Page 40 - GCN, June/July 2018

EMERGING TECH
The image above is a “feature map” for a legged robot performing a simple mapping task in an environment with uneven terrain. Each pixel represents a combination of six parameters related to the shape of the robot, such as leg length, body mass and number of joints per leg. Lighter colors represent fitter combinations. More of this map is filled in as evolution proceeds and new combinations are discovered. This kind of map helps designers locate good body designs.
for example, mounting a door onto a chassis — with relative ease because the environment it works in is tightly controlled. Intelligence is not required here, and it is easy for us to design robots for these types of tasks.
But robots struggle when we take them outside. Australia is a prime location for deploying robots outdoors, whether performing long- term biodiversity studies in rainforests or providing vital information to first responders following a natural disaster. Robots would be incredibly helpful in these “unstructured” environments, which are unpredictable and uncontrollable.
To build robots that are ready to handle the challenges of unstructured environments, we need a design process that focuses on adaptation and intelligence. Luckily, we know of an incredibly powerful algorithm that creates intelligent, robust and adaptive machines already: evolution.
The vast richness and complexity of flora and fauna observed on our planet is the result of genetics and Darwinian evolution. Creatures thrive in the most challenging and unexpected environments, adapting themselves over numerous generations to become masters of the niche in which they reside.
Let’s consider a high-level description of evolution: Genes define creatures, and creatures exist in an environment. Creatures automatically adapt to their environmental niches because genes that allow them to survive and procreate in that environment are passed on to the next generation.
Artificial evolution
Instead of a population of birds or plants, what if we used Darwin’s principles to evolve a population of robots that automatically improve their performance in an environment?
We can do this by either creating
a computer simulation of the robots, which saves time and money, or creating them in real life. Either way, we need the following components:
• Genome. A genome defines what the robot looks like and how it behaves. It is made up of a list of numbers, or genes. A leg might be defined by three genes — for example, the numbers 250, 3 and 2, which give us a leg 250mm in length with three joints and two toes. Further numbers might tell us how many legs the robot has, where they are placed in relation to the body, what sensors the robot has and how they are wired up, and how the robot translates its sensor readings into movements to produce a behavior.
• A population of robots. We make our robots according to the instructions in their genomes. Each robot initially has random numbers in its genome.
•A task. Let’s say we want our robots to count wombats in a certain region. We define an equation called a fitness
40 GCN JUNE/JULY 2018 • GCN.COM