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Robotic design tip: Build quickly, test plenty

18 May 2015  | Brian Fuller

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For someone who has successfully supervised the development of Dyson's first robotic vacuum cleaner, the learnings from the experience Mike Aldred have gained in the process may come as a sort of revelation to those who are currently or would be involved in the area of robotics design. From inception to actual roll-out, he has this to say: "Keep it simple and test relentlessly."

Aldred, electronics lead for U.K.-based Dyson, keynoted the Embedded Vision Summit last May 12 ostensibly about the Dyson team's use of vision technologies in its Dyson 360 Eye robotic vacuum cleaner. But his hour-long presentation to 500 fellow engineers was more of a graduate seminar in innovation.

"Just because you can doesn't mean you should," he said. "If it works, stop there. Do the simplest things you need to achieve for what you want to do."

The Dyson 360 Eye has been 10 years in the making, longer if you count an earlier failed attempt from which Aldred and his team mined golden nuggets of learning.

He acknowledged that when the 360 Eye project was beginning in 2005, he would have estimated that 60 per cent of the effort would go into conceiving the design, 10 per cent into implementing it and 30 per cent into testing and optimising. In fact, the opposite was true, he said.

"On the 360 Eye, we tested for eight years," he said. "We probably have done 100,000 runs to get to a system. We're still testing even as we're in production."

Aldred, whose doctoral thesis was on visual methods for robot navigation, acknowledges that he works for a man, company founder James Dyson, who understands the value of patience, focus and learning from mistakes.

Aldred emphasised that the company's mission was not to build a robotic vacuum cleaner but a machine that could clean better than humans and free up human time and energy. Robotics is a way to achieve that, and a vision system (as opposed to lasers) is the right enabling technology because it provides a richness and quantity of information to get the job done.

But there were challenges. First and foremost was energy: The 360 Eye runs for about 45 minutes before it needs to scamper back to its charging station. That meant the engineering team needed to figure ways to optimise the machine's navigation and pathways to conserve power where possible. In other words, make it not cover the same ground twice.

During the concept phase, engineers fixed on an imaging system mounted atop the vacuum that could see in panorama and up to an angle of 45 degrees (not much useful information can be gleaned from imaging the ceiling, Aldred noted). They chose this approach because a forward-facing camera on a robotic vacuum cleaner faces a fundamental problem when it runs into something: It's lost its perspective because it can no longer see. With a panoramic capability, the system can recalibrate where it is based on what it sees to the sides and behind.

"The information content is so large, the chances of being blinded are massively reduced," he said.

They next decided on a SLAM system (simultaneous localisation and mapping). To oversimplify, the system looks at edges, straight lines, and corners and estimates those in 3D space and then uses math and Kalman filtering to estimate the robot's position. (Think about a sailor's dead reckoning by stars, only in this project the stars are objects in the room with which the system figures out how to localise itself.)

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