Full metal jacket: the fashion of Iron Man

by Peter W McOwan and Paul Curzon, Queen Mary University of London

Spoiler Alert

Industrialist Tony Stark always dresses for the occasion, even when that particular occasion happens to be a fight with the powers of evil. His clothes are driven by computer science: the ultimate in wearable computing.

In the Iron Man comic and movie franchise Anthony Edward Stark, Tony to his friends, becomes his crime fighting alter ego by donning his high tech suit. The character was created by Marvel comic legend Stan Lee and first hit the pages in 1963. The back story tells how industrial armaments engineer and international playboy Stark is kidnapped and forced to work to develop new forms of weapons, but instead manages to escape by building a flying armoured suit.

Though the escape is successful Stark suffers a major heart injury during the kidnap ordeal, becoming dependant on technology to keep him alive. The experience forces him to reconsider his life, and the crime avenging Iron Man is born. Lee’s ‘businessman superhero’ has proved extremely popular and in recent years the Iron Man movies, starring Robert Downey Jr, have been box office hits. But as Tony himself would be the first to admit, there is more than a little computer science supporting Iron Man’s superhero standing.

Suits you

The Iron Man suit is an example of a powered exoskeleton. The technology surrounding the wearer amplifies the movement of the body, a little like a wearable robot. This area of research is often called ‘human performance augmentation’ and there are a number of organisations interested in it, including universities and, unsurprisingly, defence companies like Stark Industries. Their researchers are building real exoskeletons which have powers uncannily like those of the Iron Man suit.

To make the exoskeleton work the technology needs to be able to accurately read the exact movements of the wearer, then have the robot components duplicate them almost instantly. Creating this fluid mechanical shadow means the exoskeleton needs to contain massive computing power, able to read the forces being applied and convert them into signals to control the robot servo motors without any delay. Slow computing would cause mechanical drag for the wearer, who would feel like they were wading through treacle. Not a good idea when you’re trying to save the world.

Pump it up

Humans move by using their muscles in what are called antagonistic pairs. There are always two muscles on either side of the joint that pull the limb in different directions. For example, in your upper arm there are the muscles called the biceps and the triceps. Contracting the biceps muscle bends your elbow up, and contracting your triceps straightens your elbow back. It’s a clever way to control biological movement using just a single type of shortening muscle tissue rather than needing one kind that shortens and another that lengthens.

In an exoskeleton, the robot actuators (the things that do the moving) take the place of the muscles, and we can build these to move however we want, but as the robot’s movements need to shadow the person’s movements inside, the computer needs to understand how humans move. As the human bends their elbow to lift up an object, sensors in the exoskeleton measure the forces applied, and the onboard computer calculates how to move the exoskeleton to minimise the resulting strain on the person’s hand. In strength amplifying exoskeletons the actuators are high pressure hydraulic pistons, meaning that the human operators can lift considerable weight. The hydraulics support the load, the humans movements provide the control.

I knew you were going to do that

It is important that the human user doesn’t need to expend any effort in moving the exoskeleton; people get tired very easily if they have to counteract even a small but continual force. To allow this to happen the computer system must ensure that all the sensors read zero force whenever possible. That way the robot does the work and the human is just moving inside the frame. The sensors can take thousands of readings per second from all over the exoskeleton: arms, legs, back and so on.

This information is used to predict what the user is trying to do. For example, when you are lifting a weight the computer begins by calculating where all the various exoskeleton ‘muscles’ need to be to mirror your movements. Then the robot arm is instructed to grab the weight before the user exerts any significant force, so you get no strain but a lot of gain.

Flight suit?

Exoskeleton systems exist already. Soldiers can march further with heavy packs by having an exoskeleton provide some extra mechanical support that mimics their movements. There are also medical applications that help paralysed patients walk again. Sadly, current exoskeletons still don’t have the ability to let you run faster or do other complex activities like fly.

Flying is another area where the real trick is in the computer programming. Iron Man’s suit is covered in smart ‘control surfaces’ that move under computer control to allow him to manoeuvre at speed. Tony Stark controls his suit through a heads-up display and voice control in his helmet, technology that at least we do have today. Could we have fully functional Iron Man suits in the future? It’s probably just a matter of time, technology and computer science (and visionary multi-millionaire industrialists too).


