom of the DJI Phantom, it looks as though they absorbed the brunt of the impact of the nearly 3 pound unmanned aerial system hitting the ground at terminal velocity.

Quick aside: the Phantom 2 has a takeoff weight of around 1.3kg. This fell around 11 stories, which is about 36-37 meters. It would have been traveling at around 27 meters per second when it landed which is nearly 100kph or just shy of 60mph.

Keep an eye out for more posts on the nuts and bolts of using unmanned aerial systems of all types for research applications!

In modern archaeological research, some portion of the research will take place in a Geographic Information System or GIS. Whether it’s fully integrated into the project both documenting and directing research, or utilized solely for the archiving of mapped data, there is a  GIS. The seeming precision of GIS mapped data, coupled with the ability to calculate complex spatial statistics for research and guidance results in a very friendly environment for black box fallacies. This is the problem of closed source, undocumented analytic software tools being used to conduct research. When a black box is used uncritically, without thought for research design and control the results of the research conducted with the black box can become suspect.

Take for example, the issue of  analyzing site inter-visibility with a viewshed analysis. This is a common use of this GIS tool in landscape archaeology. By tracing lines of sight the software is able to create a binary map, where a pixel will be recorded as either visible or not visible. Commercial software package ArcMap from ESRI has one set of algorithms for this purpose. The most popular open source GIS, Qgis has a different set of very similar algorithms. The result is that with the same input parameters and data Qgis and ArcMap can produce slightly different binary maps of visibility.

Because Qgis is open source, an interested party could very easily examine the source code responsible for the resultant visibility map. None of this is possible with a black box, whether it be for viewshed analysis, point cloud processing or interpolating data. It’s impossible to view the source and impossible to modify or know exactly why you get the results you get. But because the results are from a computer they can very often be treated uncritically by researchers despite the lack of information on how exactly they arrive at a given result.

This last point is actually a larger problem within the use of GIS and archaeology. As an example, I relay the findings of Thomas J. Loebel of St. Xavier University. Loebel examined the distribution of paleoindian stone tools across one county in the midwest US. Loebel found that the distribution of tools across space was more highly correlated with modern human activities than any environmental or ecological features, which he interpreted as resulting from a higher incidence of ground disturbing activities. The result is the same, an uncritically used tool can result in seemingly precise results which have absolutely no bearing on the physical world.

There is no real uniform solution to the black box problem with GIS, however some of the resultant questions can be mitigated by clear and explicit explanation of what software, what version of the software and what settings are used. Each of these factors can have an impact on on the final result, which is then used for analysis and interpretation. Documenting these factors is a critical baseline step for mitigating the impacts of software on research programs. Further steps would include documenting and maintaining precise descriptions of all steps in a processing chain as well as critical research done on comparable closed and open source versions of a given set of tools. It’s especially critical when interpolating or extrapolating data to recognize the importance of source data. The saying garabage in garbage out has very real relevance here. If the input data is a poor representation of the phenomena being modeled, the model will also be a poor representation of the phenomena.

References