̽��ֱ�� of Cambridge - ̽��ֱ�� of Lincoln

Dive bombing Killer flies are so fast they lose steering control

jg533 — Thu, 27 May 2021 08:20:03 +0000

These are the findings of a study by researchers at the Universities of Cambridge, Lincoln, and Minnesota, published in the Journal of the Royal Society Interface.

Killer flies (Coenosia attenuata) perform high-speed aerial dives to attack prey flying beneath them, reaching impressive accelerations of up to 36 m/s2, equivalent to 3.6 times the acceleration due to gravity (or 3.6g). This happens because they beat their wings as they fall, combining the acceleration of powered flight with the acceleration of gravity.

This is an impressive feat: diving Falcons, the fastest animals that predate in the air, achieve much lower accelerations of only 6.8m/s2. Falcons dive by folding their wings and simply letting gravity accelerate them towards their prey.

For the tiny Killer fly though, the high speeds achieved in aerial dives could come as a surprise - because the researchers think the fly doesn’t take the effect of gravity into account when diving to intercept a target.

To get their results, the researchers built a transparent ‘flight arena’ and flew a dummy prey target through it at constant velocity. Killer flies were filmed with high speed video cameras as they attacked the target, and the researchers watched the footage back in slow motion - using this data to reconstruct the entire attack sequence in 3D.

̽��ֱ��study found that Killer flies reached much higher accelerations in flight when taking off from the ceiling of the arena, compared to from the floor or walls. ̽��ֱ��flies beat their wings at a similar rate wherever they launched from, indicating that their flight speed is determined by a combination of wing power and gravity.

“When Killer flies took off from the floor or walls of the arena, they moved at the time when they could take the shortest path to the target. But they couldn’t manage that when they took off from the ceiling because the high acceleration caused by gravity changed the expected flight path,” said Sergio Rossoni, a PhD student in the ̽��ֱ�� of Cambridge’s Department of Zoology and first author of the paper.

By diving with super-high acceleration the Killer fly sometimes catches its target prey extremely quickly, but it often misses because its speed makes it challenging to change course mid-dive if the prey moves. But even if the fly doesn’t land on target, the dive quickly reduces its distance from the prey so it can keep sight of it while making the final manoeuvers to catch it.

̽��ֱ��researchers think the effect of not accounting for gravity during downward dives might be compensated by another advantage. High speed dives force the potential prey to change direction as the attacker approaches, but to do this the prey has to slow down - making it easier to catch.

Insects that hunt in the air usually attack their prey upwards, because the contrast of the prey against the sky makes it easier to see. Killer flies are unusual insect predators in this respect; hunting downwards against a visually cluttered ground, using eyes that have only coarse resolution, is more difficult.

“This research into miniature flies helps us understand what shortcuts are acceptable when survival depends on fast decisions and accurate actions, but the sensory capabilities and processing power of the predator are heavily constrained,” said Professor Gonzalez-Bellido at the ̽��ֱ�� of Minnesota, who led the study.

This research was funded by the Air Force Office of Scientific Research, the Biotechnology and Biological Sciences Research Council and the Royal Society.

Reference
Rossoni, S. et al: ‘Gravity and active acceleration limit the ability of killer flies (Coenosia attenuate) to steer towards prey when attacking from above.’ J.R.Soc.Interface, May 2021. DOI: 10.1098/rsif.2021.0058

Killer flies can reach accelerations of over 3g when aerial diving to catch their prey – but at such high speeds they often miss because they can’t correct their course.

̽��ֱ��high acceleration caused by gravity changed the flies' expected flight path when they took off from the ceiling

Sergio Rossoni

S.T.Fabian

Killer fly

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – as here, on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

Yes

Licence type:

Attribution

Robot uses machine learning to harvest lettuce

sc604 — Sun, 07 Jul 2019 23:00:59 +0000

̽��ֱ��‘Vegebot’, developed by a team at the ̽��ֱ�� of Cambridge, was initially trained to recognise and harvest iceberg lettuce in a lab setting. It has now been successfully tested in a variety of field conditions in cooperation with G’s Growers, a local fruit and vegetable co-operative.