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This blog is funded through EPSRC grant EP/W033615/1.

The Hive at Kew

Art meets bees, science and electronics

by Paul Curzon, Queen Mary University of London

(from the archive)

a boy lying in the middle of the Hive at Kew Gardens.

Combine an understanding of science, with electronics skills and the creativity of an artist and you can get inspiring, memorable and fascinating experiences. That is what the Hive, an art instillation at Kew Gardens in London, does. It is a massive sculpture linked to a subtle sound and light experience, surrounded by a wildflower meadow, but based on the work of scientists studying bees.

The Hive is a giant aluminium structure that represents a bee hive. Once inside you see it is covered with LED lights that flicker on and off apparently randomly. They aren’t random though, they are controlled by a real bee hive elsewhere in the gardens. Each pulse of a light represents bees communicating in that real hive where the artist Wolfgang Buttress placed accelerometers. These are simple sensors like those in phones or a BBC micro:bit that sense movement. The sensitive ones in the bee hive pick up vibrations caused by bees communicating with each other The signals generated are used to control lights in the sculpture.

A new way to communicate

This is where the science comes in. The work was inspired by Martin Bencsik’s team at Nottingham Trent University who in 2011 discovered a new kind of communication between bees using vibrations. Before bees are about to swarm, where a large part of the colony split off to create a new hive, they make a specific kind of vibration, as they prepare to leave. The scientists discovered this using the set up copied by Wolfgang Buttress, using accelerometers in bee hives to help them understand bee behaviour. Monitoring hives like this could help scientists understand the current decline of bees, not least because large numbers of bees die when they swarm to search for a new nest.

Hear the vibrations through your teeth

Good vibrations

The Kew Hive has one last experience to surprise you. You can hear vibrations too. In the base of the Hive you can listen to the soundtrack through your teeth. Cover your ears and place a small coffee stirrer style stick between your teeth, and put the other end of the stick in to a slot. Suddenly you can hear the sounds of the bees and music. Vibrations are passing down the stick, through your teeth and bones of your jawbone to be picked up in a different way by your ears.

A clever use of simple electronics has taught scientists something new and created an amazing work of art.


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A hoverfly on a leaf

EPSRC supports this blog through research grant EP/W033615/1, and through EP/K040251/2 held by Professor Ursula Martin. 

Hoverflies: comin’ to get ya

by Peter W McOwan and Paul Curzon, Queen Mary University of London

(from the archive)

A hoverfly on a blade of grass

By understanding the way hoverflies mate, computer scientists found a way to sneak up on humans, giving a way to make games harder.

When hoverflies get the hots for each other they make some interesting moves. Biologists had noticed that as one hoverfly moves towards a second to try and mate, the approaching fly doesn’t go in a straight line. It makes a strange curved flight. Peter and his student Andrew Anderson thought this was an interesting observation and started to look at why it might be. They came up with a cunning idea. The hoverfly was trying to sneak up on its prospective mate unseen.

The route the approaching fly takes matches the movements of the prospective mate in such a way that, to the mate, the fly in the distance looks like it’s far away and ‘probably’ stationary.

Tracking the motion of a hoverfly and its sightlines

How does it do this? Imagine you are walking across a field with a single tree in it, and a friend is trying to sneak up on you. Your friend starts at the tree and moves in such a way that they are always in direct line of sight between your current position and the tree. As they move towards you they are always silhouetted against the tree. Their motion towards you is mimicking the stationary tree’s apparent motion as you walk past it… and that’s just what the hoverfly does when approaching a mate. It’s a stealth technique called ‘active motion camouflage’.

By building a computer model of the mating flies, the team were able to show that this complex behaviour can actually be done with only a small amount of ‘brain power’. They went on to show that humans are also fooled by active motion camouflage. They did this by creating a computer game where you had to dodge missiles. Some of those missiles used active motion camouflage. The missiles using the fly trick were the most difficult to spot.

It just goes to show: there is such a thing as a useful computer bug.


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EPSRC supports this blog through research grant EP/W033615/1, and through EP/K040251/2 held by Professor Ursula Martin. 

