UBC Reports | Vol. 50 | No. 4| Apr. 1, 2004

Building a Better Mars Rover

UBC Computer scientist works on giving robots the brains to fend for themselves

By Michelle Cook

When NASA bounced a pair of robots onto Mars earlier this year to explore the red planet and look for evidence of water, Nando de Freitas took more than a passing interest in how the two rovers, dubbed Spirit and Opportunity, would manage their missions.

The UBC computer scientist is part of a team of researchers working with NASA to design the “brains” of the next generation of rovers that will follow Spirit and Opportunity into space. The team’s goal is to develop a more autonomous robot able to fend for itself 170 million kilometres from its creators on Earth.

The two rovers currently exploring Mars have some autonomy but, for the most part, their activities are controlled via basic instructions sent daily from mission control.

“We want rovers to handle the more mundane tasks of monitoring their own “health” and navigating the rough Mars terrain so that scientists back on Earth can focus on the smaller amount of information [the robot is sending back] related to scientific questions about the planet,” says de Freitas.

“We’re really only interested in a certain amount of information from the rovers such as ‘did you see an alien?’” he adds with a laugh.

To do this, de Freitas has been exploring how to give a rover the ability to learn to do things such as recognize when something is wrong with it and then fix itself.

“As humans, we know our bodies. We know how we feel. We also know when something doesn’t feel right -- if our heart is beating too fast, for example,” de Freitas explains.

“A robot by itself should know, without having to communicate with Earth, ‘okay, my wheel isn’t working; I should replace it.’ We’re not at that point yet but that’s where we want to be -- to really get robots aware for their internal state.”

Judging from the work de Freitas has been conducting in UBC’s Lab for Computational Intelligence, it may not be too long before the kind of touchy-feely rover he envisions is brought to life. His research team has already created a robot that can differentiate between various surfaces -- carpet, grass, tile -- that it travels over and diagnose whether its wheel is stuck. The results of the team’s research will be published this month by the Institute of Electrical and Electronic Engineers (IEEE).

While it may all sound a bit Frankenstein-ish, the process doesn’t require knowledge of anatomy so much as a mastery of algorithms - Monte Carlo algorithms to be exact.

Developed by an employee of the Guinness beer company in the 1700s and, more infamously, used to build the atom bomb, de Freitas says Monte Carlo algorithms are particularly suited to the task of programming an autonomous robot to learn. Algorithms are a set of mathematical rules. De Freitas compares them to cookbook recipes. They give a robot a formula with parameters that is also flexible enough to allow for variations and substitutions in information and a margin of error.

With this, de Freitas and other scientists are developing a robot that can recognize and then fix any number of potential problems on a space mission. By loading the robot with data and then simulating as many scenarios as they can beforehand, they “teach” it to become familiar with when it’s functioning properly and when it’s not.

“We have to explain to it, this is what it feels like to have a broken wheel so that it learns all the possible internal states and when you let it go, if anything happens, it knows what’s happening and what to do,” he says.

De Freitas is also trying to improve a rover’s ability to see. Better vision would enable it to better self-navigate and carry out other small tasks on its own. The Spirit and Opportunity rovers are equipped with sensors and a set of nine cameras each. These capture the spectacular panoramic pictures they’ve been sending back to Earth, but the rovers can’t yet process the images they’re seeing and decide where to go by themselves.

The main challenge to overcome is understanding how human vision works, de Freitas says, and to be able to model mathematically everything that goes into visual recognition -- colour, texture, shape -- so a robot can understand what they’re seeing.

“Think of all the things that are the colour blue,” he explains. “How do you distinguish between the blue of the sky and of the ocean? Humans bring context to what they see; robots don’t. There’s a lot of uncertainty and you have to bring in context for them.”

Again, the Monte Carlo algorithms are particularly suited for teaching a robot how to sift through massive amounts of data in order to build a probalistic model to represent the world around them. This enables them to learn how to recognize objects, find patterns in what they’re seeing, match images to words, and label things.

De Freitas says the algorithms allow robots to simulate possible scenarios before they make a decision on what action to take. This mental decision process is constructed so that the number of mistakes is reduced or completely eliminated.

So how smart will the next crew of rovers be?

“They will be more robust robots able to fix themselves and able to operate for much longer times,” de Freitas says. “That’s important when you consider the cost of these missions. They’re extremely expensive and it would be nice if you could just drop rovers off and you knew they would be able to move and do all sorts of things without having to contact us all the time.”