Although the prototype is nowhere near as fast or efficient as a human worker, it demonstrates how the use of robotics in agriculture might be expanded, even for crops like iceberg lettuce which are particularly challenging to harvest mechanically. ̽��ֱ��results are published in ̽��ֱ��Journal of Field Robotics.

Crops such as potatoes and wheat have been harvested mechanically at scale for decades, but many other crops have to date resisted automation. Iceberg lettuce is one such crop. Although it is the most common type of lettuce grown in the UK, iceberg is easily damaged and grows relatively flat to the ground, presenting a challenge for robotic harvesters.

“Every field is different, every lettuce is different,” said co-author Simon Birrell from Cambridge’s Department of Engineering. “But if we can make a robotic harvester work with iceberg lettuce, we could also make it work with many other crops.”

“At the moment, harvesting is the only part of the lettuce life cycle that is done manually, and it’s very physically demanding,” said co-author Julia Cai, who worked on the computer vision components of the Vegebot while she was an undergraduate student in the lab of Dr Fumiya Iida.

̽��ֱ��Vegebot first identifies the ‘target’ crop within its field of vision, then determines whether a particular lettuce is healthy and ready to be harvested, and finally cuts the lettuce from the rest of the plant without crushing it so that it is ‘supermarket ready’. “For a human, the entire process takes a couple of seconds, but it’s a really challenging problem for a robot,” said co-author Josie Hughes.

̽��ֱ��Vegebot has two main components: a computer vision system and a cutting system. ̽��ֱ��overhead camera on the Vegebot takes an image of the lettuce field and first identifies all the lettuces in the image, and then for each lettuce, classifies whether it should be harvested or not. A lettuce might be rejected because it’s not yet mature, or it might have a disease that could spread to other lettuces in the harvest.

̽��ֱ��researchers developed and trained a machine learning algorithm on example images of lettuces. Once the Vegebot could recognise healthy lettuces in the lab, it was then trained in the field, in a variety of weather conditions, on thousands of real lettuces.

A second camera on the Vegebot is positioned near the cutting blade and helps ensure a smooth cut. ̽��ֱ��researchers were also able to adjust the pressure in the robot’s gripping arm so that it held the lettuce firmly enough not to drop it, but not so firm as to crush it. ̽��ֱ��force of the grip can be adjusted for other crops.

“We wanted to develop approaches that weren’t necessarily specific to iceberg lettuce so that they can be used for other types of above-ground crops,” said Iida, who leads the team behind the research.

In future, robotic harvesters could help address problems with labour shortages in agriculture, and could also help reduce food waste. At the moment, each field is typically harvested once, and any unripe vegetables or fruits are discarded. However, a robotic harvester could be trained to pick only ripe vegetables, and since it could harvest around the clock, it could perform multiple passes on the same field, returning at a later date to harvest the vegetables that were unripe during previous passes.

“We’re also collecting lots of data about lettuce, which could be used to improve efficiency, such as which fields have the highest yields,” said Hughes. “We’ve still got to speed our Vegebot up to the point where it could compete with a human, but we think robots have lots of potential in agri-tech.”

Iida’s group at Cambridge is also part of the world’s first Centre for Doctoral Training (CDT) in agri-food robotics. In collaboration with researchers at the ̽��ֱ�� of Lincoln and the ̽��ֱ�� of East Anglia, the Cambridge researchers will train the next generation of specialists in robotics and autonomous systems for application in the agri-tech sector. ̽��ֱ��Engineering and Physical Sciences Research Council (EPSRC) has awarded £6.6m for the new CDT, which will support at least 50 PhD students.

Reference:
Simon Birrell et al. ‘A Field Tested Robotic Harvesting System for Iceberg Lettuce.’ Journal of Field Robotics (2019). DOI: 10.1002/rob.21888

A vegetable-picking robot that uses machine learning to identify and harvest a commonplace, but challenging, agricultural crop has been developed by engineers.

For a human, the entire process takes a couple of seconds, but it’s a really challenging problem for a robot

Josie Hughes

Robot uses machine learning to harvest lettuce

Yes