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Drone Delivery: By The Numbers

What do medicine, batteries, and forgotten anniversary gifts have in common?  They are the most likely items consumers will want delivered by drone once that service is available.  At least, that’s the verdict from the consumers I surveyed in August and September of this year. In my post Drone Delivery: How Much Would You Pay?, I ran a poll with three simple questions:

What’s the maximum amount you would be willing to pay for a package delivered by drone? Which of ten items would you want delivered in 30 minutes? Under what circumstance would you need something so quickly that you’d pay top dollar for it?

These are the topline results.  You can see the companion summary presentation with more complete graphs and charts of the data here.

The max you’d pay?  First, I wanted some base data on how much people would pay for drone delivery.  So, I asked if consumers were willing to pay for the service and whether they wanted to pay a flat fee or a percentage of the price of their purchased items.  More than three-quarters of respondents (82%) told us they would be willing to pay (vs. 18% who said they wouldn’t), and the largest majority of those who’d pay (62%) said they would prefer paying a percentage of the item’s purchase price (vs. 18% who said they would rather pay a flat fee).

When we asked those who were willing to pay for the service how much they would be willing to pay, we saw big differences in preference. For instance, as I mentioned, only 18% of respondents said they’d prefer a flat fee, and 80% of those people said they wouldn’t pay more than US $50 for delivery.  That’s not a lot more than express overnight delivery fees. I doubt these consumers will be using fixed charge drone delivery services.

For those respondents who indicated they’d pay a percentage of an item’s price, more than half (51%) said they would pay up to 10% of an item’s purchase price.  Most of the rest (43%) said up to 20%, and only 6% said up to 30%.  It seems that a percentage charge could leave a delivery service with losses if the delivered items aren’t high priced.

Items you’d want delivered?  Second, I was curious to know what items consumers would likely purchase and want delivered by drone. I reviewed online shopping trends to find most popular product categories and top purchase drivers – keeping in mind the items had to fit the following drone delivery requirements:

The order must be small enough to fit in the drone’s cargo box The items must weigh less than 5 lbs.

There were clear winners and losers in the list of items consumers would want delivered in 30 minutes or less (see Figure 1).

FIGURE 1 – Which of 10 Items Would You Want Delivered in 30 Minutes?

Drone Deliver Fig 1

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Google Science Fair winner models drone obstacle evasion on fruit flies

He's 14 years old. The mind boggles.

From IEEE Spectrum:

Mihir Garimella, from Pittsburgh, (video above) figured out a way to make flying robots evade collisions with obstacles, moving and nonmoving, by behaving like fruit flies. Garimella got the idea for his system when his family returned from a trip, to find rotten bananas on a counter and a house full of fruit flies that seemed to be able to brilliantly evade swatting. Garimella’s uses the simple vision system of a fruit fly to allow an onboard computer to quickly detect and analyze a coming threat, and wrote algorithms to mimic the fruit fly’s tendency to dodge by moving first horizontally, then vertically to escape to the threat. Garmilla’s project won top honors in the 13-14 age group and the computer science award.

His full, and amazing, report is here. Summary:

Noting the limitations of approaches presented by previous work, I aimed to create a simpler, faster, and more practical method of onboard threat evasion, inspired by the way fruit flies detect and respond to threats. After reviewing relevant work in the fields of biology and robotics, I designed a computationally and physically lightweight sensor module, modeled after the fruit fly's rudimentary but fast visual system. I also created novel algorithms to model the trajectory of and escape from approaching threats by mimicking fruit fly escape behaviors, and verified the effectiveness of these algorithms both experimentally and through a still image comparison to fruit fly escape.

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Commercial Drones Are Coming, And Sooner Than You Think

via techcrunch.com Over the coming decade, drones will very likely become commonplace. Today, when we think of drones, we tend to think of the military variety, but in the coming years, we are going to see drones doing jobs where it’s too dangerous, too remote or too expensive for human-run aircraft to go. Think of use cases like …

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Team Thunder’s 2014 UAV Outback Challenge

Team Thunder is a Sydney based team of RC enthusiasts who competed in this year's UAV Outback Search and Rescue Challenge in Kingaroy, Queensland, Australia, running the week of September 22-26, 2014. (http://www.uavoutbackchallenge.com.au/). The following is a description of our mission flight. A more technical report will follow.

The Flight

Friday came around and finally it was Team Thunder’s turn to find Joe. 

The wind was picking up over 15 knots but we knew our X8 flying wing was up for it.

