PRE2015 3 Groep2 week6

Further testing

NOTE: Experiments in random order.

Due to limited battery time, a small adjustment has been made to the experiment to increase productivity. Instead of testing with only one test subject at the time, two people will be standing next to each other as the drone approaches them. Because of the accuracy of the drone, which is about 0.5 meter, both test subjects should feel as they are being approached by the drone. The test subjects have approved it and indicate that because the full focus is on the drone, it does not influence the results. Afterwards both test subjects are questioned separately so their opinions will not influence each other.

Experiment 2

Results A (Landing vertical)

Experiment	Very bad	Bad	Neutral	Good	Very good
1A		X
2A	X
3A		X
4A			X
5A				X
6A				X
7A			X
8A				X

Results B (Landing at an angle)

Experiment	Very bad	Bad	Neutral	Good	Very good
1B				X
2B				X
3B				X
4B				X
5B			X
6B	X
7B	X
8B				X

Results C

Experiment	Very bad	Bad	Neutral	Good	Very good
1C				X
2C					X
3C				X
4C			X
5C		X
6C				X
7C				X
8C		X

Analyzing results

Test group

During the experiment it wasn't possible to to the experiment on a diverse test group. As a result the conclusion of the experiment will only be valid for Dutch males, aged between 20 and 25 years and who have a technical background. If their is more time and people available who don't fall into this region, preferably females, these will be added to the results. No other test subjects will be added if these people can't be found and the results, and so the conclusion, will be based on a heavily biased test group.

Figure 1: A bar graph of the three ways of approaching vs. the rating. The error bars show the two-tailed 95% coincidence interval.

T-test

A T-test gives the possibility to decide if an hypotheses needs to be rejected or not . The hypothese in this case are the values: 'Very bad' (1), 'Bad' (2), 'Neutral' (3), 'Good' (4) and 'Very Good' (5). A t-test will be conducted of all the hypotheses value of the different approach routes.

To determine how likely a rating will occur during a specific approach, a p-value will also be computed. This value stand for the probability that an user will rate the experience a certain way.

Shown in the table bellow are the p-values of the different ways of approaching. Values which are rejected by the t-test (one-tailed 95% coincidence interval, α = 0.05) are marked with an 'X'.

Rating	Vertical	At an angle	Horizontal
Very bad	X	X	X
Bad	0,064	0,051	X
Neutral	0,763	0,802	0,227
Good	X	0,111	0,227
Very good	X	X	X

Looking at the p-values it the vertical approach seems is the worst of the three approaches to use. Landing at an angle and a horizontal approach are far better rated by the user, with the done approaching the user at an low height (approaching horizontal) at number 1.

During the experiment the test subjects where asked to support their ratings. It became clear that users where more considered about their head, and especially eyes, that this could be the reason that landing vertical and approaching at an angle are rated lower than approaching at low heights.

For the autonomous landing part flying at low heights also has an advantage, since the ultrasound sensor can detect smaller objects.

State of the Art

Being at the brink of being introduced, drone delivery still encounters many problems. Most of these problems are not in autonomous flying however, but mainly in the implementation of these technologies. The first companies are already using less urban areas as their test-grounds. The concept is working, but remains unreliable. The drones still have the tendency to crash into objects and the endless list of unexpected events that can possibly happen ask for a high reaction speed. Also the limited battery lifetime appears to pose problems. Below Amazon's Prime Air is described, as the leader in the current developments, followed by more general current developments and points that still are being researched on.

Amazon Prime Air

The leader in the current developments is the Amazon Prime Air concept from Amazon. Unfortunately the drones are not ready to be embedded in society, but successful flights already have been made. One of the drones they are using is small plane that has the possibility of vertical takeoff. Flying the drone as a plane increases the speed and range of the drone, resulting in a delivery time of 30 minutes or less and a range of 24 kilometers. As the drone approaches its destination, a message is send to notify the customer that the package is arriving. The drone recognizes its destination by a big 'A' that is placed on the ground. Before lowering itself, the landing area first is scanned for obstacles. After the landing, a small valve is opened releasing the package. A second drone that is being developed is a quad-copter. This drone has a range of about 14 miles and can carry packages of about 2.5 kg. This may not seem much, be around 80% of the packages delivered by Amazon falls within this weight margin, making the likelihood of this technology being introduced in the recent future rather high. Hopes are to be able to make us of this technology around 2020.

Also Google and Walmart are joining the competition to get the first working delivery drones ready. These three competitors all want to be the first company that can use the drones, which means that a lot of research (and money) is involved. The problem those companies are working on is the reliability of the drones.

Can and cannot

Autonomous flying is not the main problem of technology anymore, some companies already want to carry out their autonomous drone in the near future. Those companies however, are using less urban areas for testing but will not hold in busier areas. Drones need to be more reliable, they still have a tendency to crash and run into objects. Some experiments, for example from MIT, are getting better at avoiding object autonomously , but this problem is still big because of unexpected events that asks the drone to react very quick. How for example to cope with moving objects like people, animals (an enthusiastic dog for example) or flying objects (a football for example). Simply stuffing the drones with numberless sensors would drive up the price drastically, hopes are to be able to develop smart software that only uses few and or cheaper sensors to make the drones more attractive on the market.

A second problem is the limited battery-life of the drones. Increasing the size of the battery would reduce the loading capacity, but a small battery again will drastically decrease the range of the drone. This would require many distribution centers, which on their turn need to be supplied as well. An alternative however is given by ###, proposing to have the battery replaced without landing.

Another problem with delivery drones these days is the ‘problem of the last meters’. These last problems aren’t so much about the technology, since most of it already exists, but how to implement all these technologies to make it actually work. These problems are for example: how do we deliver packages in (high) apartment buildings and how do people (and animals) react to these kind of deliveries . The article from the Washington Post gives a great start to start asking questions which aren’t technical, but more to the side of users. What do we want as society? Areas where relatively less research is done, compared to the more technical side. This however is exactly what this project about, making a next step in the development of user-friendly or rather user-centered drones.

First version report

Media:First_version_report_group_2_quarter_3_2015-2016.pdf