Ant Art

by Paul Curzon, Queen Mary University of London

(from the archive)

The close up head of an ant staring at you
Image by Virvoreanu Laurentiu from Pixabay 

There are many ways Artificial Intelligences might create art. Breeding a colony of virtual ants is one of the most creative.

Photogrowth from the University of Coimbra does exactly that. The basic idea is to take an image and paint an abstract version of it. Normally you would paint with brush strokes. In ant paintings you paint with the trails of hundreds of ants as they crawl over the picture, depositing ink rather than the normal chemical trails ants use to guide other ants to food. The colours in the original image act as food for the ants, which absorb energy from its bright parts. They then use up energy as they move around. They die if they don’t find enough food, but reproduce if they have lots. The results are highly novel swirl-filled pictures.

The program uses vector graphics rather than pixel-based approaches. In pixel graphics, an image is divided into a grid of squares and each allocated a colour. That means when you zoom in to an area, you just see larger squares, not more detail. With vector graphics, the exact position of the line followed is recorded. That line is just mapped on to the particular grid of the display when you view it. The more pixels in the display, the more detailed the trail is drawn. That means you can zoom in to the pictures and just see ever more detail of the ant trails that make them up.

You become a breeder of a species of ant

that produce trails, and so images,

you will find pleasing

Because the virtual ants wander around at random, each time you run the program you will get a different image. However, there are lots of ways to control how ants can move around their world. Exploring the possibilities by hand would only ever uncover a small fraction of the possibilities. Photogrowth therefore uses a genetic algorithm. Rather than set all the options of ant behaviour for each image, you help design a fitness function for the algorithm. You do this by adjusting the importance of different aspects like the thickness of trail left and the extent the ants will try and cover the whole canvas. In effect you become a breeder of a species of ant that produce trails, and so images, you will find pleasing. Once you’ve chosen the fitness function, the program evolves a colony of ants based on it, and they then paint you a picture with their trails.

The result is a painting painted by ants bred purely to create images that please you.


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EPSRC supports this blog through research grant EP/W033615/1, and through EP/K040251/2 held by Professor Ursula Martin. 

Ant Track Algorithms

by Peter W McOwan and Paul Curzon, Queen Mary University of London

(Updated from the archive)

A single ant on a rock
Image by vlada11 from Pixabay

Ants communicate by leaving trails of chemicals that other ants can follow to sources of food they’ve found. Very quickly after a new source of food is found ants from the nest are following the shortest path to get to it, even if the original ant trail was not that direct and wiggled around. How do they do that? And how come computers are copying them?

Bongo playing physicist, Richard Feynman, better known for his Nobel Prize for Physics, wondered about this one day watching ants in his bath. The marvellous thing about science is it can be done anywhere! He grabbed some crayons and started marking the paths each ant followed by drawing a line behind it. He quickly discovered from the trails that what was happening was that each ant was following earlier trails but hurriedly so not sticking to it exactly. Instead it was leaving its own trail. As this was done over and over again the smooth direct route emerged as having the strongest line from the superimposed hurried trails. It’s a bit like when you sketch – you do a series of rough lines to start, but as you do that over and over the final line is much smoother.

From very simple behaviour the ants are able to achieve complex things that might otherwise need complex geometrical skills. As a result, Computer Scientists have been inspired by the ants. Marco Dorigo, Université Libre de Bruxelles first came up with the idea of ‘ant algorithms’: ways of programming separate software agents to do complex things that otherwise would bog down even fast computers. They are part of a more general idea of swarm computing. Finding shortest routes, whether for taxi drivers or for messages sent over networks, is a very common problem of the kind ant algorithms can solve. An ant algorithm solution involves programming lots of software agents to behave a bit like ants leaving digital trails for other agents to pick up. Over time, their simple individual behaviour yields a good solution to the otherwise complex problem of finding the shortest route. Another use is to detect the edges of objects in images – the first step in understanding a picture. Here the virtual ants wander from pixel to pixel based on the differences between nearby pixels, with the result that the strongest trail is left along edges of things shown in the image.

So ants are helping to solve real problems. Not bad for such a tiny brain.


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EPSRC supports this blog through research grant EP/W033615/1, and through EP/K040251/2 held by Professor Ursula Martin.