The social animals that are inspiring new behaviours for robot swarms

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Termite team.
7th Son Studio/Shutterstock

From flocks of birds to fish schools in the sea, or towering termite mounds, many social groups in nature exist together to survive and thrive. This cooperative behaviour can be used by engineers as “bio-inspiration” to solve practical human problems, and by computer scientists studying swarm intelligence.

“Swarm robotics” took off in the early 2000s, an early example being the “s-bot” (short for swarm-bot). This is a fully autonomous robot that can perform basic tasks including navigation and the grasping of objects, and which can self-assemble into chains to cross gaps or pull heavy loads. More recently, “TERMES” robots have been developed as a concept in construction, and the “CoCoRo” project has developed an underwater robot swarm that functions like a school of fish that exchanges information to monitor the environment. So far, we’ve only just begun to explore the vast possibilities that animal collectives and their behaviour can offer as inspiration to robot swarm design.

Swarm behaviour in birds – or robots designed to mimic them?
EyeSeeMicrostock/Shutterstock

Robots that can cooperate in large numbers could achieve things that would be difficult or even impossible for a single entity. Following an earthquake, for example, a swarm of search and rescue robots could quickly explore multiple collapsed buildings looking for signs of life. Threatened by a large wildfire, a swarm of drones could help emergency services track and predict the fire’s spread. Or a swarm of floating robots (“Row-bots”) could nibble away at oceanic garbage patches, powered by plastic-eating bacteria.

A future where floating robots powered by plastic-eating bacteria could tackle ocean waste.
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Bio-inspiration in swarm robotics usually starts with social insects – ants, bees and termites – because colony members are highly related, which favours impressive cooperation. Three further characteristics appeal to researchers: robustness, because individuals can be lost without affecting performance; flexibility, because social insect workers are able to respond to changing work needs; and scalability, because a colony’s decentralised organisation is sustainable with 100 workers or 100,000. These characteristics could be especially useful for doing jobs such as environmental monitoring, which requires coverage of huge, varied and sometimes hazardous areas.

Social learning

Beyond social insects, other species and behavioural phenomena in the animal kingdom offer inspiration to engineers. A growing area of biological research is in animal cultures, where animals engage in social learning to pick up behaviours that they are unlikely to innovate alone. For example, whales and dolphins can have distinctive foraging methods that are passed down through the generations. This includes forms of tool use – dolphins have been observed breaking off marine sponges to protect their beaks as they go rooting around for fish, like a person might put a glove over a hand.

Bottlenose dolphin playing with a sponge. Some have learned to use them to help them catch fish.
Yann Hubert/Shutterstock

Forms of social learning and artificial robotic cultures, perhaps using forms of artificial intelligence, could be very powerful in adapting robots to their environment over time. For example, assistive robots for home care could adapt to human behavioural differences in different communities and countries over time.

Robot (or animal) cultures, however, depend on learning abilities that are costly to develop, requiring a larger brain – or, in the case of robots, a more advanced computer. But the value of the “swarm” approach is to deploy robots that are simple, cheap and disposable. Swarm robotics exploits the reality of emergence (“more is different”) to create social complexity from individual simplicity. A more fundamental form of “learning” about the environment is seen in nature – in sensitive developmental processes – which do not require a big brain.

‘Phenotypic plasticity’

Some animals can change behavioural type, or even develop different forms, shapes or internal functions, within the same species, despite having the same initial “programming”. This is known as “phenotypic plasticity” – where the genes of an organism produce different observable results depending on environmental conditions. Such flexibility can be seen in the social insects, but sometimes even more dramatically in other animals.
Most spiders are decidedly solitary, but in about 20 of 45,000 spider species, individuals live in a shared nest and capture food on a shared web. These social spiders benefit from having a mixture of “personality” types in their group, for example bold and shy.

Social spider (Stegodyphus) spin collective webs in Addo Elephant Park, South Africa.
PicturesofThings/Shutterstock

My research identified a flexibility in behaviour where shy spiders would step into a role vacated by absent bold nestmates. This is necessary because the spider colony needs a balance of bold individuals to encourage collective predation, and shyer ones to focus on nest maintenance and parental care. Robots could be programmed with adjustable risk-taking behaviour, sensitive to group composition, with bolder robots entering into hazardous environments while shyer ones know to hold back. This could be very helpful in mapping a disaster area such as Fukushima, including its most dangerous parts, while avoiding too many robots in the swarm being damaged at once.

The ability to adapt

Cane toads were introduced in Australia in the 1930s as a pest control, and have since become an invasive species themselves. In new areas cane toads are seen to be somewhat social. One reason for their growth in numbers is that they are able to adapt to a wide temperature range, a form of physiological plasticity. Swarms of robots with the capability to switch power consumption mode, depending on environmental conditions such as ambient temperature, could be considerably more durable if we want them to function autonomously for the long term. For example, if we want to send robots off to map Mars then they will need to cope with temperatures that can swing from -150°C at the poles to 20°C at the equator.