Setup went smooth enough until the GCS operator (Peter) discovered that the USB serial link to the RFD900 radio link wasn’t being recognised by the Windows GCS laptop. A reboot but Windows wouldn’t start up again. A little bit of panic and thinking “What will Tridge think of these fools who decided to rely on Windows for a mission critical device?” A quick change to Marks backup PC got things going again. Luckily Windows eventually started on the main GCS PC again so things were back on track.

A bit of a time out due to air traffic, then started again and we had the plane on the launcher at the end of setup time. David had all the flight systems up, Mark had the bungee loaded, Lloyd had the image recognition system up running and communicating over the 3G and RFD900 links. The GCS was all ready to go so the thumbs up was given to the pilot Tim pushed the lever on the launcher. 9:00am and Thunderbird 1 was away.


Before we knew it, the plane was passing EL1 and then EL2 into the search area. Smiles all round. This was exciting. 

As there was a strong south-westerly blowing we had decided on north south runs, starting from the east to avoid overshooting downwind turns. We would be flying at 120m AGL, scanning 100m strips, leaving 50m gaps between the strips on the first pass that would be cleaned up on the second pass.

The first downwind turn was a little nerve racking but were soon at the north-eastern end of the search area and into our search runs.

Things were going well. Although it was windy and the plane was jumping around a bit, it was doing what it was meant to be doing and the upwind turns were working out ok.

Midway through the 3rd scan run and only 12 minutes into the mission, Mark called out that he had an automatic (thanks to Tridge’s algorithm) Joe position.

With a set of coordinates from Lloyd we got them away to the judges who passed on permission for us to drop the bottle.

We then passed over the Joe a number of times to get a more accurate averaged position. We uploaded the bottle drop waypoints. We were taking things very slowly and carefully now. We had heaps of battery and though the wind was strong, the X8 was handling it ok. Once we were happy we set off the sequence with a predefined routine from the GCS and the command “godrop” was given. Soon the bottle was away and Thunderbird 1 was on its way back to the airfield.

The marshals confirmed that the package separated from the aircraft ok to our relief.

3 minutes later, Thunderbird 1 was spotted approaching the airfield, ready to attempt an automatic landing. We underestimated the wind a little on our east-west landing at the end of the runway ending up in the long grass but a safe landing in one piece nonetheless.


The BOM recorded maximum wind for Friday was 43 km/h at 09:34. This was just as we landed.

The judges confirmed we had dropped the bottle within 100m of Joe and that 500ml of water had been recovered. Mission accomplished.

More details including documents, Joe Recognition System and flight logs to follow.

Team Thunder (left to right)

Tim Fu: Pilot and airframe
Lloyd Breckenridge:  Joe detection system
Peter Wlodarczyk: Leader and software
Mark Frasca: Search, bottle drop and airframe
David Creusot: Avionics


Andrew Tridgell and Canberra UAV:  For the extraordinary open sourcing of everything they did, including APM software, MAVProxy, simulation environment, Joe recognition algorithm, RFD900 radio firmware, Joe image sets as well as advice during the course of the competition.

Michael Oborne: Developer of Mission Planner

The general Ardupilot developer community.

Orion Integration (http://www.orionintegration.com.au) : For the use of radios and camera and the tolerance of skunkworks by Lloyd.

Claudio Natoli: For the kind loan of a failsafe board.

Millswood Engineering (http://www.millswoodeng.com.au): For the design and kind donation of a spare failsafe board.

Seppo at RFDesign  (http://rfdesign.com.au): For an awesome RFD900 radio design and advice.

Hawkesbury Model Air Sports (HMAS) Club (http://www.hmas.org.au): Support, encouragement and an excellent field at Vineyards.

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PyQuadSim: Hover-In-Place via Optical Flow

This video shows automated Hover-In-Place (HIP) using the PyQuadSim Python Quadrotor Simulator with OpenCV for optical flow.  This setup allows us to prototype HIP and other optical-flow-based algorithms for use with an actual optical-flow sensor like the PX4Flow.

The quadrotor is being flown over a simulated concrete floor using a FrSky R/C transmitter, allowing us to switch position-hold (HIP) on/off.  The small black window shows the pitch/roll/yaw/climb demands from the transmitter. The small gray window shows the optical flow computed by OpenCV from a downward-facing 128x128-pixel vision sensor created in V-REP.  (If you look carefully, you'll see that the side-to-side flow is backwards in the video. I have since corrected this problem in the online code.)

The position-hold signal is computed as the average X,Y pixel flow sampled every 16 pixels, converted to meters-per-second using trigonometry.  The second part of the video shows HIP facing a "breeze" from a blower.  Even without Kalman filtering or other smoothing, the optical-flow signal is sufficient to keep the vehicle more or less stationary.

This project is open source and available at


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