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It has been suggested that an advanced group of robots will be needed if humans are ever to settle on other planets. Sent in advance to create conditions favorable to humanity, these robots will need to be resilient, adaptable and recyclable if they are to survive in the inhospitable cosmic climates that await them.
In collaboration with roboticists and computer scientists, my team and I worked on such a set of robots. Produced via a 3D printer – and assembled independently – the robots we create are continually evolving in order to quickly optimize themselves for the conditions in which they find themselves.
Our work represents the latest advancements towards the type of autonomous robot ecosystems that could help build humanity’s future homes, far from Earth and far from human oversight.
The rise of robots
Robots have come a long way since our first clumsy forays into man-made movement decades ago. Today, companies like Boston Dynamics are producing ultra-efficient robots that load trucks, build pallets, and move boxes around factories, taking on tasks you might think only humans could do.
Despite these advancements, designing robots to work in unfamiliar or inhospitable environments – like exoplanets or deep ocean trenches – still poses a considerable challenge to scientists and engineers. In the cosmos, what shape and size should the ideal robot be? Should he crawl or walk? What tools will it need to manipulate its environment – and how will it survive extremes of pressure, temperature and chemical corrosion?
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Impossible puzzle for humans, nature has already solved this problem. Darwinian evolution has resulted in millions of species perfectly adapted to their environment. Although biological evolution takes millions of years, artificial evolution – the modeling of evolutionary processes inside a computer – can take place in hours or even minutes. Computer scientists have been harnessing its power for decades, resulting in gas nozzles for satellite antennas perfectly suited to their function, for example.
But the current artificial evolution of moving physical objects still requires a lot of human oversight, requiring a tight feedback loop between robot and human. If artificial evolution is to design a useful robot for exoplanetary exploration, we will have to take the human out of the loop. Essentially, advanced robot designs must self-build, assemble, and test themselves – without any human oversight.
Unnatural selection
Any evolved robot will need to be able to sense its surroundings and have a variety of means to move around – for example using wheels, articulated legs, or even a mix of the two. And to bridge the inevitable reality gap that occurs when moving a design from software to hardware, it is also desirable that at least some evolution takes place in hardware – within an ecosystem of evolving robots. in real time and in real space.
The Autonomous Robot Evolution (ARE) project responds exactly to that, bringing together scientists and engineers from four universities in an ambitious four-year project to develop this radical new technology.
As illustrated above, robots will “be born” through the use of 3D manufacturing. We use a new kind of hybrid hardware-software scalable architecture for the design. This means that every physical robot has a digital clone. The performance of physical robots is tested in real environments, while their digital clones enter a software program, where they undergo rapid simulated evolution. This hybrid system introduces a new kind of evolution: new generations can be produced from the union of the most successful traits of a virtual “mother” and a physical “father”.
In addition to being rendered in our simulator, the “child” robots produced through our hybrid evolution are also 3D printed and brought into a real environment, similar to a nursery. The most successful individuals at this physical training center make their “genetic code” available for reproduction and for the improvement of future generations, while the less “fit” robots can simply be hoisted and retrained into new ones. as part of an ongoing evolutionary cycle.
Two years after the start of the project, significant progress has been made. From a scientific point of view, we have designed new artificial evolutionary algorithms that have produced a diverse set of robots that drive or crawl and can learn to navigate complex mazes. These algorithms change both the body plane and the robot’s brain.
The brain contains a controller that determines how the robot moves, interpreting sensory information from the environment and translating it into motor commands. Once the robot is built, a learning algorithm quickly refines the child’s brain to account for any potential mismatch between their new body and their inherited brain.
From an engineering perspective, we designed the “RoboFab” to fully automate manufacturing. This robotic arm attaches wires, sensors and other “organs” chosen by evolution to the robot’s 3D printed chassis. We designed these components to facilitate quick assembly, giving RoboFab access to a large toolbox of robot limbs and organs.
Waste treatment
The first major use case we plan to address is the deployment of this technology to design robots to undertake the cleanup of legacy waste in a nuclear reactor – as seen in the Chernobyl TV miniseries. Using humans for this task is both dangerous and expensive, and the necessary robotic solutions remain to be developed.
Going forward, the long-term vision is to develop the technology enough to enable the evolution of entire autonomous robotic ecosystems that live and work for long periods in harsh and dynamic environments without the need for direct human oversight. .
In this radical new paradigm, robots are designed and born, rather than designed and manufactured. Such robots will fundamentally change the concept of machines, presenting a new breed that can change shape and behavior over time – just like us.
This article by Emma Hart, Chair of Natural Computing, Edinburgh Napier University is republished from The Conversation under a Creative Commons license. Read the original article.
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