Cane toads can adapt to temperature changes.
Radek Ziemniewicz/Shutterstock

In addition to behavioural and physiological plasticity, some organisms show morphological (shape) plasticity. For example, some bacteria change their shape in response to stress, becoming elongated and so more resilient to being “eaten” by other organisms. If swarms of robots can combine together in a modular fashion and (re)assemble into more suitable structures this could be very helpful in unpredictable environments. For example, groups of robots could aggregate together for safety when the weather takes a challenging turn.

Whether it’s the “cultures” developed by animal groups that are reliant on learning abilities, or the more fundamental ability to change “personality”, internal function or shape, swarm robotics still has plenty of mileage left when it comes to drawing inspiration from nature. We might even wish to mix and match behaviours from different species, to create robot “hybrids” of our own. Humanity faces challenges ranging from climate change affecting ocean currents, to a growing need for food production, to space exploration – and swarm robotics can play a decisive part given the right bio-inspiration.The Conversation

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This blog was written by Cabot Institute member Dr Edmund Hunt, EPSRC Doctoral Prize Fellow, University of BristolThis article is republished from The Conversation under a Creative Commons license. Read the original article.

Edmund Hunt

Public opinion: What is it really worth?

I recently attended a session at the House of Commons co-hosted by the All-Party Parliamentary Climate Change Group (APPCCG) and the Centre on Innovation and Energy Demand (CIED). The session tackled the topic of the UK’s “energy efficiency revolution”, and whether the UK is living up to the high standards expected by successive governments.
 
Energy efficiency is what is known as a demand-side measure in the language of energy policymakers. Making devices that use energy more efficient is one way of reducing demand for energy overall, and thus bringing the UK closer to its carbon reduction goals. Indeed, increasing energy efficiency is often regarded as one of the most cost-effective methods of carbon reduction.
 
An area of great interest to researchers in this field is human behaviour; how can people be induced to behave in a way that reduces their carbon emissions?
 
The ‘default’ reaction of governments when attempting to change the behaviour of their citizens is to provide financial incentives to encourage adoption of the desired behaviour. This is based on simple economic theory, and depends on the assumption that the average rational citizen will immediately drop undesirable habits as soon as it becomes financially worthwhile to do so.
 
An alternative view is that people are not swayed as heavily by financial motives as they are by their fundamental beliefs; if somebody is a firm believer in the cause of tackling climate change, they can be relied upon to adopt energy-saving behaviours sooner or later.
 
There is a fundamental tension between these two views of how humans behave. Energy policymakers often find themselves caught between these viewpoints, and this can cause delays and poor policy decisions. This is a question that clearly needs to be addressed by researchers.

Let’s take a closer look at this problem by using a simple mathematical model. Imagine that there is a new behaviour, perhaps a form of recycling, that the government is keen for people to adopt. Since it is brand new, almost nobody has heard of it, and even fewer people have actually adopted it.

In order to make this behaviour the norm, the government allocates some of its limited resources to the problem. These resources can either be spent on advertising, to win people over to the behaviour on ideological grounds, or can be spent on direct financial incentives. The government has to choose what proportion of the resources go towards advertising and incentives, based on the objective of full adoption of the behaviour as quickly as possible.

In our model, a certain proportion of the population choose to adopt the new behaviour each day. That proportion is a function of the number of ideological believers (which I will henceforth refer to as ‘public opinion’) and the financial incentive available. Money spent on incentives therefore provides an immediate boost to the adoption of the new behaviour, whereas advertising has an indirect effect. The effect of advertising is to convert a certain number of people each day into ideological believers, making them far more likely to adopt the new behaviour.

 

 

 

 

So what are the results of this simple model? It’s clear that using financial incentives causes the time needed to reach full adoption to become shorter. Therefore, should the government should always use financial incentives in order to reach its stated objectives as quickly as possible?
 
Unfortunately it isn’t that simple. While it is true that the objective of full adoption is met quicker by using mostly financial incentives, the gap between ‘economic’ and ‘ideological’ adopters is large; it’s possible that many of the people who have adopted the behaviour will return to their old ways as soon as the incentives are taken away. It’s also worth considering the possibility that ideological adopters might also be easier to convince when it comes time to introduce the next energy-saving behaviour, whereas economic adopters would need to be paid off from scratch.
 
I should say at this point that this model is meant as a means of communicating a concept, and is an oversimplification of the way technology and belief adoption actually works. I’ve also chosen parameters for the model arbitrarily – choosing a different set of parameters or tweaking the model could result in radically different outcomes.

Nonetheless, the underlying tension remains; should we invest in changing people’s opinions, even if it’s a longer, costlier process? What is public opinion really worth?

It’s my sincere hope that researchers, be it from CIED, Cabot Institute or elsewhere, will be able to answer these questions in the years to come.
 
This blog is written by Cabot Institute member Neeraj Oak, the Chief Analyst and Energy Practice Lead at Shift Thought.

 

Neeraj Oak